Process Optimization

Since it's Mardi Gras in New Orleans today, I'll do a rare, same-day post. This year is an extra big year with the New Orleans Saints football team bringing the Super Bowl trophy home to the Crescent City. As a former resident back in the 1980s, I'm obliged to bring a bit of the sounds of Mardi Gras to my office by streaming MardiGrasMusicRadio.com.

The post can't wait because I'd lose the first paragraph if I wait another day and also ModelingAndControl.com's Greg McMillan will be presenting in two short weeks at the ISA New Orleans chapter. Greg is calling his two-day series of presentations, March 3 and 4, Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements. He described what he plans to share in a post, Exceptional Opportunities in Process Control - Virtual Plants.

There are only 30-available slots so you'll want to visit their event page for costs ($400 ISA members/$500 non-members), how to enroll, location, etc.

I'll highlight the sections Greg will be presenting over the two days. The sessions begin at 8am on March 3^rd with improving process dynamics by considering process responses, sample times, and ultimate loop performance limits. Following will be a session on improving controller tuning by delving into controller modes, options, structure, tuning, and loop performance practical limits.

Greg continues around the control loop to next look at control valves and their considerations including slewing rate, backlash, stick-slip, installed characteristics, and rangeability. He next will move on the measurement devices and their considerations such as accuracy, noise, response, turndown, and measurement types--flow, pressure, level, temperature, and pH.

Next comes resting your brains and enjoying an evening in New Orleans. The sessions resume at 8am on March 4^th with a look at improving common loops--flow, pressure, level, temperature, pH, and composition.

Greg wraps up the two days showing ways to improve neutralizer, reactor, and evaporator control. Greg notes that each attendee will have access to a virtual plant for hands-on learning and interactive exploration of process control improvements and a copy of his ISA book Essentials of Modern Measurements and Final Elements - a Guide to Design, Configuration, Installation, and Maintenance.

If you're in New Orleans--or need a good excuse to be there--here's your opportunity. And knowing Greg as I do, you'll learn quite a bit.

As we get further away from our college years, sometimes misconceptions can solidify. For instance, most engineers that had a basic control theory class may recall that increasing the gain in a feedback loop at some point will introduce instability/oscillations. The misconception is that this is not universally true for all loops, such as level loops.

ModelingAndControl.com's Greg McMillan has a great article co-developed with researchers at India's MIT Anna University on ControlGlobal.com, Adaptive Level Control: Exploring the Complexities of Tuning Level Controllers and How an Adaptive Controller Can Be Used in Level Applications. Greg, Sridhar Dasani and Dr. Prakash Jagadeesan clear up the gain misconception:

...the opposite correction is more likely needed for integrating processes. Most level loops are tuned with a gain below a lower gain limit. We are familiar with the upper gain limit that causes relatively fast oscillations growing in amplitude. We are not so cognizant of the oscillations with a slow period and slow decay caused by too low of a controller gain. The period and decay gets slower as the controller gain is decreased. In other words, if the user sees these oscillations and thinks they are due to too high a controller gain, he or she may decrease the controller gain, making the oscillations worse (more persistent).

The authors describe some challenging level control applications such as continuous reactors, crystallizers, and material balances in unit operations that require extremely tight level control. For process vessels such as horizontal tanks, drums, and spheres, the level change for a given flow rate is not linear because of the geometry of the vessel. Changes in the fluid density and use of non-linear valves can also increase the challenge to perform tight level control.

The authors note that adaptive level controls, built on adaptive control software such as DeltaV InSight, can

...not only account for the effect of vessel geometry, but also deal with the changes in process gain from changes in fluid density and nonlinear valves. Even if these nonlinearities are not significant, the adaptive level control with proper tuning rules removes the confusion of the allowable gain window, and prevents the situation of level loops being tuned with not enough gain and too much reset action.

The article highlights process dynamics related to conical tanks. These tanks have extreme changes in cross sectional area as the level changes. The MIT Anna University research lab used the embedded DeltaV InSight software to automatically identify the process dynamics around changes to the level setpoint within the conical tank. The authors describe how this is done:

The adaptive controller employs an optimal search method with re-centering that finds the process dead time, process time constant, and process gain that best fits the observed response. The trigger for process identification can be a setpoint change or periodic perturbation automatically introduced into the controller output or any manual change in the controller output made by the operator.

The article is complete with equations for integrating process gains, conical tank dynamics, and controller tuning rules. These may help awaken those brain cells if you're like me and have let these cells remain dormant over the past several years.

The authors summarize their findings:

Adaptive level controllers can eliminate tuning problems from the extreme changes in level control dynamics associated with different equipment designs and operating conditions. The integrated tuning rules prevent the user from getting into the confusing situations of upper and lower gain limits and the associated fast and slow oscillations. The smoother and more consistent response allows the user to optimize the speed of the level loop from fast manipulation of column reflux and reactor or crystallizer feed to slow manipulation of surge tank discharge flow control.

MP3 | iTunes

Utilities Middle East magazine had a recent interview with Emerson's Jeff Householder. Jeff is based in Dubai and leads the Systems and Solutions efforts for the Middle East and Africa (MEA) region.

This Q&A article explores Jeff views on the outlook for the power and water utilities industry in the MEA region. Unlike other process manufacturing industries and world areas, the MEA power and water industry enjoyed growth in 2009 and expects this growth to continue into 2010. On the prospects for 2010, Jeff responds:

There is substantial investment across the region. Saudi Arabia has invested heavily in modernisation of their power plants. We see Kuwait entering a similar phase for power and water. Egypt has consistently invested over the last several years and we see this continuing. The UAE is investing in new plants and modernization, driven by their growth in population and industry.

Like many industries, there is focus on optimizing plant operations and finding ways to avoid unplanned shutdowns. Technologies like high-speed digital communications (Foundation fieldbus, HART 7) and wireless process control instrumentation play an important role, but also important is to:

...work collaboratively with our customers to help develop plant management philosophies based on the increased plant intelligence to assist in creating a roadmap for the plant.

Jeff notes that the process control devices and systems often have the embedded functionality to support optimization and efficiency-related projects, but the "...roadmap drives increased plant performance." In other words, the technology investment is often already in place, but the roadmap plan helps drive the focused efforts required to realize the value through optimization and efficiency.

Energy efficiency projects have grown in number due to utility competition and recent rises in fuel prices. Jeff notes:

Performance efficiencies are mainly sought within a plant's main process areas such as boilers, turbines, condensers, and large pump/motor skids. For plants with multiple units, a more technically advanced plant-wide program is available which prioritizes efficiencies across multiple units. Such optimisation is achieved by developing working models of each unit's specific operational characteristics and the facilities overall economic drivers.

On a question about the acceptance of wireless field instrumentation among utilities, Jeff shares:

We have seen a significant amount of interest in wireless in utilities. Considering that wireless adapters (THUM) can be retrofitted to devices already installed in the field, networked and transmitted to the central asset management system, there are many opportunities for non-invasive application of the technology. Certain areas in the facility that are challenging from the health and safety aspects are normally targeted for use of wireless technology.

Jeff offers sound guidance on collaboration with automation suppliers on developing a plant roadmap to use the current automation technologies to improve efficiency and reduce unplanned shutdowns--no matter what your industry or world location happens to be.

MP3 | iTunes

Last week I was on the phone with Emerson's Bob Sabin, a consulting engineer on the Industrial Energy Solutions team. You may recall Bob from some earlier energy efficiency-related posts. As I'm prone to do this time year after our annual Emerson Exchange meeting, I asked Bob if he did an Emerson Exchange presentation. He did in fact present, A Structured Optimization Plan for Leveraging Control Technology to Reduce Energy Costs and Improve Overall Plant/Mill Profitability.

Bob discussed the increasing focus on energy due to its cost and increasing emissions regulatory climate across the globe. It's a case where greater energy efficiency is both the "green" thing to do by reducing emissions and it lowers operational costs by reducing one of the largest controllable costs. Energy usage improvement is an aspect of overall production optimization and savings go directly to the bottom line.

Bob cited an ARC Advisory Group study, Best Practices in Energy Management, which categorizes leaders, competitors, and followers in the reduction of energy usage. Half of the leaders reduced energy consumption by 10-15% each year, while over half the followers made no progress or did not know if they had made any progress.

He outlined a typical site energy-flow perspective, beginning with the sources of energy: purchased steam, purchased fuel, raw materials consumed as fuel, and purchased power. The fuel and raw material fuel are converted to steam and electrical power and consumed by the process in steam and electric drives, process heating and cooling, fired equipment such as fired heaters and dehydration units, and direct-fueled equipment and processes. The site may also export steam, fuel and power. Bob and the consulting team work with process manufacturers to assess these areas for ways to minimize (energy inputs), improve efficiency, optimize, and maximize (energy outputs).

Bob described the energy improvement process that begins with survey and measurement, followed by actions to fix field devices and loops, followed by equipment repair, followed by unit process optimization, followed by site coordination to drive the entire operation to the best cost point within constraints. Although the process is never ending, the savings are cumulative with each pass through the improvement cycle.

In the survey and measurement phase where measurements don't currently exist, Bob recommends considering wireless devices to monitor steam flows, condensate returns, water and warm water usage, air flows, and air pressures. Wireless measurements can be implemented at a fraction of the cost of traditional wired devices. The survey and measurement phase is where benchmarks are established to monitor performance over time and compare current operations with known industry standards to establish the economic case to justify investment.

Many plants have opportunities to fix leaks, maintain steam traps and improve insulation on their steam, air, and water systems. Other areas to fix the basics include measurement device calibration and final control element inspection for linearity and repeatability. These loops are often in manual when the devices are not performing correctly. Variable frequency drives for fans, pumps, and other cyclical load devices can be more efficient than processes with recirculation loops and throttled flow.

Once these basics are addressed in a bottom up approach and the process is returned to automatic control, units can be optimized. The highest benefit is typically only sustainable if a holistic approach is taken starting with the basics. Bob recommends a "single knob" strategy where a single operator input establishes the process rate. It incorporates equipment and process constraints, coordinated rate/load changes, and bumpless, balanceless manual/auto transfer. The regulatory control can then be enhanced with advanced process control that incorporates process specific techniques and expertise. To gain the desired improvements in energy efficiency, the design targets the process controls to be in automatic mode more than 95% of the time.

Bob gives examples of simple utility operations with and without multiple fuel sources to more complex operations. No matter the complexity, the road to lower emissions and lower energy usage begins by measuring it, fixing it from the bottom up, getting on automatic control, incorporating process expertise into the control strategies, and layering models for area/site optimization. It's also the way to move profitably from follower to leader.

MP3 | iTunes

Here's another great presentation from the recent Emerson Exchange from refining & chemicals industry solutions director, Pete Sharpe. You may recall Pete from earlier process optimization-related posts.

His presentation, SmartProcess Distillation Application Improves Recovery and Saves Energy--A Case Study, describes a project to improve operations on a high-purity distillation process at a Goodyear facility. These distillation columns, 11 in total, were part of a purification unit with multiple trains. The ultra-high purity product specifications required very tight quality controls.

From a control strategy standpoint, the process had multiple large, 200+ tray columns with extremely long time constants. Also, different feedstock suppliers provided feed with different qualities. The operators had to have large safety margins to compensate for disturbances caused by the feed variability. These conservative margins reduced the overall recovery rate and increased the energy required per unit output. Overall, the purification unit was a large energy consumer within the plant.

Pete and the APC consultants worked with the engineers to scope a project that included a functional specification that included the design for all 11 columns in the purification unit. The initial implementation phase covered only the first column in the series to:

• Have the Goodyear team gain experience with the technology
• Develop acceptance by the plant operators
• Demonstrate the value of the distillation optimization to the management staff.

The plant engineering staff with support from the Emerson APC consultants would implement subsequent columns.

During the functional design specification phase that included an on-site audit of the installed instrumentation and control strategies, the team identified regulatory control issues including sticking control valves. As I've mentioned in an earlier post, process variability can often be traced back to valve performance problems and these should be addressed first.

After these issues had been addressed, the team installed a SmartProcess Distillation Optimizer that embeds DeltaV model predictive control and neural network function blocks on the first column. The control strategies were based on a "what comes in must go out" approach that included material (overhead to feed ratio) and energy (reflux to feed ratio) balances. The objective of the optimization was to minimize overhead product loss while controlling bottoms impurities to target.

The manipulated variables (MV) for the MPC controller included the distillate rate and reboiler steam. The control variables (CV) included the lower tray pressure-compensated temperature (PCT) and the overhead PCT. The disturbance variables (DV) included the feed flows and reflux temperature. The column constraint variables included measurements from on-line analyzers for overhead product loss and light impurities in the bottoms as well as internal flux rate, column delta pressure and the reflux/distillate ratio.

Pete and the team had to work through reboiler steam temperature instability caused by a process design issue. Also, the overhead pressure controller range was insufficient to handle the difference in temperature between day and night. Two valves controlled the pressure in a condenser system, one large and one small. The pressure swings caused changes in the temperature and quality of the column output. The team developed a control strategy to adjust the large valve to keep the small valve within the correct control range.

A two-week period on-site was required to address these issues, commission the optimizer, and train the operating and engineering staff. The results from this project were impressive. The optimizer immediately started reducing distillate rate and overhead product losses. The average overhead product loss was reduced 22% while the impurities in the bottoms were maintained within specification. Steam usage dropped 7%.

For this one column, this optimization project delivered an estimated annual value of $700,000 (USD) through increased recovery rates and decreased energy usage.

MP3 | iTunes

Update: I've uploaded a copy of Pete's presentation to Slideshare and embedded it within this post. It's also available for download from Slideshare.

For those attending the Emerson Exchange with distillation processes, you don't want to miss Emerson's Lou Heavner's presentation, Advanced Distillation - 102. Lou is an advanced control consultant, whom you may recall from earlier posts. He'll be presenting Wed, 9/30 at 4:15pm in the Sun 4 room and again Thurs, 10/1 at 9:00am in the Captiva 1 room.

Lou's preference is to have each session be an open, interactive discussion on distillation control challenges and solution approaches. He does have a presentation and will talk if that is what the session attendees would prefer, but he's hopeful an interactive discussion will blossom. I've gleaned some highlights from the presentation.

This is a picture of a traditional binary distillation column. The feed may be vapor, liquid, or a mixture, but is most commonly a liquid. Its composition is usually variable. Liquid in the bottom of the column is boiled up through the column and vapor leaving the top is condensed and returned as reflux. The product flows must equal the feed flow or the process won't operate very long. So, the liquid level in the bottom of the column and the liquid level in the reflux accumulator are controlled by manipulating product flows or sometimes by manipulating heat to the reboiler or reflux to the column.

Usually, composition is inferred key from temperatures in the column and controlled by manipulating heat to the reboiler and reflux flow to the column. Pressure can be controlled by venting non-condensibles (if they are present) or by controlling the amount of condensing in the column overhead.

Feed is usually not available for control, but may be in some cases. When it is available, it can be a good choice for optimization--maximizing throughput. In some cases, online analyzers are available and if they are, they may be used for control or simply monitored.

Lou stresses the fact that you can't control something that you can't measure. Online analyzers or product purity measurements are one of the key requirements for good distillation control. If there is not an appropriate online analyzer, then some kind of inferential measurement will be required. Other measurements such as flooding (a column operating constraint) and reflux ratio can be used to track performance.

Interaction is one of the defining challenges of distillation control. Interestingly, there are many ways to pair controlled and manipulated variables. Some will work well in one column and poorly in another. The whole study of relative-gain array (RGA) analysis has been developed to understand the best way to pair control and manipulated variables. This is largely dependent on factors like product purity specs, feed composition, number of theoretical stages in the column and typical reflux ratio.

Another source of interaction outside of many columns is thermal integration. It is common for the hot product to be cooled against the feed to provide some preheat and efficiency to the column. Variability in the bottom product temperature or flow rate will be recycled back into the column through the feed. Sometimes a heat pump arrangement is used to boil the bottoms against the overhead vapors, which have been compressed. This configuration is rarely seen and only practical when the overhead and bottom products have similar boiling points and non-condensables are not present.

Another factor that makes distillation control difficult is the actual process dynamics such as long time delays associated with the time it takes for liquid reflux to cascade all of the way down to the bottom of the tower. This is more problematic when one considers that vapors will rise much more quickly up the tower. The controls need to respond to both of these kinds of dynamic responses.

When looking at the distillation process, it's a classic multi-variable process with controlled and manipulated variables. You can include the material balance loops (i.e. the level control loops) in the model predictive control (MPC) strategy. The levels would be the control (or constraint) variables and the product flows would be the manipulated variables. There are extra, manipulated variables, so an opportunity exists to include optimization. There may also be additional constraints (e.g. flooding or valve positions) and measured disturbances (e.g. reflux temperature and feed temperature).

Lou goes on to describe interaction and the relative gain array analysis process to identify the best pairings to minimize the effect of loop interaction and simplifying the matrices. He also covers pressure compensated temperature pros and cons, multi-component distillation, level control, azeotropes, and batch distillation.

If you've been battling distillation issues in meeting quality specs, energy usage, yield, and/or capacity, bring these to one of the two sessions and see what thoughts Lou and fellow attendees have to offer. It will hopefully be worth your while!

Update: Here's Lou in action:

If you're in the coal-fired power generation business, you may know that Coal-Gen 2009 is going on this week. During the conference, a Midwest power producer and the Emerson Power & Water Solutions team gave a joint presentation on combustion optimization.

Emerson's Jeff Williams, one of the presenters, was kind enough to send me a copy so I could relay a few highlights in this post. The presenters discussed how they were able to optimize the combustion process to reduce NO_X levels beyond the guarantee level.

Coal-fired power plants are impacted by many dynamic factors including source fuel type & quality, market deregulation, tightened emission standards to name a few. Costs for NO_X and SO₂ credits have increased over the last few years.

There are many pre- and post-combustion technologies available to reduce NO_X and SO₂ emissions, each with its own cost-benefit ratio--investment cost of the technology vs. the %NO_X reduction.

For the project described in the presentation, the team benchmarked pre-project NO_X, O₂, and steam temperature levels and burner tilt performance. Two improvements were identified, the addition of separated OverFire air (SOFA) dampers & tilts and combustion optimization in the plant's Ovation control system.

The OverFire air process redistributes air within the boiler combustion zone and injects additional air above the combustion zone to complete the combustion process. Decreasing the air within the burner zone lowers stoichiometry, which lowers the flame temperature and reduces thermal NO_X. This also reduces the tendency of fuel-bound nitrogen to oxidize to nitrous oxides.

To compensate for temperature excursions caused by rapid changes in SOFA positions, advanced control strategies were developed. These control strategies were based on an advanced non-linear, fuzzy-neural NARMAX (FNM) algorithm.

The team followed a multi-step process, which included a study of the current combustion process, DCS control improvements, parametric testing, model development, open-loop testing, closed-loop testing, and commissioning.

For this project's optimization model NO_X and CO were the control variables. Manipulated variables included the OFA and SOFA dampers, SOFA tilts, O₂ trim, auxiliary air dampers, window-to-furnace differential pressure, fuel air dampers, and feeders. The disturbance variables included load, ambient temperature, total air flow, and burner tilts demand.

Over the multi-year process that included the combustion optimization, followed by the SOFA equipment, followed by the advanced control optimization of the SOFA equipment, the plant reduced annual NO_X output from over 1400 tons to under 600 tons.

MP3 | iTunes

I managed to get my hands on a great paper, Olefin Plant Energy Savings through Enhanced Automation, written by Emerson's Dr. Douglas C. White, whom you may recall from earlier posts. Doug is a principal consultant who leads the Process Improvement and Optimization Consulting team.

He presented this paper at the AIChE Spring National Meeting as part of the Ethylene Plant Technology - Energy Consumption and Optimization track. The abstract:

Energy is the single largest controllable cost for olefin plants and the recent rise in prices has caused most plants to look even more closely at their usage. Automation and advanced automation can significantly reduce usage across all areas of the plant. Some of these savings can be achieved with no investment, only changes in normal operating procedures. In other cases improvements to on-line analyses, measurements and control action are justified but generally require relatively modest investments. The management of the utilities at a major olefin site can be difficult with many daily operating decisions that must balance competing economic and production issues. Real time modeling of process and utility equipment and monitoring of the energy usage in plants permits allocation decisions to be made much more frequently and accurately, often resulting in substantial savings.

Doug describes the economics that Olefin producers face:

Olefin plants are large energy consumers with energy the largest variable operating cost after feedstocks. Using energy efficiently has been and remains a primary goal for olefin producers.

Natural gas is the marginal fuel consumed and its price has been a source of volatility over the past several years. Doug describes surveys where there is at least a 40% spread in energy usage between the most and least efficient plants. The source of this variation is due to the age and efficiency of the equipment and the heat integration.

Doug provides an energy investment opportunity matrix of high, medium, and low potential energy savings versus capital cost/time to implement. An example of a potentially high energy saving opportunity, but coming at a high capital cost is and integrated turbine. At the other end (low savings / low investment) are things like increased insulation and heat exchanger maintenance.

He describes two ways to reduce energy costs--either by reducing supply costs or reducing process energy demand. On the supply-cost side, the focus is to increase internal utility production efficiency and reduce external purchase costs. Advanced control and optimization on the furnaces, quench/fractionators, compressors, and distillation columns are a few examples cited on the process energy demand reduction side.

The paper describes areas to find energy savings. These include: control loop performance improvements, more accurate measurement of process variables, measurement additions via WirelessHART technology, valve performance improvements to handle the various olefin plant load conditions, loop dynamic analysis and tuning, and steam system management and control. The paper provides further thoughts in each of these areas.

Doug recommends developing an automation energy savings program and beginning with a full assessment of current operating conditions. This not only helps with the justification, but also provides the benchmark to compare improvements against to provide return on investment. He counsels that a part of this assessment is to identify the control and advanced control loops that have a major impact on energy usage. He has another matrix of energy loss consequences versus historical frequency for monitoring and maintenance. This analysis helps prioritize financial impact and focus the justification efforts.

Whether or not you're an Olefins producer, you'll gain some insight in how to find and plan a path to energy savings.

MP3 | iTunes

I caught up with Emerson's Lou Heavner the other day and we traded a few teenager "war stories." Lou is an advanced automation consultant and I've shared some of his expertise in posts over the years. He mentioned he had done a very basic presentation to show the interaction of operations, control, advanced control, and control strategies using the DeltaV system as his example.

I asked if I could post his presentation in my SlideShare account and discuss it here in this post. He was kind enough to agree.

Lou starts by describing key areas of the operator graphics describing the navigation, toolbars, alarm banner, and buttons to the model predictive controller (MPC) display that he typically will add to the advanced control project. He shows the loop faceplate which comes up when the operator clicks on an alarm. He notes that the operator:

...can change mode, SP, etc. He has one click access to loop tuning, alarm acknowledgement, trending, and with the appropriate privilege, he can access the engineering environment.

He shows the operator faceplate for a PredictPro MPC controller where the operator can view the optimizer, change modes and setpoints, and view the trend prediction horizon. For those who may be unfamiliar with an MPC controller, Lou shows the optimizer, which shows the variables being maximized or minimized and their associated economic value.

Lou next switches to the engineering environment where the modules and their associated parameters are located. Advanced control functions like loop tuning, neural networks, and MPC are available along with the regulatory control options. Lou shows the engineering side of creating MPC controllers from initiating automation step tests to creating and downloading the MPC controllers into the Delta controllers.

He shows an example of a composite block to calculate heater efficiency using the heat loss method. The calculation nests these composite blocks and Lou shows how to drill down and back out. He closes the brief presentation showing an on-line view which aids in troubleshooting the control strategy. This on-line view is available when the strategy is in simulate mode or actually running on-line in the controllers.

I hope Lou's simple descriptions and screen captures helps show the interaction of the advanced controls from their design through to their operation. We'd also like your thoughts on if you'd find this valuable to be seen in another form like a screencast.

I've featured quite a number of experts around Emerson over these past three years. Some categories, like Process Optimization, have more than 50 posts. There is quite a bit of wisdom mixed in all those posts.

A few months ago, I was asked by Plant Engineering magazine managing editor, Jack Smith, if I'd be willing to write a process optimization article highlighting some of the ideas offered in these posts. It was a thrill to be asked and I quickly agreed.

I wrote a draft and went back to some of the experts highlighted, such as Mark Coughran and Pete Sharpe, for their improvement suggestions. The resulting article, Downturn a good time to review, improve process optimization went live on PlantEngineering.com website last Friday and is printed in the March 2009 magazine edition.

I tried to stress things you could do as a plant engineer to improve your process without having to spend a lot of capital, which is an issue for many process manufacturers in this global economic slowdown. Finding ways to reduce process variability is a good first step. Sources of variability that our variability consultants have tabulated over the years include:

• Control valve performance - 30%
• Improper tuning - 30%
• Improper process and/or control scheme design - 20%
• Other - 20%. The 20% of other causes are not necessarily design- or control-scheme related, but more operational issues that occur over time.

I distilled down five ways to reduce this variability: size control valves properly, minimize loop dead time, measure process dynamics and compensate for them, tune the loops, and apply advanced process control. I won't spill all the secrets divulged in the article but instead highlight a couple of points.

My Fisher valve colleagues often remind me of the importance of the control valve since it directly touches the process.

Control valves, being variable in gain, must be correctly sized and characterized for the application's flow to be sufficiently linear to stay within specified gain limits over the operating range of the process.

Other parts of your control loops to check for:

...sources of dead time include inadequate signal conditioning on transmitters, incorrect transmitter range/resolution, poor physical location of transmitters and measurement lags from applied filters and dampeners.

Without the proper process dynamic measurement applications, many plant engineers have had to rely on rules of thumb and guesswork to loop tuning parameters. With the process dynamics understood, you can tune the loops with linear responses and try to reduce the non-linearities in the others in several ways including:

...changing any master loop configuration to prevent interaction with the slave loops. See if you can remove unnecessary interlocks that may disturb the control loop. If you uncover extremely high process gains, adding upstream control loops can help. Other advanced regulatory control strategies such as feedforward, cascade, override and split-range control can compensate for different process conditions.

With the basics addressed, you can look for areas to apply advanced process control, especially in big, energy-consuming units.

I hope some of the ideas excerpted from the article help you find ways to improve your process and help your business through these challenging times.

MP3 | iTunes

Update:I heard from Jack that this article is currently the most popular one on the PlantEngineering.com website. Thanks for stopping by to read it!

I recently exchanged some emails with Emerson's Sergei Kuznetsov, part of TAG projects organization, and based in Minneapolis, Minnesota. Sergei is principal control systems engineer, certified professional engineer, and has an MSEE degree.

He shared with me an article, Staying In Control that he had written for Engineered Wood Journal magazine. The article describes ways to improve flake blending and mat forming in older oriented strand board (OSB) mills. For those unfamiliar with OSB, Wikipedia defines it:

Oriented strand board, or OSB, or waferboard, or Sterling board (UK) or SmartPly (UK & Ireland) is an engineered wood product formed by layering strands (flakes) of wood in specific orientations.

The issue with many older OSB mills built in the 1970s and 1980s is that they have large transport delays in the conveyors, which connect process equipment spread across the mill. Sergei notes that the problem most adversely impacts the blender inflow control and mat forming bin level control. These areas have large impact on the quality and consistency of the final product.

Such a problem of course is not limited to OSB production lines. Any process that involves a particulate material via conveyers can potentially have its deadtime affecting efficient control of related process variables.

From a control strategy perspective, Sergei described the challenge and solution:

A conventional PID (proportional-integral-derivative) feedback controller will not work well in applications with long process deadtimes. Good control can be accomplished, even in older mills, by employing the Smith Predictor control algorithm to address processes with significant transport delays or deadtimes.

In some extreme cases, this deadtime can be five minutes from the dry wood bin to the blender and then to the forming bin. If this deadtime is ignored in the tuning of the forming bin level controller and wood flow controller, process changes will prompt overcorrections and likely oscillatory conditions, unless the controllers are substantially detuned. Detuning causes sluggish response to changes and impacts the quality and consistency of the strand board.

Sergei detailed how the Smith Predictor algorithm addresses this deadtime:

The Smith Predictor uses a process model to calculate predicted process change in response to a control action as if there is no deadtime. This change is added to the PID process variable so the controller is made to "believe" that the corrective action actually took effect immediately, and thus will not take additional action. With such a modification, the PID controller can be aggressively tuned so it can provide good control of its process variable.

For the blending wood flow control, the flow can deviate due to the woodpile shape or differences in the bins that feed the conveyor. With a Smith Predictor accounting for transport deadtime, the loops can be aggressively tuned to handle the natural deviations in flow and bin switching. By closely controlling the wood flow, the proper ratios of wood to wax/resin can be maintained in the blender.

For the forming bin, controlling this level in older mills is notoriously difficult and typically requires a high level of operator intervention. Deadtime from long conveyors and blender retention time is a large part of this control challenge. A high forming bin level can cause unplanned shutdowns and bin level deviations can impact quality and consistency. A PID-based level controller with a Smith Predictor can account for this deadtime so that the level loop can be tuned aggressively to handle changes in the process and hold the level steady.

Sergei shared how these two loops are cascaded where the level controller is the master loop and the flow controller is the slave loop. He wrote:

When a forming bin level gets too high, the master sends a lower flow setpoint to the flow controller. If the level gets too high, flow setpoint is reduced. Both slave and master have their respective process deadtimes compensated by the Smith Predictor algorithm, so the cascaded pair works almost as if there is no deadtime at all.

The Smith Predictor does very well in processes with a fixed deadtime. When the deadtime varies, advanced process control (APC) strategies like Model Predictive Control can help provide reliable control.

MP3 | iTunes

I saw news of a combustion optimization and simulation project award for a 385 megawatt power station in the U.S. It reminded me of Emerson's Jeff Williams presentation on the topic of emissions reduction equipment optimization at the last Emerson Exchange. He also presented at the ISA Power Industry Division Symposium on the topic of applied statistical analysis for performance calculations.

In power producer's quest for cleaner, greener operations, these statistical optimization methods are showing great applicability. Statistical tools like Principal Component Analysis (PCA) help to discover which process variables have the most influence on heat rate distribution. For those like me that are unfamiliar with the term heat rate, I found this definition:

A measurement used in the energy industry to calculate how efficiently a generator uses heat energy. It is expressed as the number of BTUs of heat required to produce a kilowatt-hour of energy...

It's often the case that mechanical problems or incorrect loop tuning cause most energy losses.

Jeff describes the process to find optimization opportunities. It starts with mining the automation system's historical data. He shared results from 200MW coal-fired generating units that were identical in design. The SmartProcess team took 9 months of performance data from an Ovation system.

The PCA analysis on twin 225MW units provided fast identification of the greatest effect on heat rate increase (reduced efficiency.) The two major causes were wide variability of reheat steam temperature when Unit A was at low load and variability on the condenser unit of Unit B.

New correction curves (heat rate in BTU/kWh versus reheat steam temperature in degC) were established. These were created through empirical modeling of the heat rate based on historical data. The model is created using tools such as linear and nonlinear regression, neural networks, and hybrid methods. A model of heat rate is created based on the main input operating parameters of the unit. A calculation of gradients is performed to generate these new correction curves. These curves were tested for reheat temperature.

These statistical methods were also applied to oxygen concentration in flue gas. Higher O₂ leads to increased flue gas temperatures, which increases the unit heat rate. Optimizing the O₂ concentration improved the heat rate.

A final example was with the unit's feedwater temperature control. The PCA analysis showed high variation of feedwater temperature across various generator loads. It caused a significant energy loss due to the lower feedwater temperature. Once identified, the team took actions to correct the control valves and loop tuning that was causing the excessive variability.

These statistical methods showed the greatest sources of energy losses without having to devote extensive engineering efforts to build accurate thermal models for comparison to the actual plant operational data. Jeff notes that this also means less ongoing maintenance is required for these applications.

MP3 | iTunes

One only has to come across reports like the Institute for Supply Management's Manufacturing ISM Report On Business to see the slowing global economic conditions. The report notes:

Manufacturing activity continued to decline at a rapid rate during the month of December. The decline covers the full breadth of manufacturing industries, as none of the industries in the sector report growth at this time.

So if you're a process or plant or automation engineer, what do you do?

I exchanged emails with Emerson's James Beall to ask him what changes he's hearing about from process manufacturers since economic conditions began slowing in the fall. James and the variability management consultants help process manufacturers find ways to optimize their process.

James wrote:

Certainly, the focus has shifted from increased production to decreasing the costs of goods sold. Energy savings have even more emphasis than before. Distillation process are heavy energy users and are often using 5-25% more energy than is required--unless they are using a properly designed Model Predictive Multivariable Controller (MPC) or advanced regulatory control strategy.

Besides lower energy usage, improved control performance reduces variability allowing the process to operate closer to its constraints, which can improve yield.

In an earlier process variability post, James cited a study from the team's work that showed major causes of variability include control valve performance (30%), improper tuning (30%), and improper process and/or control scheme design (20%). In the ideal world, you could optimize your plant and reap these benefits throughout the plant's lifecycle. For the 20% process design issues you can. Unfortunately for the rest, the law of entropy being what it is--production processes tend to disorder over time. Valves stick as they wear, sensors plug, vibration on rotating equipment increases, etc.

Where digital instrumentation exists, the devices can report these issues to the operations and maintenance staff. A 4-20mA analog input signal provides a process variable, but not if the measurement signal is good. Likewise, a 4-20mA analog output signal to a control valve without feedback does not let the control algorithms know if the valve has moved to its intended location. These issues have to be uncovered through offline analytical techniques.

These all combine to change process dead times (the time delay from an output change to a change in the process variable) and the control dynamics of the process. The goal is to try to make the process dynamics as linear as possible and minimize dead time.

James recommends that you measure these changes in dynamics, annually at a minimum, and more frequently if the ROI justifies it. He and the team use Emerson's Entech Toolkit to identify common dynamics such as first order, second order overdamped and integrator+lag. This helps identify the process dynamics so that the control loops can be properly tuned.

With the process dynamics clearly understood, and final elements and measurement devices repaired or replaced, James and the team help plant engineers select the proper control algorithms for the process dynamics and tune the loops for best response without oscillation.

With the process properly lined out, it can now operate closer to operating limits due to reduced variability. Also, waste is reduced and less energy is typically consumed. These all directly impact the bottom line--a very good thing for these economic times.

Well-tuned regulatory control opens up the opportunity to also apply advanced control algorithms like MPC at a process unit level to further improve control performance and reduce operating costs.

MP3 | iTunes (I'm trying a trick from Gary Mintchell to stand to see if that adds more energy to my voice in these podcasts.)

Update: I just saw ARC Advisory Group's Larry O'Brien reference the ISM report. I was hoping my post was first because I found it by Googling around. Alas, Larry's post was the day before. Read it for more on the ISM findings and trust that I'd link to his post, if he was my original source!

When I struggle with a technical issue, whether it is hardware, software or just something I'm trying to understand better, I usually start with Google. I'll add as many relevant keywords as I can think of, and add "howto" as one more keyword. The search often returns amazing results, usually from individual's blog or a response in a forum, describing exactly how to solve the issue.

The web offers so many ways, not just blogs and forums, to share your interests and expertise. These many ways were the focus of a presentation Deb Franke and I gave at last year's Emerson Exchange.

At the ModelingAndControl.com blog, Greg McMillan shares his wisdom every week. This week's post, What Have I Learned - Einstein and the Ultimate Limits for Loop Performance is a perfect example. If you're a process control engineer and you're not already subscribed to the blog's RSS feed, I recommend you do.

This post offers straightforward guidance, like:

The absolute limit to feedback control system performance is the total dead time in the loop, which is the summation of all the final element, process, measurement, I/O, and controller execution time delays. A feedback control system cannot correct for something it hasn't seen yet and hasn't been able to change yet in the process...

Greg references an on-line eBook, Funny you should Ask a Process Control Engineer where you can find more information to support this guidance. Greg has numerous eBooks, application notes, lectures, and articles available on the Modeling and Control Blog.

Another example Greg offers is that advanced process control (APC) also cannot violate this absolute limit. He writes:

Many of the early APC algorithms significantly increased the loop deadtime (See "Advanced Control Algorithms- Beware of False Prophecies in the Funny Thing E-book). While model predictive control (MPC) can potentially help dead time dominant systems, the original execution time (e.g. 1 minute) of separate MPC software packages was so large their applicability was restricted to slow processes. With the advent of the MPC embedded in the DCS, the execution time can be as fast as 1 second which means MPC can be applicable to all but the fastest processes (e.g. liquid pressure control and furnace pressure loops).

In all of Greg's guidance, he provides links where you can get more detail. It's like a self-directed, university-level course for process control engineers. All one needs is the quiet and focus to take it in and absorb it.

I'm sure you have some specialized knowledge for which you're known. If you're the type of person who believes you'll get back far more than you give, consider using some of the tools we mention in our Emerson Exchange presentation like Google Reader, Delicious, LinkedIn, Twitter, etc. to share this knowledge for the next person searching for answers.

MP3 | iTunes

One of my posts, Improving Gas Plant Throughput and Robustness with MPC, brought a strong response from a reader. Entitled MPC Unsustainable Benefits, it described his experiences applying MPC in a major U.S. oil company over a ten-year period.

He summed up his thoughts, "MPC is an 800-pound gorilla. It can be big and ugly. Benefits are retained by thin threads. Benefits tend to leak." You can follow the link to read some of his points around design, control models, valve linearization, MPC engineering, and the monolithic nature of MPC models. Even for someone like me not steeped in the wisdom of advanced process control (APC)--it was readily apparent that he is not a fan of model predictive control.

I forward his comments to members of Emerson's advanced automation consulting team for their thoughts on some of the impassioned points made. Senior process control consultant, Greg Martin, thought the key phrase in this document was, "Large-scale MPCs are monolithic."

Greg notes that traditionally there have been two approaches to MPC applications:

1. The "big matrix"
2. Smaller controllers that fit the process applications, working in parallel

An example "big matrix" would be to put a whole gas plant in one controller. An example of smaller controllers would be to have individual MPCs for each column. He believes the perspective of this response is from the "big matrix" view. In that context, many points made are true. From Greg's experience, they are not true if the smaller controllers are used.

Automation systems like the DeltaV system have embedded model predictive controller function blocks into its library of control blocks precisely to provide automation engineers a way to apply MPC at a unit level like a distillation column, lime kiln, or fermenter. The advanced automation consultants have created a library of SmartProcess applications to fit these process applications.

Greg had some thoughts on specific points raised in the document. "Keep Regulatory Control Loops closed - Suffer from terrible model mismatch errors." Greg believes the first step in one of these applications is to fix the regulatory loops through tuning or modifications of the existing devices. No amount of advanced control can help if the control valve is improperly sized or not functioning properly. These loops do not necessarily imply a model mismatch.

MPC applications that reflect the objectives that the operating supervisors seek do stay on for a long time in Greg's experience. The MPC application should be a "white box" that is understood and owned by the operating supervisor.

To the point, "FCC Unit MPC is still being re-designed and re-done at many sites", Greg believes that most are due to system changes. Applications that have the greatest success and longevity are usually are process-centric. MPC does best when controlling at the process constraints. When the valve position is a constraint, the best approach may be to change the trim of the valve. Altering this trim will necessitate an MPC model update, but controllers that are smaller in scope are easier to update and maintain.

I consulted my trusty friend, Wikipedia, about the history of MPC, and it has been applied since the 1980s. With the march of technological progress, the applications have migrated from the "big matrix" to smaller, unit-level application as the MPC software has moved from host-level computer systems into automation system controllers.

I appreciate the reader and Greg sharing their thoughts and experiences. Join in if you have some thoughts to share.

One quick note, I'm not in the office this week and don't have access to my podcasting setup, so this week's posts will not include podcast entries.

Last week I mentioned uploading two of ModelingAndControl.com blog's Greg McMillan's recent presentations. Like I did with his first presentation, here's a short recap of the second one, Control Loop Foundation for Batch and Continuous Control:

What are great about Greg's presentations are the specific application examples. Visit the slides 19-21 to see ways of improving neutralizer control using Feed forward control, signal characterization and proper piping to provide proper spacing for measurement devices. Similarly, slides 22-24 show ways to improve distillation column control using Feed forward control and signal characterization. You mostly don't realize the benefits of improved control until you reduce variability and move the setpoint closer to the operating limit.

Greg is really good at boiling things down. Here are his words summing up basic opportunities in process control (from slides 27 and 28):

• Decrease stick-slip and improve the sensitivity of the final element (Standard Deviation is the product of stick-slip, valve gain, and process gain)
  • Use properly tuned smart positioners, short shafts with tight connections, and low friction packing and seating surfaces to decrease valve slip-stick and dead band (do not use isolation valves for throttling valves)
  • If high friction packing must be used, aggressively tune the smart positioner
  • Improve valve type and sizing and add signal characterization to increase valve sensitivity
  • Use variable speed drives where appropriate for the best sensitivity
• Improve the short and long term reproducibility and reduce the interference and noise in the measurement (Standard Deviation is proportional to reproducibility and noise)
  • Use magnetic and Coriolis mass flow meters to eliminate sensing lines, improve rangeability, and reduce effect of Reynolds Number and piping
  • Use smart transmitters to reduce process and ambient effects
  • Use RTDs and digital transmitters to decrease temperature noise and drift
• Reduce loop dead time (Minimum Integrated Error is proportional to the dead time squared)
  • Decrease valve dead time (stick and dead band)
  • Decrease transport (plug flow volume) and mixing delay (turnover time)
  • Decrease measurement lags (sensor lag, dampening, and filter time)
  • Decrease discrete device delays (scan or update time)
  • Decrease analyzer sample transport and cycle time
• Tune the controllers (Integrated Error is inversely proportional to the controller gain and directly proportional to the controller integral time)
• Add cascade control (Standard Deviation is proportional to the ratio of the period of the secondary to the process time constant of the primary loop)
• Add feed forward control (Standard Deviation is proportional to the root mean square of the measurement, feed forward gain, and timing errors)
• Eliminate or slow down disturbances (track down source and speed)
• Add inline analyzers (probes) and at-line analyzers with automated sampling since ultimately what you want to control is a composition
• Optimize set points (based on process knowledge and variability)
• To realize the benefit of reduced variability, often need to change a set point

He sums up the presentation with these key points:

• Tune the loops
• Use digital positioners and throttle valves to get resolution better than 0.5%
• Use Coriolis and Magmeters to get accuracy better than 0.5% of rate
• Add cascade and feed forward control for disturbances
• Model the process to dispel myths and build on process knowledge
• Improve the set points
• Add composition control
• Reduce the size and speed of disturbances
• Transfer variability from most important process outputs
• Add online data analytics (multivariate statistical process control)
• Add online metrics to spur competition, and to adjust, verify, and justify controls

View or download the presentation if you think some of this guidance might benefit you.

MP3 | iTunes

ModelingAndControl.com blog's Greg McMillan copied me on two presentations he recently gave to a major chemical manufacturer. Being a blogger and firmly believing that great content should be shared with the world, I asked Greg if I could upload the files to my slideshare account. Greg graciously agreed.

Here's one of them, Opportunity Assessment and Advanced Control:

Greg listed the benefits that advanced process control can bring, based on his experience and 33 year career in the chemical manufacturing industry. These included:

• Improved yield (better selectivity)^*
• Less blending, scrap, and rework or higher price for higher grade^*
• Lower utility costs (energy minimization)
• Higher production rate (feed maximization)
• Increased on stream time (fewer shutdowns)
• Reduced maintenance (less stress on equipment)
• Safer operation (fewer shutdowns and less stress on equipment)

^*The benefits for improved yield and less scrap or rework can be taken as an increase in capacity or a reduction in raw materials

The presentation is rich with guidance for opportunity sizing and assessment, common myths and misconceptions, lessons learned, rules of thumb, and of course, Greg's famous top 10 lists. I'll highlight just the opportunity assessment portion of the presentation and leave the rest for your perusal.

Greg showed a chart of three companies who benchmarked their regulatory and batch control, advanced control, and data management. The total improvement in cost of goods sold (COGS) across these three categories was 8%.

Greg advised to begin with a thorough opportunity sizing before the opportunity assessment using cost sheets, product prices, historical trends, business plans, research reports, technical studies, and simulations to establish actual, practical, and theoretical performance--like yields and capacity improvements--with operations and technology.

Next, he counseled to work with the plant process engineers to go through the process, identify constraints, and offer ideas on opportunities to reduce gaps identified in the opportunity sizing exercise to see and work way out of the current process box. You'll want to avoid the temptation of a canned solution or to come to conclusions before the plant personnel thoroughly discuss peculiarities and special problems. Greg felt that it's important for knowledgeable people to speak first and ask questions--and to hold off on solutions. Instead, offer concepts that people can use to generate solutions and be a good listener during this phase.

And from the process itself, use the automation system and the historian to find loops in manual, limit cycles, slow or oscillatory set point and load responses, and controller outputs running near limits.

Your next step is to look for opportunities to infer compositions from fast, lower maintenance measurements such as density, viscosity, mass spectrometers, microwave, and nuclear magnetic resonance. Seek applications of accurate mass flow ratios for material balance knowledge and control.

You'll want to ask the operations folks what would happen if a set point or operating mode were changed. When developing possible solutions, pick control technologies to address opportunities and give relative estimates of implementation cost and time (e.g. high, medium, low) and percent of gap addressed. For a sanity check, ask plant process engineers to estimate percentage of gap addressed by each solution.

Greg's closing thoughts for this assessment process were to take advantage of momentum and group enthusiasm by starting on "quick hits" immediately and setting definitive schedules and assignments for others (to avoid inertia of waiting for a quote or study.) Finally, take the action to tune the loops and improve the loops.

If you view or download the presentation, look at some of the questions you should ask during this assessment on slides 16 and 17. Hopefully you'll find some nuggets on how and where to apply APC to reduce your COGS.

MP3 | iTunes

I saw Emerson's James Beall the other week and asked him for a copy of his Emerson Exchange presentation, Interesting and Useful Features of the DeltaV PID Controller.

Every year, James presents to standing-room-only crowds and his presentation (given twice) this year was no exception. PID or proportional-integral-derivative control is definitely not a new concept. I did some Googling around and found references to it dating back to 1922 when N. Minorsky published an article on its use for automatic ship steering control.

While PID control has been around for a long time, technologists keep adding innovations, like degrees of freedom to the proportional action and the derivative action.

James began by describing three common PID forms: parallel, standard (a.k.a. ISA form), and series (a.k.a. classical form.) The standard form is the default choice in the DeltaV PID function block and the series form is an option. James counseled that the choice is based on your prior experience and personal preference. The series and standard forms are identical if the derivative action is not used. Also, your choice of forms can impact the conversion of tuning constants from a previous control system.

The PID function block also has a STRUCTURE parameter that provides two degrees of freedom for the proportional and derivative actions. On a change of setpoint (SP), you can scale these actions (BETA = proportional action scaling, GAMMA = derivative action scaling) between 0 and 100%.

The PID function block has an integral dead band (IDEADBAND) for when the error (SP minus PV) gets within this dead band. At this point, the integral action stops. James described a level controller application that feeds a downstream unit in order to reduce the movement of the controller output when the error enters the dead band.

James discussed three setpoint filters based on rate of change. One filter provides a time constant in seconds of the first order SP filter (SP_FTIME). Another provides a ramp rate at which downward setpoint changes (SP_RATE_DN) or upward setpoint changes (SP_RATE_UP) are acted on when the loop is in automatic mode.

Limits can also be placed on highest and lowest setpoints allowed, whether or not these limits are obeyed when the loop is in cascade or remote cascade mode, or whether output limits of the master loop in a cascade pair are used to limit the setpoint to the slave loop in cascade and remote cascade mode.

On the subject of cascade-control loops, James shared how mode tracking, bumpless transfers, and other loop interactions are automatically handled by the PID block's BKCAL interblock communications.

Gain scheduling is another PID control innovation for loops with nonlinearities where different regions of the PID controller can have different PID tuning parameters. The DeltaV PID function block can have up to three regions with different tuning parameters, based on a selected state variable (output, process variable, error, production rate, etc.) The algorithm provides a smooth transition between the regions.

James also provides guidance on valve output characterization and anti-reset windup limits in the presentation. Although these advanced PID functions can appear quite technical, they can significantly improve the performance of PID control and provide ways to handle difficult process dynamics. The bottom line to getting this control right is better control performance and a more efficient process.

You can read about the full capabilities of the PID function block in the 9.3 version of DeltaV Books On-line.

MP3 | iTunes

The controlled chaos that surrounds a plant turnaround, or planned shutdown, has given more than a few engineers some gray hair. I highlighted a plant turnaround planning presentation at last year's Emerson Exchange and I asked Emerson's Chris Forland if I could get this year's presentation.

Chris, Scott Grunwald, and Miranda Pilrose presented, Parts, People Process: The Winning Formula for Emerson Turnarounds and Certified Services.

Some of the challenges causing the gray hairs to sprout include the loss of experienced folks to plan and execute the turnarounds. You can also count on finding things during the turnaround that you did not expect. You might also miss finding hidden problems during the turnaround that manifest themselves once you've started the process up again.

The turnaround period is also a golden opportunity to look for optimization opportunities to reduce energy consumption and improve process efficiency.

Chris, Scott and Miranda stressed the need to address these challenges head on by starting the planning process early--since the plan flexibility decreases as the turnaround start date approaches. It's likely that any investment in pre-turnaround planning and equipment analysis will rapidly pay itself back in improved performance.

They describe a six-step turnaround program that includes project kick-off, condition assessment, refining the details, internal planning, turnaround execution, and post-turnaround review.

The project kickoff step defines the scope of outages, personnel, roles and mission of the Emerson turnaround team. The turnaround project plan is thoroughly reviewed, maintenance records are reviewed, and the timing, duration, and budget are scoped. The team conducts a detailed plant walk-down to familiarize everyone with the facility and the challenges.

The condition assessment step looks for control performance issues while the plant is still running. It identifies equipment, control strategies and process dynamics that need to be addressed during the turnaround.

In the refining the details step, internal valve conditions are analyzed with Flowscanner and AMS ValveLink, process dynamics are measured with the Entech Toolkit, and gap analysis is performed to find opportunities for integrating with other plant software like computerized maintenance management system (CMMS) software. Another key activity is to review the plant's use of diagnostics in turnaround planning and maintenance.

Turnaround execution--the time of controlled chaos--is made more manageable because only the valves that need work are removed. Since the conditions are known ahead of time, the necessary repair parts can be on hand and work performed to a pre-planned schedule. During this period of frequent communication among turnaround team members, status reports are updated and changes to the turnaround plan are documented and rescheduled as required. Equipment asset performance is returned to OEM specification with the necessary ASME conformance and FM Approvals documented. Predictive diagnostic technologies can also be installed and commissioned during this step. Finally, per the measured process dynamics, tuning and control strategy adjustments are made to optimize the performance of the process.

The post-turnaround step captures and documents what was learned throughout the planning and execution--for the next turnaround that will likely include many new team members from the process manufacturer's staff. Budget items are reconciled, improvements documented, asset repair reports assembled, valve diagnostic curves archived, and baselines generated for ongoing performance analysis. The information is assembled into a final documentation package and reviewed at the post-turnaround review meeting. It's also important to quantify the improvements to verify the value of the time and resources that went into this extensive planning and execution process.

As part of the team, Emerson brings expertise from many areas including instrument & valve services, electrical reliability, and control system performance due to the wide-ranging skills required to perform a successful turnaround.

The key is to identify, plan and schedule as much as possible--as early as possible--to minimize the unplanned, gray-hair producing moments.

MP3 | iTunes

Last week at the ISA Expo in Houston, I sat in on a great session featuring Emerson's Ed Bailey, as well as folks from Siemens, Ametek and a private consultant with years of experience with Dow Corning. The session was entitled, Energy Management Issues for Process Optimization, and it had the following description:

Subjects open for discussion in this session include nearly anything relevant to this topic, not just process control and instrumentation. Expect discussions regarding process maintenance, process modifications, maybe whole new processes that were less cost effective under the old energy cost structure but now are more cost effective.

Ed leads the technology development efforts for the Rosemount Analytical Gas measurement products. He kicked off the panel discussion showing the forecasted growth of energy production. From an ExxonMobil outlook study, most of the world's growing energy needs will continue to be met by the combustion of oil, gas, and coal.

To help manage the carbon emissions, to deal with the increases in fuel costs over their historical averages, and to operate in an environment with increasing governmental regulations, process manufacturers have an ever-increasing need for improved combustion flue gas analysis. The best way to minimize carbon dioxide (CO₂) emissions is to operate existing combustion processes at their maximum efficiency.

Ed described some of the existing industry practices like averaging the output of a few analyzers as not providing enough insight to diagnose and optimize the burners. Burner differences and stratification are normal conditions that this averaging strategy does not well address. Instead, Ed recommended a mix of oxygen (O₂) and carbon monoxide (CO) measurements be used combined with neural network strategies that enable more complex models to be created to maximize efficiency versus the load/fuel variations--and to minimize mono-nitrogen oxide compounds (NO_x). The key point is that more discrete measurement points, which in turn feed more sophisticated control algorithms, will drive efficiency.

One of the discussion points during the session was the use of zirconium oxide (ZrO₂) oxygen analyzers to measure the residual oxygen remaining in the flue gases from any combustion process. Ed mentioned the Rosemount Analytical in-situ oxygen transmitter as an example of a zirconium oxide oxygen analyzer to help provide data to better control and optimize the combustion process.

An interesting question came into the panel about the safety considerations of running the combustion process right on the edge at its most efficient but potentially dangerous point. The panel had good thoughts that you need to separate the control aspects from the safety instrumented system burner management aspects. Like any process with safety risks, a risk analysis and risk mitigation strategy per the IEC 61511 international safety standard is critical.

MP3 | iTunes | My Podcast Alley feed! {pca-d211b332524855a78944048f9c70f6e7}

A great presentation at the recent Emerson Exchange was one that discussed the results of applying model predictive control (MPC) in a challenging gas plant process. Model predictive control has been available for decades and used in very large applications such as refinery process units, but its use in smaller applications found in the oil and gas sector is relatively new.

This plant was limited by its field compression capability, and throughput could be increased if they could reduce the differential pressure (DP)--mainly by lowering the inlet pressure to the gas plant. Swings in gas flows were introducing disturbances to downstream equipment such as carbon dioxide removal trains, causing them to trip. This caused further disturbances and in turn caused the operators to run these CO₂ removal trains very conservatively to minimize the risk of cascading train trips. The result was that overall throughput was reduced--directly impacting the bottom line financial performance of the plant.

Sarah Perkins and Andrew Taylor of ProSys Engineering, based in Australia, were called in to work with the gas plant's engineering staff to develop control strategies to maximize throughput while minimizing upset conditions and their cascading effect on other process units.

They saw three areas where advanced process control, specifically MPC, could be applied to meet the overall objective of maximizing plant throughput capacity. These included minimizing the differential pressure across the CO₂ removal trains, minimizing the liquid recovery plant (LRP) inlet pressure (to reduce overall plant inlet pressure), and using the plant's incoming pipeline as a surge vessel to eliminate spikes in inlet pressure which might trip the reciprocating compressors.

Let's dig in a little deeper in one of these areas--CO₂ removal trains. This gas plant had a number of these trains in parallel. The objective of the model predictive control strategy was to maximize the flow through the train either to a specified high limit or to valve saturation--whichever constraint was active first. Satisfying this objective effectively minimizes the differential pressure across each CO₂ removal train.

For each train, they designed and implemented a dedicated DeltaV PredictPro MPC control block running in their DeltaV controllers. To minimize the differential pressure, the goal was to maximize the butterfly valve opening coupled with the need to quickly cut back the flow in case another CO₂ removal train trips. With this MPC-based flow controller, the butterfly valves were linearized where the output was expressed as a % of flow capacity, instead of a % of valve position. Each butterfly valve had different flow characteristics, so each MPC flow controller was individually characterized.

The feed gas flow was the manipulated variable; the constraints were setpoint (SP) minus process variable (PV) error, and an operator-entered maximum flow rate. These constraints help to detect trip conditions and honor process limits like flow rates at which foaming begins to occur. In abnormal situations, the maximum flow rates are set to current flow rates to allow the operators a chance to make decisions about redistributing the flow rates.

The team made custom graphics for the operators to see the CO₂ removal trains on a single view, to quickly recognize patterns of abnormal situations and to take manual corrective action.

The payback on increased throughput was less than two months and even more throughput will occur when all of the reciprocating compressors are reconfigured for the reduced operating discharge pressure.

With MPC available at the DCS controller level, it can be applied to many smaller and mid-size applications in oil & gas and other industries. Engineers like Andrew and Sarah are helping process manufacturers solve challenging problems like these that also deliver fast financial results.

MP3 | iTunes

I've been catching up on some of my automation and industry RSS feeds, and saw an interesting post, Energy Costs: Why is Industry So Slooooooow to React?, from the Energy Pathfinder blog.

The post describes process manufacturers struggling with high energy costs. They tend to pursue lower energy prices first, but cutting waste is a much slower process. The fourth bullet point caught my attention:

To make energy improvements, a facility must accommodate change. Meaningful energy solutions require some combination of changes to technology, procedures, and practices. Change poses challenges--even threats--to people whose livelihood is connected to long-standing procedures and priorities. Change requires front line energy managers to practice a certain amount of salesmanship. Sadly, this kind of communication is often not the strength of most powerhouse superintendents or maintenance directors. Many good energy-saving proposals never get off the ground for this reason.

I sent a link to the article to Emerson's Bob Sabin, whom you may recall from earlier posts. Bob is an energy-management consulting engineer and I wanted to see if his experiences were similar or different.

Bob wrote back:

It is curious why North American industry has been slow to react to energy costs, but then we have seen the same deliberate, measured response to other competitive pressures. Energy improvement projects compete with all other potential maintenance or improvement projects for the scarce capital dollar.

The way many organizations are structured, it does typically take a person acting as a project champion to raise an energy improvement idea for consideration. It takes a lot of effort to deliver the documentation regarding payback, to convince business management that there is low risk, and then to work with line operations to convince them that the project is in their interest, also. These champions most often emerge from operations or engineering middle management.

Unfortunately, middle management in many plants/mills suffers from existing day-to-day challenges and the lack of resources and training. They are often not in a position to make necessary changes to entrenched work processes. We see this every day in the instrumentation and control business.

With PCs on every desk, handhelds by the dozens, the Internet, wireless, and other technologies, a large percentage of plants/mills still struggle with basic process measurement and automatic control. There is still quite a bit of opportunity to apply basic process control technology to reduce energy consumption and improve other production performance measures.

The potential savings from lower energy costs can help place focus on education, leadership, and training which in turn will improve energy performance and other business metrics.

I agree with Bob's assessment that progress begins with economic justification and the focus of an organizational champion to drive the process forward. With many North American facilities designed in an era of inexpensive energy, folks like Bob can work with plants and mills to develop the justification to make their production process more energy efficient.

My colleague, Deb Franke, pointed me to a great article in her RSS feeds. The ChemicalProcessing.com article, Innovative Fixes for Saving Energy in Plants, describes some ideas to help reduce plant energy costs. Although energy costs have come down in recent weeks, they are still one of the largest controllable costs as I have mentioned in an earlier post.

The article points out innovative solutions including dual drive pumps, variable speed motors, water/glycol systems, automated blowdown systems, low BTU sweep gas and wireless sonic leak detectors. Give the article a read if you think some of these might apply in your plant processes.

I forwarded the article to Emerson's Lou Heavner, whom you may recall from earlier advanced process control application posts. I asked what new and innovative, energy saving ideas he might have to share.

Lou had a couple of ideas. But, being the modest sort, he added a caveat that they may not qualify as new or innovative. To me, if you're looking for ways to reduce your energy costs and you didn't consider one of these, it's definitely new.

Lou's first thought was on distillation processes. He writes:

In distillation, relative volatility and hence difficulty of separation tends to improve at lower pressure. When cooling water and/or air are used to condense the overheads, the pressure is often tightly controlled for stability in the face of changing ambient conditions and the extra cooling capacity available during nights or colder weather is not fully utilized. If pressure is allowed to "float" and as much condensing occurs as is possible, pressure will fall in the column and separation will normally improve. This means less heat is needed in the reboiler and hence energy savings when using steam or some other "costly" utility stream to provide reboil.

His second thought was around combustion processes burning fuel gases with changing compositions. Lou notes:

In heaters or boilers where the gaseous fuel consists of a hydrocarbon mixture of varying composition (like refinery fuel gas), a change in fuel can have an effect on the heat generated by combustion and on the excess air level in the flue gas for a given fuel flow rate. Sometimes, if variability of the flue gas justifies, companies will install fuel quality analyzers that measure composition or heating value. In many cases, the same thing can be achieved and better flow control at the same time, by using a Coriolis mass flow meter. It turns out that the mass flow of a hydrocarbon and the "btu" flow are directly related since both are related directly to MW.

You can't do this with PT compensated flow, because it knows nothing of MW. But Coriolis measures mass directly and can be used to reduce variability of "btu" feed to the burner. This can be dramatic where the fuel gas varies significantly. It is not a good solution if the "btu" content changes due to the presence of inerts (like N₂ or CO₂) or non-hydrocarbons (like H₂ or CO), since they do not exhibit a linear relationship between mass flow and "btu" flow. But if they are present in small quantities and don't vary much, the concept can still work.

On processes that degrade the "quality" of energy, Lou shares:

Saving energy can be as simple as minimizing thermodynamically irreversible operations. Mixing, heat transfer, and throttling of process flows are common examples of irreversible processes. In general, industry should avoid over-purifying/heating/cooling followed by mixing or blending to achieve the target composition/temperature. Process design should attempt to get as much work as possible out of utilities and recover as much heat as possible. Pinch technology is one approach to heat integration design used by process engineers. Of course, there are practical limitations like capital cost considerations, dynamic response and controllability, and availability/reliability of utilities, especially ambient cooling.

Also, control valves should be selected to minimize throttling losses and allocation and valve position should be used to minimize overall pressure drop in systems like utilities where resources are shared by different units or equipment. For example, if multiple reactors are cooled with a shared refrigeration unit, the coolant temperature setpoint can be raised (reducing the refrigeration required) until one of the user's demand exceeds the capability of its corresponding control valve to deliver.

Let's hope that something between the ChemicalProcessing.com article and Lou's thoughts provides you at least one idea that can help reduce your plant's energy bills.

Let's close the week with a short post about energy saving opportunities. The pain of higher energy costs is fresh on my mind with an unexpected trip by car from Austin to Houston and back with gasoline prices now just shy of $4/gallon USD.

Back in May, I wrote about an AIChE paper Emerson's Doug White presented, How to Save Energy through Advanced Automation. Doug is a principal consultant and vice president for advanced process control (APC) services, and has many years of experience justifying, designing, installing and commissioning APC applications for process manufacturers.

If you didn't get a chance to hear Doug present this at the AIChE Spring meeting, or read the PDF of the presentation, you may have a chance to see him live in your area to get your energy-saving questions answered. He's teaming with Scott Pettigrew, an Emerson senior energy consultant.

This seminar series will begin in the Houston area, in La Porte, and will be jointly hosted by Emerson and its local business partner, Puffer Sweiven.

From the seminar flyer, here's what it covers:

Survey the root causes of excessive plant energy usage and how automation can reduce consumption. Review a systematic approach to identifying potentially high payback improvement areas and solutions. Opportunities can originate in the process, measurement devices, valves, or controllers. Learn basic principles and key concepts to understand the nature of challenges and options. Actual plant case studies will be presented. Specific operational improvements in the following areas due to enhanced automation performance will be addressed: reduced fuel costs, reduced electricity usage, reduced steam costs, increased equipment availability, reduced compressor costs, improved boiler efficiency.

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The date is August 21^st from 7:30 to 1pm U.S. Central time at the Puffer Sweiven La Porte office. Send an email to RSVP a spot.

Rumor has it that they'll be another session further East along the Gulf Coast, and possibly other locations. I'll update this post as I hear more.

If you have interest in your area, send me an email and I'll pass it on to Doug, Scott and the team.

I was catching up on some of my industry-based RSS feeds and came upon an Energy Pathfinder blog post, Taming Energy Costs While Going Green: An Open Letter to Corporate America. The blog's author, Christopher Russell, asks and answers:

Energy cost control... Green marketing... Can you be successful at both? The answer is "yes," but you should be prepared to manage both in a combined effort.

What caught my eye was his fourth point:

Harvest more value from your existing process control systems. Companies everywhere are relying on information systems to manage their core production processes. It's a small effort to amend those same systems to accommodate energy performance monitoring. Energy savings can increase the returns on existing control systems.

I ran this post by Emerson's Bob Sabin, an energy management specialist. You may recall Bob from earlier posts on boilers and energy management. With the rapid escalation in energy prices, you might imagine that the energy management team is pretty busy--and you'd be right.

I asked Bob for his thoughts on this fourth point, and he had a great response:

I believe it is true that many existing process control systems can be amended or enhanced to provide additional value in energy performance improvement. In the simplest case, the energy performance of most any process equipment can be closely monitored for efficiency of energy use. Trends of energy efficiency can be examined over time, and when degradation is seen, the root cause can be quickly identified and remedied. Monitoring of efficiency can be done locally at the plant/mill site, or it can be handled remotely by a central team or service provider.

In addition, processes can often be run with less variability such that they can be pushed nearer to their constraints. Being nearer to process constraints frequently brings the benefit of improved energy efficiency. Enhancing controls will drive reduced variability by allowing full automatic operation for a higher percentage of time and/or providing calculations that compensate for incoming variability.

Further, for sites that have complexity in operation that affects energy use, it can be beneficial to provide enhanced information systems capability that will support profitable operations decision making.

Often, energy needs, energy prices, and operating scenarios change so quickly and with so many permutations that it is virtually impossible for operations personnel to determine the single most profitable operating scenario at any given time. An Energy Management Information System (EMIS) can deliver this information in real time every day, all day.

An EMIS consists of a model of the processes involved that is automatically fed process data and gathers or takes user entered cost data. The EMIS model arrives at the most profitable operating scenario based on current production needs, actual costs in play, and the constraints that are in place for process operation. Emerson supports process manufacturers with various types of performance monitoring, variability reduction, and EMIS implementations.

As with most things we do, focus can produce results. In this case, energy savings can be achieved by leveraging and amending the existing process control and information systems. Depending on your plant or mill's energy consumption, it may be worth the development of models to compare actual operating conditions against the ideal case for optimum profitability.

Emerson's Mark Coughran has been busy sharing his process control expertise lately. His latest article, Improve Batch Reactor temperature control, appears in the June issue of Chemical Processing magazine.

Mark describes three batch reactor temperature control cases with split-range control configurations. The first case involves control valves to hot and cold headers on the reactor jacket. The second case involves control valves to steam and chilled-water heat exchangers and the final case involves a control valve on the chilled fluid and variable electric heater.

You'll see common advice in the posts where Mark is featured. In this article, he summarizes this advice into five recommended steps on how you should approach loop tuning:

1. Make the process dynamics as linear as possible.
2. Minimize dead time.
3. Measure the process dynamics.
4. Choose the right controller algorithm to compensate for the process dynamics.
5. Tune for the speed required, without oscillation.

Proper selection and sizing of control valves and minimizing non-linearities in control strategies such as dead zones in split-range control help to address the first point. For a batch reactor, the jacket heating and cooling responses may be very different. One way to mitigate this difference is to use a controller, which supports gain scheduling to provide separate tuning parameters for the cooling and heating steps.

Dead time (the time delay from an output change to a change in the process variable) can occur in the transport delay of heating/cooling media from the control valve into the jacket. Circulating pumps and jacket-temperature sensor location can help reduce this cause of dead time. Also, filters applied to the temperature transmitters will appear as dead time to the control loop. Mark counsels that you allow one overshoot on the jacket-temperature setpoint response to get the fastest linear response and to minimize dead time.

For measuring the process dynamics for integrating (those that ramp at various slopes on a change in output), processes like reactor temperatures are easily determined from step tests with the loop in manual mode. The proportional + integral + derivative (PID) controller compensates for these process dynamics. Proportional action is mainly used for integrating processes. Some derivative action may be needed on the reactor temperature controller but usually not for the jacket controller.

Mark recommends the Lambda tuning method to tune for the speed required without oscillation. Start with the jacket (slave) control loop first. It must be faster than the reactor (master) control loop per the rule of cascade tuning. For processes with significant nonlinearities, fuzzy logic control might work better.

As he concludes in the article, the benefits of getting this tuning right is improved product quality, reduced batch cycle time and reduced energy usage and waste.

I caught up with Emerson's Mark Coughran, a senior process variability consultant whom you may recall from earlier process tuning and optimization posts.

Mark shared a story of a plastics manufacturer that was challenged to bring a new product to market with a new extruder. This manufacturer needed to run trials with varying polymer formulations at various temperatures and speeds while trying to perfect the production process.

The plant control engineer was struggling with control strategy and necessary tuning to hold the required temperature. The temperature loop wasn't responding to setpoint step changes and was oscillating even when no disturbances were present.

When Mark arrived to lend his experience to this challenge, many anxious folks greeted him. The project engineer was glad to see him. The project manager asked if he could stay the weekend. The plant manager assured Mark that the situation had visibility at the highest levels of the organization. A corporate engineer added pressure by saying single loop control worked just fine at a similar plant. I imagine that Mark didn't enjoy all this attention.

He and the project engineer began by measuring the process dynamics--both the linear and non-linear components. For the linear process responses, Mark applied Lambda tuning.

For the non-linear portions of the overall process dynamics, the approach was to mitigate these nonlinearities as much as possible. They performed four actions to accomplish this. The first step was to improve the control strategy by changing the master loop configuration to prevent interaction with the slave loops. Next Mark helped identify and have an unnecessary interlock removed that disturbed the control loop.

The process dynamic measurements uncovered an extremely high process gain, which was reduced by establishing pressure control upstream from the extruder. Finally, the output pulsing was adjusted to better match the control strategy with the control valve dynamics.

After applying these changes, the temperature process variables tracked the setpoint changes over the operating range of the trials. Mark typically likes to work with the process manufacturer to financially quantify the results to prove the value of his services. It also helps the people he works with look good to their upper management. Unfortunately, until this new product gets to market its value is not yet determined and the control engineer didn't want to speculate. In this case, robust control was established and the level of anxiety dropped considerably.

James Beall delivered a Back to the Basics - Process Control Diagnostics Improves Refinery Performance presentation at the recent AIChE spring meeting. James, whom you may recall from earlier variability management posts, is a principal process control consultant. He's a senior member of Emerson's variability management consulting team.

In this presentation, James stressed what he normally stresses with process manufacturers--that some of the largest and most frequent opportunities exist in basic process control. These opportunities include eliminating variability at the source, tuning the controllers to meet the control objective, using ratio, cascade and feed forward control as well as using a process analysis system to diagnose problems and tune loops. Addressing these opportunities also builds a control foundation essential for any advanced process control (APC) initiatives.

He referenced a 1997 McKinsey study that showed 50-60% of the value realized from a process optimization project comes from addressing loop variability. The balance 40-50% comes from applying APC on top of these optimized loops. The financial results from reducing variability are being able to operate closer to constraints such as specification limits. Benefits can come from reduced energy consumption, less waste and rework, higher yields, higher quality, etc.

The variability management team keeps statistics on control loops with excessive variability from site audits. The major causes of this variability include control valve performance (30%), improper tuning (30%) and improper process design (20%).

James shared several valve-performance examples including a regenerator pressure valve. By looking at the setpoint, pressure, output, and valve position trends, he spotted the valve sticking and then jumping 3% followed by a quick spike of another 2-3%. This caused periods of oscillations before settling out. Once the sticking problem was addressed, the oscillations became tiny ripples on the trends. Similarly, poorly tuned loops can cause large oscillations impacting overall process variability.

He noted that you must have a process dynamics analysis and diagnostic tool of some type to pinpoint these sources of variability. Problem identification is the first step in corrective action. And these problems may be significantly impacting the overall efficiency of the process.

James described some of the tests that he and the variability management consultants use with the Entech Toolkit. One of the most important tests is to identify the process dynamics so that the control loops can be properly tuned. Emerson's Entech Toolkit can identify common dynamics such as first order, second order overdamped and integrator+lag. Dynamics that are more complex can be identified by this process analysis toolkit (11 tests in total) and the associated controller can be properly tuned. Many of the more complex process dynamic responses cannot be identified by less sophisticated analysis systems.

If you have the bandwidth and inclination to learn the skills to do it yourself, James recommends three Emerson Education Center courses: Process Dynamics, Control and Tuning Fundamentals, Process Analysis and Minimizing Variability and Modern Loop Tuning.

Emerson's Doug White sent me his presentations from the recent AIChE spring meeting. Doug is a principal consultant and vice president for advanced process control (APC) services, and has many years of experience justifying, designing, installing and commissioning APC applications for process manufacturers.

Given rapid rising energy costs, his presentation, How to Save Energy through Advanced Automation, caught my attention. He starts by showing an upward trend in natural gas prices (in one word--ouch!) Doug makes the point that process energy usage is often the largest controllable cost in most plants.

Doug shows energy flows for process manufacturers in different industries including chemicals, pulp and paper and oil refining. He also gives some typical percentages of the energy flow inputs and outputs. For example, a typical refinery's sources of energy include 1% purchased steam, 25% purchased fuel, 64% raw materials consumed as fuel and 10% purchased power. This energy is used in steam production and central power production in the power plant. In the process and offsites areas, the energy is mainly consumed in the process-fired equipment, direct fuel usage and electric motor drives. Energy not consumed in the process is exported as steam, fuel and power.

Applying better automation and APC can help improve efficiencies around individual equipment like boilers, heaters and kilns (links are to earlier posts where equipment efficiency stories have been chronicled.) Savings can also be achieved at a unit, multi-unit and site level by finding opportunities in optimization, waste heat recovery, and off-spec/waste minimization.

As the earlier percentages indicate, you may have a control loop heavily involved in your plant's energy usage. It may well be worth improving the measurement, control valve performance and loop control performance to reduce variability and energy consumption. Also, your process may have bypasses around production equipment that may be compensating for poor control through the equipment. Optimized control can eliminate the need for these bypasses.

The presentation is loaded with specific examples including stem systems, combustion control, heaters, distillation controls, plant utility systems, facility optimizers, boiler load allocation and site energy balances. Some examples like power boilers include return on investment (ROI) calculations that may assist you in your project justification efforts.

I wanted to highlight some key points Doug makes around heater optimization. If there is resistance in improving heater controls because the damper control is are not reliable, then he recommends adding positioners to the dampers. Bring the feedback to the control system and then analyze and fix any controller problems. If the next objection is on-line analyzers don't exist or are not maintainable, Doug notes that analyzers are cheaper and more reliable, especially mass flow meters. With today's higher fuel costs, these analyzers should be well justified.

There are likely many areas to look for energy savings. Doug recommends a disciplined approach to evaluation and analysis to prioritize the opportunities. Given the increasing costs of energy and the fact that this is often the largest controllable cost in a process manufacturing plant, it may make sense to establish a program around saving energy and apply focused efforts in prioritized projects to reduce overall energy consumption.

I received an email from a university student with a great question the other day. It prompted a great answer from Pete Sharpe, a Principal Advanced Automation Consultant. You may recall Pete from earlier posts on process optimization.

I've retained the anonymity of the person asking the question by editing the question:

I am doing my thesis on estimation of benefits by implementation of advanced control, I read your articles in this field and it help me so much, but I still have some questions, I would like to know if you could give me information about how to calculate the benefits to pour point, viscosity and Research Octane Number (RON). I will be grateful for your help.

Pete responded:

I was forwarded your request about calculating benefits. I've had some experience in this area. Are you estimating benefits for a blending process? If so, the opportunity is to reduce variability and approach the specifications closer using less of the more valuable components. So instead of making 87.5 RON on the average, you reduce it to 87.1. The value is the total blend rate times the difference in average octane times the octane barrel cost.

Anyhow, I'm attaching a paper that perhaps might help describe how these benefits are calculated.

I contacted the ISA and received permission to re-host this paper, Estimating Benefits from Advanced Control (Copyright © 1986 ISA. Reprinted by permission. All rights reserved.)

In the paper, the authors (Pete, P.L. Latour, and M.C. Delaney) apply statistical methods to estimate savings from dynamic control improvement and steady state optimization. At the end of the article, they run through a distillation column example calculating annual dollar savings by reducing process variability and thus allowing the column to operate more closely at its limits.

Whether you're a student or a project engineer, you might find the calculations in this "oldie but goodie" paper useful in trying to estimate and quantify the benefits for your project.

ModelingAndControl.com's Greg McMillan and Solutia's Mark Sowell will be presenting at the upcoming ISA 54th International Instrumentation Symposium. Their paper, Advances in pH Modeling and Control, describes the use of embedded simulation, coined "Virtual Plant" and model predictive control to improve the control of pH levels in a plant waste water treatment application.

The authors begin by describing the challenge of pH control:

The pH electrode offers by far the greatest sensitivity and rangeability of any industrial process measurement in terms of the measurement of concentration (hydrogen ions). To realize the full potential of this opportunity requires extraordinary performance of mixing equipment, control valves, reagent delivery systems, flow meters, control system design, and controller tuning.

The virtual plant is described:

A virtual plant can be used to sort out fact from fiction important for insuring performance and reducing capital and operating costs. The virtual plant consists of a download of the actual control system configurations and displays, embedded advanced control tools, and a dynamic process model running on personal computer...

The articles details the control strategy used:

We developed and prototyped model predictive controllers (MPC) to replace the fuzzy logic control system. MPC-1 adjusted the 1st stage pH set point to keep the second stage reagent valve at a minimum position for good response and reliability. MPC-2 trimmed the 2nd stage set point to keep the pH in the tank at an optimum pH.

The authors describe the interaction of the virtual plant with the real plant. They write:

In order to study and improve performance of the control system and the fidelity of the process model for actual process conditions, we put the virtual plant in a read-only mode online running real time. A simple interface module was configured that used object link[ing and] embedding for process control (OPC) to read indicated waste flows, controller set points, and controller modes from the actual plant.

If you are battling pH control in a waste water treatment application, you'll want to give this paper a read. You might also want to get your hands on one of Greg's books, Advanced pH Measurement and Control, if pH control is currently vexing you.

Update: Greg wrote me that the presentation went well and the room could have been bigger to hold all the folks interested in hearing about this topic. He has done a slight revision on page 1 to better tie in the results to the general situation with pH systems. This version is now posted on the original hyperlink above.

Continuous manufacturing processes have long benefited from the application of advanced process control (APC) in their processes to improve upon their regulatory control. Batch manufacturing processes have recently been able to take advantage of these technologies. I received an email the other from Lou Heavner, part of Emerson's Advanced Applied Technologies team. We've featured Lou's work here a few times in the past.

I'll summarize a few of these applications with the hopes that it might spark some ideas for application in your batch manufacturing process.

A manufacturer of sweeteners was having scheduling problems caused by the unpredictability of batch cycle times. End of batch could vary between six and twelve-plus hours. The operators could determine when end of batch was reached but not predict when this would occur. The APC consultants worked with this manufacturer to apply neural network technology as an inferential estimator to predict the end of batch time. The model can successfully predict the end of batch plus or minus ten minutes up to four hours before the completion of the batch. Scheduling downstream equipment is more manageable given these accurate predictions.

A second example Lou mentioned was again around batch cycle time, but in this case poor distillation control, which resulted in longer batches. Model Predictive Control was used in this pharmaceutical manufacturing process to control the batch distillation, specifically the reflux. Distillation time was reduced with the overall batch cycle time reduced by more than three hours per batch on average. The net effect of this improved control performance was a five-plus percent increase in production capacity. The quality of the product produced was also improved.

A third example is in a specialty chemical manufacturer's semi-continuous fluid bed hydrogenation reactor. In this process, cold solids are added to the top batch-wise based on level in the vertical reactor. Heated feedstock (gases) enters the bottom to provide the fluidizing medium and heat to drive the reaction. The reactor was a bottleneck, limited by temperature control and high temperature constraint. Adding model predictive control around the reactor provided more stable temperature control. The controller reduced temperature variability and allowed target to be moved closer to constraint limit with fewer high-temperature trips.

I thought these were great examples of advanced control technologies combined with people like Lou with process and APC application knowledge that are solving process problems and improving process efficiency. Perhaps these ideas will spark some ideas for improvement in your operations.

High energy costs continue to prompt process manufacturers to seek ways to increase their energy efficiency. A colleague pointed a great post to me, The Seven Steps to Successful Industrial Energy Management, on the Energy Pathfinder blog.

My take away was that the culture for becoming more energy efficient starts at the top and developing metrics, incentives, and disincentives to change organizational behavior are keys to success.

I thought I'd share this post with Bob Sabin, a consultant in Emerson's Industrial Energy Solutions organization. You may recall Bob from earlier posts.

Bob believes improving the operation of the Industrial Powerhouse can be a large factor in improving overall energy management at process manufacturing sites. The carbon footprint of the powerhouse can be reduced, the reliability and responsiveness of the operation can be increased, and the cost of energy can be reduced--all at the same time.

With this focus (and not to be out done by the seven steps), Bob offers his ten steps to successful Industrial Powerhouse improvement:

1. Obtain top management commitment to improving the carbon footprint, reliability, and cost of operation of the Powerhouse.
2. Benchmark current operations in terms of efficiency, reliability, cost, and emissions.
3. Survey current process equipment, control technology, and operating methods. Create a matrix of factors that are impacting or limiting operating performance.
4. Examine potential process equipment repairs and upgrades that could deliver benefit, rank these in terms of return for investment, and complete repairs and upgrades that will deliver good immediate benefit.
5. Focus on process parameter measurement devices and actuators. Especially for combustion air and fuel flows, ensure that repeatable measurement and control capability exists.
6. Implement full automatic control that is robust and reliable. Even the best operating crews cannot optimize Powerhouse performance every minute of the day for every day of the year.
7. Install optimized control functionality as appropriate to optimize efficiency, prioritize lowest cost fuels, load equipment based on cost, and make economic operating decisions automatically.
8. Change Standard Operating Procedures for the Powerhouse to ensure that process units are run in automatic using the optimized control functions. Make focus of operations identifying and troubleshooting process issues rather than manual process operating adjustments.
9. Regularly benchmark operation in terms of efficiency, reliability, cost, and emissions, repeat steps above when results are not satisfactory.
10. Investigate and consider re-powering the industrial site with lower cost fuels and/or technologies.

Bob and the Industrial Energy Solutions consultants have helped process manufacturers achieve ongoing savings from improved energy efficiency by putting these steps into practice. If your energy costs are higher than they could be, give these ten steps a try or contact the industrial energy team for help.

In spite of my best efforts to use persistent RSS search feeds in order not to miss any news about Emerson experts in action, here's one that got by me.

Mark Coughran, a consultant on Emerson's Advanced Applied Technology team, shared this control challenge question he answered with me. You may recall Mark from earlier posts.

The question he addressed appeared on the ChemicalProcess.com's Ask The Experts website. The question, Control pressure at discharge, was:

I have five pumps running parallel, transferring water. Due to pressure fluctuation at discharge, which depends on the flow requirements of the user, I am planning to install a pressure control valve at the pump discharge to keep the pump running at an optimum condition... What kind of valve is best for a 14-in discharge?

Mark notes that he's seen problems with butterfly valves used on large water lines, but that things have improved with better valve, actuator, positioner, and application software. Common sources of problems include wrong valve size, shape of butterfly disk, backlash in disk-to-shaft and shaft-to-actuator connections, poor valve positioner performance, and insufficient torque.

Control valve suppliers have addressed these issues in a number of ways. Examples include better valve sizing software, improved butterfly valve disk shapes, zero-backlash connections, valve positioners responding to 0.1% signal changes, and sizing software that predicts installed torque.

Mark points out that globe valves are typically too expensive for this application. Butterfly or segmented ball valves may be better suited if the supplier's test data for the valve + actuator + positioner shows suitability in similar applications.

Mark's final guidance concerns the control strategy. He recommends a controller tuning method that does not oscillate, but responds at the application's required speed, such as Lambda tuning. He advises:

If you need to control the five lines separately, there will be interaction and balancing concerns. The options range from individual PID controllers to a multivariable controller. All the options are easy to configure and tune in a modern DCS.

Like capacitors do for electrical circuits, vessel levels provide capacity that can absorb variability within the process. In many cases, a properly tuned level controller can make the variability of the vessel outflow can be much less than the variability vessel inflow. Of course, if it's not properly tuned, the variability can pass right through the vessel or even be amplified. In fact, an improperly tuned level controller can make the variability of the outflow higher than that of the inflow! Unfortunately, the latter cases are common and directly impact process efficiency and product quality.

I caught up with Emerson's James Beall, whom you may recall from earlier posts. James is a process control veteran with 27 years of experience including the last seven as a process control consultant. He's also chairman of the ISA 75.25 committee on control valve dynamic testing.

James stressed that there are often different objectives in tuning level controllers. What's common is to make sure this tuning is not creating variability on its own. Sometimes you want to hold the level very close to the setpoint at the expense of aggressively moving a manipulated variable. Other times, like in the case described here, you want to use the capacity of the level system to absorb variability in the process and very smoothly move the manipulated variable as little as necessary.

From James' experience, good level tuning techniques for absorbing process variability are not widely known. When the level controller for a vessel is properly tuned, variability can be reduced by a ratio of a 20:1 or more depending of the nature of the variability and process constraints. For example, a vessel with an input flow which varies plus or minus 20% of the inflow can have the variability reduced to plus or minus 1% of outflow.

Most level processes have an integrating process response. This means the level is an integration (or accumulation) of the difference between the inflows and outflows of the vessel. Absorbing process variability requires that the level control be tuned as slow as possible but still fast enough to hold the level within the allowable deviation for the maximum expected load change.

For an integrating process, Lambda is the period of time from the start of the step change in load until the process variable has stopped changing as a result of the level controller action. The level controller tuning parameters can be calculated to achieve a specific Lambda.

James notes that the required Lambda for maximum variability reduction is a function of the allowable level variation, the product of the integrating process gain and the maximum expected load disturbance. He uses this technique on a number of applications including feed tanks, distillation column bases, intermediate tanks, and reflux accumulators.

He mentioned that in some cases, the level actually needs to be controlled very close to the setpoint. Examples of this case include boiler drum levels, refrigeration evaporator levels, reactor levels, etc. He also has techniques for calculating the level controller tuning for "tight level control" which we'll explore in future posts.

James has worked with process manufacturers where reoccurring annual benefits from proper loop tuning have yielded savings from several hundred thousand dollars to several million dollars.

If you have the time and inclination to learn more about these concepts, James recommends three courses offered by Emerson Educational Services: Process Dynamics, Control and Tuning Fundamentals; Process Analysis and Minimizing Variability; Modern Loop Tuning.

If you don't have the time or inclination for these courses but need help in addressing process variability issues like level control, you'll want to connect with James and the variability management team.

Imagine that your power plant is about to have a scheduled outage. As the unit is ramped down and feed water control is taken over by the by-pass valves, you discover that the control valves refuse to close upon receiving orders from the level control system to do so. This is now the last straw for the operators who also have been fighting stability problems with these valves over the past several years. What do you do?

Well, if you know Emerson variability consultant, Eric Ascoli, you contact him. You may recall Eric from a prior post on stability problems at a sugar mill. He shared this story with me.

Instead of continuing with the shutdown, the station had to run at 20% power production for 12 hours costing them hundreds of thousands of dollars while the situation was diagnosed and corrected. A manual unit trip was not an option. The problem was aging pneumatic instrumentation that had locked up and blocked the valve positioner's operation.

Eric worked with a pneumatic specialist from Proconex, the local business partner for the power station. Their findings were that the operation of the pneumatic trip valve was not completely understood and its adjustment was slightly off. Also, the combined level control valves had a very large variation in installed valve gain and the unbalanced and aggressive controller tuning caused the instability the operators had been experiencing.

The challenge was to find a solution that would remove completely the possibility of such an event from happening again. It involved a short-term fix (servicing and adjustment of the pneumatics and modification of the characterizing functions for the valves) in preparation for the imminent scheduled power up. Additionally, Eric corrected the level controller tuning by using Lambda tuning after he analyzed and evaluated the process gain and empirically defined other important process parameters.

Their longer-term recommendations were to install digital valve positioners to replace the aging pneumatic ones. The same split-range control strategy would be maintained, but the valve performance would be improved through better positioning accuracy and dynamic behavior. The installation would be simpler and less prone to maintenance issues because I/P (current to pneumatic) converters could be removed. An even longer-term solution would be to replace the two split-ranged valves with one single start-up control valve to eliminate any crossover interactions.

I received a call recently from an automation engineer facing an upcoming planned shutdown or "turnaround" in industry parlance. Actually "controlled chaos" may be a better moniker since a tremendous amount of maintenance activity needs to be squeezed into a short period. This engineer had come across one of my earlier posts on this topic and was looking for help in analyzing the control performance of the process control loops prior to the turnaround. This analysis helps identify control issues that can be addressed during the turnaround.

Time is money when the plant is not in production, so this time must be carefully planned and methodically executed to get all the maintenance activities done without schedule delays. Large refineries, petrochemical plants and other continuous processes will run for years between turnarounds. This means there are often new people working each one, which adds to the challenge.

Chris Forland, whom you may recall from earlier posts, reminded me that planning could extend beyond control loop performance to include a plan for the control valves and other plant assets.

Emerson's Asset Optimization team has developed a smart turnaround program, which puts a primary focus on control valves but also includes instruments, rotating machinery, and power distribution assets. The program includes a pre-turnaround planning and analysis phase, turnaround execution phase, post-turnaround review phase, and an ongoing maintenance phase.

The post-turnaround review phase captures the results versus the plan and documents the baseline and best practices to serve as "institutional memory" for the next time a turnaround is scheduled and new personnel are involved. Documentation to support on-going maintenance after the turnaround is also reviewed and submitted.

Chris recommended that planning should begin six to twelve months in advance since the flexibility to make changes to the plan diminishes as the turnaround date approaches. This investment in pre-turnaround planning and equipment analysis will be offset by avoidance of delays during the turnaround, reduced turnaround cost, and more efficient operations post-turnaround from better performing assets.

Turnaround specialists review diagnostics from smart instruments based on Foundation fieldbus and HART digital communications to determine which control valves actually need to be pulled for service. Portable diagnostic equipment can be brought in if smart instruments are not in place. Chris notes that typically only 70% of these valves need to be pulled and serviced.

This program ranges from a cost reduction only focus where units are already performing optimally, to a production performance improvement level, to a level of sustaining high performance through training of plant operations and maintenance staff to more effectively use diagnostics from smart instruments.

If your plant conducts turnarounds from time to time and if are going to the Emerson Exchange next month in Dallas, make sure to check out the sessions on smart turnarounds.

Prolific author (examples here, here, and here to name a few) and ModelingAndControl.com blogger, Greg McMillan, continues to share his process control wisdom and expertise with the world.

This week he announced another freely available ebook, Continuous Control Techniques for Distributed Control Systems. This is the second in a series of books where the copyright has been returned to Greg after a time being held by the publisher.

Instead of burying these works in a box somewhere, Greg has chosen to make these freely available to help our current and future generations of automation and control engineers learn the craft as they search the internet and come upon these on-line works.

As he mentions in his post, this book follows in the footsteps of the first eBook, A Funny Thing Happened on the Way to the Control Room. He also mentions another eBook is planned, Biochemical Measurement and Control.

If you are a control engineer or student of this profession you should be subscribed to the ModelingAndControl.com RSS feed to learn from one the members of the process automation hall of fame!

A great question came in on the Operating Fired Heaters More Efficiently and Reliably blog post:

Jim I work with natural draft heaters on a daily basis and have initiated several efficiency tests with improved burner internals. I am looking for an opportunity to optimize dual firetube treater by first off improving the combustion efficiency to 80% in each tube and then staggering the temperature controls so that one tube runs 90 to 100% of the time and the other tube only fire during high load requirements.

I sent the comment around our advanced automation consultants for any comments that they might have and I received a great reply from Lou Heavner whom you may recall from earlier posts. Lou describes how to approach optimizing these heaters:

Heater efficiency is calculated using heat loss or input/output method. Input/Output method is difficult because you have to account for lags and delays between fuel firing rate changes and the measurement of process heat absorption changes and in the specific case where there is incomplete phase change on the process side (e.g. partial vaporization) you cannot easily solve with reasonable instrumentation. The heat loss method measures heat loss in the flue gas and assumes any other losses are negligible and constant. If not, they need to be measured and added as well.

Heat loss requires knowledge of the supply air (and fuel) temperatures and the flue gas exhaust temperature as well as the composition of the fuel and flue gas, just like with a boiler. In perfect combustion, there would be no unburned fuel in the flue gas and no sensible heat losses. But due to practical considerations, there are sensible heat losses and to calculate them, you need to know the delta T between the exhaust and ambient and how much excess oxygen remains in the exhaust. Efficiency calculations made using this technique can be pretty accurate in a natural draft heater, but if there is air leakage after the combustion zone, tramp air will show up as lower efficiency due to increased O2. And there is usually an optimum cost operation where the trade-off between sensible heat losses and unburned fuel losses require some level of unburned or incompletely burned fuel leaving in the flue.

When you are ready to control, the goal is to minimize excess O2 while not allowing excessive fuel to go unconsumed. CO analyzers are often used to detect incompletely burned fuel and the goal is usually to keep it below 150 ppm or some lower target. O2 is controlled to stay as low as possible without exceeding the CO limit, which is usually 2% O2 or less for the fluegas.

You can do this with simple feedback control, but feed forward control can help do better. Information on fuel quality, if it varies, and process side temperatures and flows (the heater load demand) can be used to adjust the fuel and air for combustion to meet the heating demand at maximum efficiency. Fuel and air cross limits are often used to maintain fuel and air ratio without getting into a fuel rich condition in the firebox during load changes. But airflow is usually difficult to measure. Therefore, it is often inferred from damper position.

When evaluating an application, we would want to know what instrumentation already exists and what the process variability looks like. What efficiency are they currently obtaining? Then we would look at the control valves and any other contributors to variability to see if they warrant repair or replacement. We would similarly evaluate the instrumentation and analyzers to see if they need anything there.

Then we could evaluate the control strategy and performance and recommend reconfiguration or tuning as appropriate, which may include advanced process control (APC). The person evaluating the controls would have to weigh the cost against the improvement from better loop tuning, valve repair/replacement, CO analyzer, etc. to come up with the best solution. Dampers are often the weak link in fine control of a natural draft heater.

As my colleague Doug Simmers in Emerson's Rosemount Analytical business noted, "The commenter is probably correct with the strategy to fire one heater full out, and bring the second unit on only when needed. Running at full fire develops the best turbulence for fuel/air mixing, and the excess O2 can be kept lower." This is a load allocation problem when two heaters are firing simultaneously. If we can model heater efficiency for each heater as a function of load, then we could optimize the load allocation across both heaters when both must be fired. Actual testing would identify the models, uncover the best strategy, and verify or disprove this assumption."

He may also be interested in the efficiency calculator, developed by Doug's team.

Join the conversation and add a comment if you have experience to share.

Recently, a North American chemical manufacturer was having problems with their boilers tripping during startup and shutdown sequences. This problem was caused by a wide variation in the process' demand for steam. This situation caused lost production, which affected the overall plant efficiency.

Jim Dunbar, an Emerson variability management consultant was called in to provide emergency tuning services, to set the loops on the boilers to be able to handle the range in steam demand.

Jim's mission was to work with 2 boilers and about 10 loops controlling these boilers to resolve the situation.

The problem began when the plant installed a new steam-driven compressor that required a minimum steam pressure for operation. The team installed a backpressure controller to satisfy the steam requirements of the compressor. However, the boiler still had to ramp up very quickly to maintain the plant steam header pressure on process unit shutdowns. When the boiler firing-rate was increased too rapidly, the boiler would trip due to low feedwater level.

Jim worked with the plant staff to perform open loop bump tests on the feedwater flow and drum-level control loops. This data was collected in the PI historian and analyzed with the EnTech Tuner. Lambda tuning constants were calculated resulting in much faster and stable drum level control. Next, the boiler master controls were tuned to coordinate the speed of response with the level control. It was important that the firing response was fast enough to meet the requirements of the steam header, but not so fast as to cause an unrecoverable upset to the drum level resulting in a boiler trip.

Since his visit to the site, the manufacturer has not had a boiler trip in over four weeks, despite numerous simultaneous unit shutdowns.

Beyond the improved reliability of the process, Jim provided the operations staff some key insights on what to watch for if instability creeps back into the process.

Last October, I featured one of Emerson's advanced automation service consultants, Lou Heavner, and how he worked with Lukoil to create virtual sensors based on neural networks.

Their efforts were told in more detail in the March 2007 issue of InTech magazine. The article, entitled, Crude gets smart, described the Russian refiner's challenge to keep their refined products within specification. They had been relying on lab samples that came back from the lab to the operators only once or twice a day.

To get feedback on product quality and composition more frequently, Lou and the team used neural network blocks in their DeltaV system's controllers to create property estimators. As the article states:

The goal of a property estimator is to provide an accurate gauge of product quality, especially after lab results have become stale, which is most of the time. Property estimators are not intended to eliminate lab analyses, although the frequency of analyses may lessen once estimators are proven. Even though estimators may not be as accurate as lab analyses, they can be worthwhile calculated variables to help engineering and operations personnel monitor, troubleshoot, or understand and control the process.

The article describes the steps the team took to collect the data to train the neural network models. It offers guidance for those looking to implement property estimators. Some examples of their recommendations include:

• The time stamp should reflect the time of data extraction from the process--not when it was scheduled for sampling, or when the lab technician performed the analysis, or when they reported the lab results.
• Avoid filtering or manipulating the process data. Raw snapshot data usually makes for the best models.
• If the process does not vary much, the model will not be reliable if the process wanders into a range with no collected data... the model will be changed to "Uncertain" and the operator can be alerted.

The team believes they may have one of the world's largest installations in terms of neural network models. Currently operating models include ones measuring boiling points, flash points and viscosity on the pre-flash, atmospheric, and vacuum towers.

If operators at your plant are waiting on lab information to make quality adjustments to the process, you may have a business case for creating property estimators to augment the lab sampling process.

John Egnew, a training consultant and instructor in Emerson's Educational Services has posted another tip in his series of looptips. John's looptip #12 is entitled Don't Throw Away a Good Thing.

In it, he references how a positioner used on a control valve in a fast-acting loop may actually make the loop more unstable or difficult to control. The likely culprit may be too high of a loop gain. An example of this type of loop might be a fast fluid flow application.

If this is the case, the solution is having the travel feedback signal from the positioner be the inner loop of a cascaded loop. The inner loop of cascade control must be faster than the outer loop.

He also offers specific recommendations whether your loop is running under electronic or pneumatic control.

I hope these tips along with some of the wisdom conveyed by Terry Blevins and Greg McMillan over on the ModelingAndControl.com blog help you tackle some of these situations which can impact the performance of your process.

Let's close this week by pointing to a new Advanced Control Survey at the ARC Advisory Group site. The actual survey is here.

Their purpose?

The purpose of this survey is to develop an understanding of how process manufacturers around the world are using advanced process control (APC) to create a sustainable competitive advantage.

We discuss what Emerson's experts do in optimizing processes with APC technologies from time to time, so I'm keenly interested in the results of this survey.

You're eligible if you're a process manufacturer and you work with process control. The promise is 20 minutes of your time and:

By taking this survey, you will gain insight into how users are looking to extend APC applications and related infrastructure within their organization. Those who complete the survey will receive a FREE copy of the results.

I didn't see a cutoff date, but if you meet their criteria and have 20 minutes, give it a go.

Update: Right now, the survey is expected to be open until June 15.

Emerson Educational Services' John Egnew has posted another LoopTip, entitled Is Your Process a Real Character? In it, he explores loops that are in constant need of retuning.

The likely situation is that the process that the loop controls is non-linear.

The solution is either to take advantage on gain scheduling, where the gain of the loop is changed based on which operating region the loop is trying to control. This solution only works for the automation systems which support gain scheduling or built-in adaptive modeling.

John notes that you can select control valves with non-linear characteristics which offset the non-linearity of the process. He describes it:

The control valve characteristic is used to compensate for the process gain to achieve an approximate installed linear process.

He does note it is difficult to exactly get the non-linearities to cancel one another out, but that at least you can come close in approximating a linear constant gain process and be in better shape than before you made the change.

This week's DeltaV News RSS Feed announced the DeltaV InSight integrated control performance software package. The news release described the method for improved control performance:

DeltaV InSight automatically learns users' processes with embedded learning algorithms running at the controller level and develops process models based on day-to-day operations. These models allow users to identify operational benchmarks, diagnose problems and calculate optimum loop tuning across the entire control system.

Upon process changes like an operator setpoint change or a sequential logic-induced change which causes the process to move to a new operating state, the software learns the dynamics of the process from this change and provides recommendations on new tuning calculations. I discussed his capability in detail in an earlier post with DeltaV Advanced Control product manager, John Caldwell.

Over the years, I've become a fan of blogger and new Microsoft employee, Jon Udell and his use of screencasts. These short screen-captured videos really save thousands of words and help quickly demonstrate something he is discussing.

I spoke with John Caldwell and he agreed to give it a go and do a quick screencast of DeltaV InSight.

The screencast begins with a one-slide overview of DeltaV InSight followed by a demonstration of the software. I hope it conveys in its 3:22 second run-time a sample of this process of recognizing, learning, recommending, and implementing the change. There is also a product data sheet and whitepaper now available presenting additional capabilities.

John adds that the development team worked closely with several process manufacturers in developing and testing this functionality. We developed a video from one of the manufacturers, Lubrizol, last fall to document some of the initial results they saw.

In a recent Pharmaceutical Processing magazine article, PAT Searches for its Identity, author Bikash Chatterjee discusses the seemingly slow pace of Process Analytical Technology (PAT) implementations. The article states:

What the FDA has provided is a bold chance for our industry--long mired in historical inefficiencies and product failure--to reinvent and improve existing processes for superior cycle-time, consistency and yield.

Given the change in regulatory climate the article questions why we haven't seen a glut of PAT applications to help achieve better operational results. The author points to challenges in the details to implement. Also the traditional emphasis on product and compliance orientation needs to shift as the article states:

...toward an understanding of critical processes to achieve the significant PAT benefits that have worked so well in other sectors.

Given the complexity of this undertaking the author suggests going forward with an approach like Six Sigma as an operational excellence project management framework.

I caught up with Michalle Adkins, a consultant in Emerson's Life Sciences Industry Center, whom you may recall from an earlier post on five strategies for mitigating project risk. She agrees with the author that a PAT initiative should be managed as part of an overall Operational Excellence program. This is because more structure and process can be provided to the initiative.

Michalle believes that by using the Six Sigma methodology, the right tools can be applied at the right time for evaluating, managing, and implementing PAT projects. The Six Sigma structure of define, measure, analyze, improve, control provides the structure for managing the PAT initiative.

It's interesting to note that some of the same tools in the Six Sigma toolbox are already inherently part of PAT such as design of experiments (DOE), statistical process analysis, and methods development. These are all very much related in terms of the types of statistical tools that are used.

Given that the PAT guidelines are still relatively new, pharmaceutical and biotech manufacturers are recognizing that the proven Six Sigma tools along with the analytical tools already used for methods development can help organize the PAT process and move these initiatives forward. It will be interesting to see how these PAT implementations begin to accelerate in the coming years as structured methodologies are applied.

I came across the following nine control fundamentals according to Mark Coughran, a consultant on Emerson's Advanced Applied Technology team. These are based up his years of experience working with process manufacturers to optimize their performance. You may recall Mark from earlier posts on planning plant turnarounds and turbomachinery pressure control.

His fundamentals include:

1. Make the process as linear as possible
2. Minimize dead time
3. Choose the PID controller to compensate for the process
4. Avoid resonance or amplification of disturbances
5. Use process capacity to absorb variability
6. Decouple the interactions by tuning if possible
7. Help the PID feedback controller with control strategy
8. Cascade, ratio, feedforward; a.k.a. advanced regulatory controls
9. Use Fuzzy, Neural, MPC if the above are insufficient

Although there is a lot behind each one, it's a way to think through the process of solving control performance issues.

Mark cited an example of a Good Automated Manufacturing Practice (GAMP) facility with eight reactors. All of the controls cycled strongly. As a result, steam was being wasted on the up cycle, and cooling water was being wasted on the down cycle. Thinking through the control fundamentals above, Mark recommended changes in the master controller parameters including tuning, jacket controller tuning, split range strategy, and control valve calibrations.

By implementing these changes on three of the eight reactors steam usage for the facility was reduced 10% reducing the plant's energy bills.

Pharmaceutical Technology Europe has a recent article entitled, Artificial intelligence the key to process understanding. It discusses the opportunity to enhance the FDA's Process Analytical Technologies (PAT) initiative using artificial intelligence based tools like neural networks, fuzzy logic and genetic algorithms. I shared this article with Greg McMillan who has been quite immersed with advanced control as it applies to bioprocesses.

I received this response which I'll share in total (I've inserted some context-sensitive hyperlinks to his work on Process Control Insights):

There are opportunities to improve plant performance in the front end of the process where most of the product qualities are set by the use of online process models, batch analytics, and Model Predictive Control (MPC). Online process models based on first principals offer a significant source of knowledge discovery for both the process and the control system. The models are part of a virtual plant that enables virtual experimentation for the exploration of "what if scenarios".

This is important for the next steps of implementing online batch analytics and MPC. Since fermentation batches take days to weeks to complete and the cost of wasted batches is considerable, the virtual plant can provide data on various degrees of adverse operating conditions that would be infeasible to obtain from the actual plant in terms of time and cost.

The virtual plant facilitates the development of techniques for the proper unfolding and alignment of batch data and more advanced analysis techniques such as super model based Principal Component Analysis. Neural networks can be employed to provide reaction rates when information on the kinetics is insufficient.

Fuzzy logic rules can be formularized and tested for a wide variety of scenarios. Inferential measurements can be developed for viable mass growth rates and product formation rates to fill in the blanks between lab measurements for MPC applications to improve batch consistency and yield and to reduce batch cycle time.

In summary, the virtual plant offers a synergistic environment for the application of online batch analytics, artificial intelligence, and advanced control. These opportunities and others are discussed in the book New Directions in Bioprocess Modeling and Control published and in the lectures on the Process Control Insights website.

Do you ever feel that pressure when things just aren't right? Things like increasing production costs, growing raw material and/or finished product inventories, inconsistent quality and inflexible production to meet changing customer needs. According to John Dolenc, a principal consulting engineer for Emerson's Advanced Applied Technology team, these are potential business drivers to consider modernizing your process automation.

Other potential drivers include unreliable operations caused by false trips and excessive plant alarms, poor-to-nonexistent production data, time wasting manual data entry and checking, and time consuming regulatory compliance and documentation. Each of these drivers has a cost associated with it that can be used to develop a business case for improvement.

John helps process manufacturers understand and quantify these opportunities for improvement in Process Automation Feasibility studies. The study begins with gathering the background information found in process flow diagrams, P&IDs, operating procedures, operator log sheets, plant history data, production costs and trends, quality reports, and current control strategies.

Usually a team forms with members from plant management, plant engineering, operations, maintenance, quality assurance, and even corporate engineering and management depending on the level of potential improvement. John and other advanced applied technology consultants bring expertise in production processes, plant operations, and the impact control strategies have on the process to help develop an improvement plan. They are experienced in providing a methodology based on past experiences and bring an outside perspective to facilitate discussion and have the freedom to challenge the rational behind past practices to get at the underlying issues.

The methodology examines the process unit performance first from a financial perspective. Key performance indicators (KPIs) are identified and the performance versus these KPIs is analyzed. Base line performance is established, potential improvements are identified, and financial gains are calculated. An automation plan to achieve the financial benefits is developed based on examining the production process; looking at process constraints, process disturbances, and limitations in equipment or other areas of the operation.

The cost to implement the automation plan is estimated and a financial analysis is done to determine if the projected benefits justify an automation project. For smaller units this process can take four weeks to perform the feasibility study, while larger units or plant-wide studies may take several months.

The real fun happens when projects get funded and quantified improvements get made. It goes a long way to relieve that pressure!

I caught a sneak preview of draft article that ModelingAndControl.com's Terry Blevins who collaborated with James Beall whom you may recall from earlier posts.

The draft explores the initial steps Pharmaceutical and Biotech manufacturers should consider when preparing to implement the U.S. Food and Drug Administration's Process Analytical Technology (PAT) initiative. For those unfamiliar with the PAT guidelines, they were established to encourage innovation in development and implementation of manufacturing processes to improve product quality. The existing regulation designed to achieve quality through rigorous design and documentation actually served to discourage improvements due to the time-consuming nature of the revalidation of any changes.

Terry and James offer some guidance on some initial steps that Life Sciences manufacturers can take. Since most of their manufacturing processes are batch-based, it can be trickier to apply some of the advanced process control technologies more often found in continuous processes found in the chemical, petrochemical, and oil and gas industries. They recommend starting by looking at ongoing performance monitoring. This software has typically layered on top of the automation systems but has begun to become embedded in the automation system. DeltaV Insight is a good example of this type of performance monitoring software embedded in the DeltaV system. This performance monitoring can be keyed to the phases within the running batch to account for the changing process conditions. The dynamics of the process are learned as changes in the process are made.

Terry points out that these performance monitoring tools can help manufacturers spot issues like excessive process variability which can have direct impact on product quality. Other conditions this software can help detect include control-limited conditions, bad/unreliable data coming from intelligent field devices, and control loops operating in modes other than those intended. All of these conditions can contribute to quality issues in the final product.

He notes that the ability of intelligent field devices to provide status of the goodness of the data is a key part of performance monitoring so that the control strategies, history collection, and analytical tools have a clear picture of what is really happening in the process.

I look forward to seeing the finished article!

Congratulations to Gordon McFarland, senior power plant performance analyst with Emerson's Power & Water Solutions division for receiving the ISA's Standards & Practices Award. The award recognizes Gordon for his leadership in the initial development of fossil power plant standards, and for 25 years of continuous support and direction of those standards.

I caught up with Gordon who has 37 plus years in the power industry helping power producers get the most out of their control systems, including the Ovation system and non-Emerson systems. He applies this expertise as a primary technical lead in Premier Services performance improvement assessments. These assessments typically include a unit walk-down, plant personnel interviews, unit performance data collection, observation of unit operation, analysis of data and information collected, presentation of the results, and a final performance improvement assessment report. The team documents the actual unit control performance for deviation from the set points, control overshoots on ramping, ramp rates, unit net heat rate, forced outages and load de-rates directly and indirectly related to controls, and other parameters that may be important to overall performance.

Since 2000, Gordon has performed assessments on over 50 units, including drum type units, Once-Thru units, coal-fired and gas-fired units. On several of these, the performance was benchmarked by conducting "Before" and "After" performance tests to validate performance improvements on drum units, both coal-fired and gas-fired units, and on combined cycle units.

The goal of these assessments is to give power producers a roadmap to follow to achieve the possible unit performance improvements from improved control of the unit. These recommendations typically include field devices, control systems, operator interfaces, information and alarm management, and control room layout.

One thing Gordon and the team see almost every time in their evaluations is the need to have the basic regulatory control functions covered. This include single element and three element feed water control, steam temperature control, combustion control and unit front end control. The ISA SP 77 Fossil Fuel Power Plant Standards series covers the minimum recommended controls for these functions and they are a great starting point for good power plant control.

By applying his 37+ years of experience, Gordon has helped power producers in analyzing and improving their control performance and operating costs.

I saw an email about a success at a northern U.S. paper maker that set a new production record. To whom did they attribute this success? Since this is a blog about the experts around Emerson Process Management, you might guess the answer. And you'd be correct. They attributed their new production record to the work of our Control Performance team and their process and control study process.

I caught up with Andrew Waite, a principal process control consultant on the Control Performance team. Andrew began the study by using the EnTech toolkit which collects data from a variety of sources including pneumatic controllers, 4-20mA analog values, and can import digital data from smart field devices and digital automation systems using the OSIsoft PI data historian. The toolkit performs analysis and tuning recommendations based upon the data it collects.

Andrew noted that he uncovered all of the typical problems: tuning, control strategy issues, control valve problems, and process design limitations. The mill's maintenance department went to work fixing the control valve issues while Andrew provided tuning recommendations and improvements that could be made to the existing control strategies.

The mill attributed the increased production to taking care of the basics and having a fresh set of eyes come in to audit the existing performance. Not too bad for a couple of weeks work.

In an earlier post about fired heater efficiency and reliability, I had spoken with Emerson operations consultant, Chris Forland, on the opportunities for refiners to optimize this energy intensive unit.

Working with engineers in the Rosemount Analytical Gas division, Chris has developed a spreadsheet with fired heater efficiency economic calculations which allows refiners to get a rough estimate of the potential value in applying efficiency solutions like the SmartProcess Heater Optimizer.

You can enter data in the cells with blue text for each fired heater in your plant to get a quick assessment. Chris has filled in typical values from a cross section of refineries in case you don't have exact data. This will let you get a feeling for the overall improvement opportunity and if there is enough return on investment to warrant a closer look.

If you have fired heater units in your manufacturing process, give this calculator a try and let us know what you think by adding a comment or contacting us.

If you're a process engineer responsible for keeping the process running smoothly, you know how difficult it can be to analyze and optimize the performance of your loops on an ongoing basis.

Over the years, applications were included in Emerson's DeltaV system to inspect loop variability and tune the PID and fuzzy logic loops. The DeltaV technologists have been working on ways to further simplify these applications and have them operate more easily than they do today.

The result is DeltaV InSight, which will be available in the next release of the DeltaV software. The software is currently scheduled for release before the end of this calendar year. Control magazine's Dan Hebert wrote a nice review of DeltaV Insight which was in the October issue, in an article, User-Friendly Advanced Control. Dan spoke with DeltaV advanced control product manager John Caldwell, about what technologies and capabilities come with DeltaV InSight.

John boils down what DeltaV InSight does for process engineers as improving process control by monitoring control performance, identifying and diagnosing problem loops, recommending tuning and maintenance improvements, and continuously adapting to changing process conditions.

The gee-whiz technology part consists of learning algorithms that continuously identify dynamic process models based on normal day-to-day operations. The learning algorithms run down in the controller and update the models each time there is a change to the process. By constantly updating these models to changing process conditions, InSight can provide adaptive tuning to keep your loops running smoothly with minimal tuning effort.

DeltaV InSight also reduces start-up costs and ongoing maintenance by automatically configuring your performance monitoring and tuning applications based on your current DeltaV configuration. Whenever a control loop is added, deleted or changed, InSight will automatically recognize the change and update the InSight configuration. By reducing the on-going maintenance requirement, InSight has overcome a significant barrier that process engineers have had in the past with other layered software applications.

The software identifies abnormal control conditions such as wrong control mode, limited output, and high variability. It also identifies malfunctioning devices that may cause control problems and points to the problem loops that need retuning. Although some applications could do this in the past, the automatic, ongoing learning of the process dynamics really helps the software point the operations and maintenance staff to the areas requiring most attention. It does a lot more but you'll need to keep an eye on or subscribe to the DeltaV News RSS feed and look for the DeltaV InSight product data sheet to be posted in the coming weeks.

Like the recently announced Smart Wireless initiative which went through extensive testing with BP, DeltaV InSight also was put through its paces with lubricant additive maker Lubrizol. A video with their experiences was recently announced on the EasyDeltaV.com website. Check it out.

View the Video: 100K | 300K

Emerson's Lou Heavner, a consultant on the Advanced Automation Services team, recently co-authored a paper, Using Neural Network Technology for Virtual Sensing in Crude Refining Units, at the recent ISA EXPO 2006. Lou worked with engineers from Russian oil refiner, Lukoil.

Always a great presenter, Lou not only shared his expertise on this project, but the fact he homebrews beer. I imagine this keeps his control application skills honed!

The paper describes the need to improve quality of refined products to improve upon the current process of taking lab samples once or twice a day. The operators would make changes to the process based upon the lab readings. They tried to control key temperatures and other process variables on the pre-flash, atmospheric, and vacuum columns by manipulating flows including reflux, furnace fuel, pump-arounds and product draws.

To make these quality adjustments more in real-time, the refinery engineers want to use something other than costly and maintenance-prone on-line analyzers. They decided to use neural network technology to build real-time inferential property estimators which could run inside the refinery's existing DeltaV controllers.

Lou work with the Lukoil engineers to build ten artificial neural networks measuring gasoline, kerosene, diesel, VGO, and residue on the pre-flash, atmospheric, and vacuum towers. They believe this application of virtual sensors to be one of the world's largest on a single crude unit.

The real work comes in collecting the data needed to train the neural networks. They needed around 100 lab samples for each model and the continuous historical data for process variables over this sample period. The DeltaV Neural software helped automatically perform the data collection and model training need to build and prove the neural networks. Up to 20 process variables were collected as inputs in training each of the ten neural networks. Any abnormal operating conditions were identifies to exclude the data from this time period from the model. Any of the variables that had little or no effect on the model outputs were eliminated.

The largest challenge in the data collection effort was in the lab data. It had to be accurate in terms of precise time of taken sample and the proper analysis of the sample. The quality of the neural networks is directly impacted by the accuracy of the samples. Another important factor is to make sure the process data is not filtered or manipulated, but instead a raw snapshot.

The resulting inferential sensors predicted what the lab results showed within a few degrees. The ones estimating lighter refined products were more accurate. The engineers have not closed the loop to run the control strategies based on these readings, but they do present the information to the operators to make adjustments more frequently then they could with samples coming only once or twice a day.

I had the chance to catch up with James Beall while he was here in Austin for meetings. You may remember James from an earlier post of a refiner in dire need of process stabilization assistance.

James will be presenting a paper at the rapidly approaching Emerson Exchange meeting in Nashville. He'll be presenting a short course and two workshops, including one entitled, Coordinated Loop Tuning Saves Energy on Distillation Column. He is co-presenting this with two engineers from the Monsanto Corporation.

The project they profile is on one of their distillation columns. The team started by diagnosing the current performance of the column. They discovered by looking at uncompressed history data that there were problems with several key valves which had excessive deadbands. This caused limit cycling on the level and manipulated flow into the column. This was fixed by installing Fieldvue DVCs (digital valve controllers.)

After getting better performance from the valves from the digital valve controllers, the team attacked the issue of the interactions between the loops on the distillation column. The approach taken was to set a loop tuning sequence and began manual output step tests to detect problems and measure process dynamics.

James used Emerson's Entech Toolkit which performed high-speed, uncompressed data collection and provides an analysis of non-linearities. It also analyzed the process dynamics, calculated tuning, and performed simulations of loop response. Prior to this work each loop had been tuned independently and therefore the process dynamics were not coordinated between these loops.

By manipulating the closed loop time constant, the team was able to coordinate the speed of the loops and determine the critical tuning sequence to account for the process dynamics and loop interactions. The results from this optimized tuning were a 27% reduction in reflux and 5% energy savings from lower steam usage.

At the recent conference, Implementing Manufacturing Execution Systems in the Pharmaceutical and Biotech Industry, Emerson Senior Vice President John Gardner presented with DMI International's Bob Schiros a paper on Integrated Recipe Authoring on the first day of the conference. John is the general manager for the Life Sciences, Food and Beverage, Pulp & Paper, Energy, Metals and Mining industry organizations.

The thrust of their presentation is that manufacturers need to integrate their existing "functional islands". Today people want to get information easier, but that's just the tip of the iceberg. Integrating and simplifying the management of documents, personnel qualifications, equipment and material, work activity, automation, various plant floor systems, etc. is where the real operational benefits occur.

John stresses the place to begin is to analyze the causes of deviations in these areas. Eliminating these deviations provides the potential operational improvements at the heart of your business case for change. These areas of opportunities should be broken into manageable phases.

Gaining executive sponsorship for the changes is critical since people and processes are likely impacted, and organizational inertia tends to resist changes.

For pharmaceutical and biotech manufacturers, the opportunity comes in reducing the cost of goods manufactured. John stresses the place to begin is to analyze your current functional operations and the causes of deviations in these areas. This will lead to better inventory and yield management, lower regulatory compliance costs, and reduced product release times. John stresses the place to begin is to analyze your current functional operations and the causes of deviations in these areas. Focusing on eliminating these deviations is the most immediate potential improvement and that's at the heart of your business case for change. These areas should then be broken into manageable phases.

John believes the key is to start with the low hanging fruit which is to have a structured integrated recipe authoring process. The process starts by disassembling the recipe into its components: personnel, materials, equipment, data, and documents. These components are optimized and a database of reusable objects is created. Now the recipe can be reassembled with the optimized components and made available for execution of the batch with its associated electronic batch record.

The Emerson Life Sciences industry experts use the manufacturing execution system (MES) software product, Compliance Suite, as a platform to help manufacturers achieve this structured approach.

The presentation highlights measurable results which have been achieved including 40-70% reduction in batch record complexity, 30-50% reduction in product release cycle times, 20-40% reduction in documentation authoring and approval cycle times, and up to 40% reduction in errors, omissions and deviations of operational data.

Trying to manage energy consumption and steam usage in a manufacturing process can be a tricky undertaking. The need to do it is ever increasing with higher fuel costs. A recent AEI Environment Policy Outlook study shows the gas and oil price trends over the past 25 years.

The variables operations staff typically must juggle include process load requirements, multiple fuel types, boiler/turbine availability and efficiency levels, and electric buy/sell prices to name a few. Of course, steady state operations are rarely possible because product mix and volumes being produced are normally in flux.

You may recall Bob Sabin, a consultant in Emerson's Industrial Energy Solutions organization, from an earlier post on Chemical Recovery Boilers. Bob discussed how the team of energy consultants works with process manufacturers to develop facility specific models and rule sets to continually determine the optimum operating setpoints for all the process units.

They have packaged their approach into a SmartProcess Energy application that is used to reduce the total cost of energy in a mill/plant by automating critical decision-making. The energy optimization process begins with a review of existing Powerhouse operations and recent operating data. The consultants use off-line modeling tools to evaluate improved operating methods and estimate the level of savings that can be achieved. The effort reviews the fuel alternatives, purchased versus produced power options and constraints, and the current decision making process for optimizing energy and steam production and usage.

Bob said that a key to Emerson's energy optimization approach is extensive data validation to help the application tolerate measurement errors and device failures. The decision making rules for optimum operation are implemented using mathematical models running within the automation system controller.

He pointed to two areas of savings. The first is identifying large opportunities for cost improvement such as changes in fuel type usage. Perhaps more important is the second area, which is the constant small adjustments being made to process setpoints in real-time. This helps move the total operation to its absolute best cost point based on current constraints. These are adjustments that could not easily be recognized by the operators.

The Industrial Energy Solutions team has documented typical annual savings of $500K to $2MM USD where the SmartProcess Energy application has been applied.

Moore's Law foretold of computer processing power doubling every eighteen months when the idea was introduced by Intel's cofounder Gordon Moore in 1965. This law has application in process automation since the microchips that power today's controllers and I/O have taken advantage of this increasing power.

Advanced process control algorithms, once solely running in the domain of host-level computers running above the automation systems, are now available down in automation system controllers. These algorithms include model predictive control, fuzzy logic, and neural networks to name a few. The benefit is that these controllers are closer to the action of the running process and can use the diagnostic information in smart field devices to make sure they know when to control, and when to leave control in manual with the operators. These controllers are also available in redundant configurations, something that was more difficult and expensive to achieve with host computers.

Another result of this ever increasing processing power is that more applications can take advantage of these advanced process control algorithms. What was once strictly the domain of large applications like refinery optimization due to the cost, complexity and expertise required, can now be applied to smaller applications.

Lime kilns found in pulp and paper mills, cement and steel mills are a great example of a smaller application that is well suited for model predictive control (MPC) technology. I spoke with Gordon Lawther, a consultant in our Pulp and Paper industry center. Gordon explained that lime kilns are highly interactive in that a change to one process variable impacts the others. They are also constrained by excess oxygen, hood draft pressure, and the kiln stack emissions.

Using model predictive control allows the lime kiln to be operated as a unit instead of a collection of loops which all interact with one another. Since it's an empirical model of the running processes it can predict into the future to help operators see where key variables are heading and help them resist manually intervening and inducing variability into the process.

Gordon noted that this increased variability is reflected in the lime quality and fuel usage which increases operating costs. The team has consistently documented annual energy savings of 10% or more and maximized mud throughput has saved more than $500,000 USD per year in purchased lime.

The team has packaged their expertise into a SmartProcess Lime application. It uses Emerson model predictive control technology and the expertise Gordon and team bring in benchmarking the existing process, creating and commissioning the models, and measuring the performance improvements. The importance of training operations staff cannot be overstated and is also an integral part of all SmartProcess Lime projects.

Pulp and paper manufacturers have lived in a period of time of extended price softness for their products. This as well as other market and economic forces have kept their focus on reducing cost per ton of production to help their margins. Pulpandpaper.net recently reported some optimism although not in the near term:

So far, prices for paper and pulp have not risen, in fact they remain weak in North America. But some firms around the world are beginning to get slightly larger orders from European buyers, market analysts said.

One area pulp mills have had success in reducing their cost per ton produced is in the kraft pulping process, specifically with their kraft batch digesters. The Pulp and Paper industry team has packaged their expertise to optimize the performance of the individual digesters and the controls common to all of them.

The process typically begins by working with the mill to understand the key performance indicators, areas where the mill is limited, and current operational performance versus target performance. An example of this data analysis includes a KPI on quality such as a Kappa/K#/P# scatter graph and it's correlation to H-Factor and grades.

Around the individual batch digesters are controls developed for the phases: chip and liquor filling, bring-up, cook, blow, relief and blowback, and the cooking model. Controls common to all the batch digesters are optimized including: chip system, liquor, scheduling, pulp quality, and steam limiting and smoothing. Smart alarming is added so that the alarm levels are appropriate for the operating phase of the digesters. Automatic production reporting provides the mill management with an information windshield to drive the process in the required direction, with reports available by blow, shift, day, month and annual summaries.

Results from the optimization process typically include a reduction in white and black liquor usage of 1-2%, reduced steam usage 5-15%, reduced batch cycle time 5-15%, and an increase in overall yield of 0.5-3%. Bryan adds that naturally these results vary depending on process constraints.

Bryan stresses that the key is to have an optimized solution which the operators trust to not override in manual mode. To accomplish this, the team provides history and analysis tools, training and education, and ongoing support. The design philosophy is for the operator to be no more than 2 clicks away from any information.

A continuing theme to several of these blog posts is how process manufacturers are looking for ways to improve energy efficiency in these times of high energy costs. One way to do this is to optimize the steam required for a distillation process.

I caught up with Pete Sharpe whom you may recall from an earlier post on reducing costs of APC projects using pre-engineered applications. Pete has recently completed some work for a specialty chemical manufacturer that wanted to improve the performance of the distillation columns by decreasing the steam required and decreasing the reflux flows to the columns.

Pete worked with the process engineers to apply model predictive control (MPC) technology found in the SmartProcess Distillation Optimizer. This application is one of the pre-engineered SmartProcess applications Pete described in the earlier post.

The distillation process is a classic multivariable problem with control variables, manipulated variables and constraint variables.

Using model predictive control, the column can be controlled and operated as a unit instead of a collection of loops.

In addition to reduced operator load, the process engineer identified 400 lb/hour savings in steam on one of the columns and close to 900 lb/hr on the first column where the Distillation Optimizer application was implemented. With a cost for 135 psi steam of $5 per klb, this translates into energy savings of more than $50,000 USD for these particular columns. This savings adds up as all of the distillation columns on site are converted over from multi-loop control to MPC-based control. Steam reductions are a result of lower reflux flows that have been reduced by about 20%. While this change increases the average overhead impurities as is expected, it is well within specifications.

Now that the Distillation Optimizer has demonstrated stable results on two of the columns, Pete is working with the process engineers to implement it on the remaining columns over time. Beyond better performance and increased efficiency, the best measure of the success to date has been operators leaving the MPC control on more than 90% of the time. This is one of the true tests according to Pete and the Advanced Automation Services team.

As I mentioned in an earlier post on fired heaters in refineries, this is an area where refiners can reduce energy costs by modernizing and optimizing the performance of these units.

The objective is to operate the heater at the lowest fuel cost, while being able to reliably handle the variability in fuel quality and BTU content for any waste fuels used by the heater. Many of these units operating in established markets around the globe are 20 to 30 years old and these often experience unplanned outages due to component failure. Another challenge is the tube coking / fouling in the units which can reduce operating efficiency.

Every 3-5% improvement in fired heater efficiency can mean 3 to 5 cents per barrel net margin improvement. For a 100kbpd facility, this translates into $1.8 to $2.9 million USD in annual savings.

I spoke with Chris Forland, an operations consultant for the Emerson Process Management group. Chris and the other consultants have helped refiners identify several ways to improve the efficiency and reliability of their fired heaters.

It starts with a study to baseline the performance and to confirm the operating issues impacting performance. This study helps to identify opportunities for improvement and to provide estimated costs and benefits to determine return on investment for the improvement initiative.

Beyond the SmartProcess Heater Optimizer mentioned in the earlier post, some typical opportunities Chris sees for improvement include on-line continuous measurement of fuel quality and BTU content, in-situ measurement of oxygen and carbon monoxide in the exhaust stack, predictive diagnostics for the smart instrumentation, digital valve controller actuator for the damper drives and control valves, and predictive measurements around the flame and relative coking.

These projects usually include a post project audit to determine the actual return on investment versus that forecasted one in the front-end study. This provides a measurement for the success of the project by determining the actual return on investment.

Congratulations to Praxair's Geismer, Louisiana methane reforming production facility for winning Chemical Processing magazine's Plant Innovation Award.

Praxair's innovations were the result of their objectives to reduce energy (natural gas) usage while meeting the production demands for CO, H2 and steam much of which is exported to customers' neighboring plants. The Geismar facility is comprised of four steam reformer plants of different age. The key challenge was to allocate load based on current plant performance and product slate.

I spoke with Chris Hawkins, a Senior Consultant and Technology Manager in Emerson's Asset Optimization organization. The AO team worked with Praxair to design advanced site optimization using the AMS Optimizer to calculate values for key process variables in real-time to increase the energy efficiency and consistency of each of the individual units. This work was combined with some model predictive control strategies implemented by the Praxair project team.

Working with the Praxair team, AO team members developed detailed models in the AMS Optimizer for each of the operational components of the site. The models compare the current plant operation and customer demands to determine the most economic set of production setpoints across the multiple units. These setpoints are automatically sent to the lower level control systems to keep the process running at optimal efficiency.

Chemical Process magazine reported the following results from the project:

Individually, the MPC systems increased in the carbon monoxide recovery on the cold boxes an average of 5-8% across multiple units, and much more consistently running of the units during production changes and load disturbances. Full implementation of this approach cut energy usage by over 1.0% for the facility. While that may not seem like a large percentage, for a site of this size, such a reduction equates to several hundreds of thousands of dollars a year in savings and provided a project pay back on the order of 2 years.

I caught up with Lou Heavner who you may remember from an earlier model predictive control post. When it comes to blending operations in refineries, Lou knows this application very well.

He's recently created a Blend Lite application which is a scaled down version of the Blender Control application that has been developed and deployed for numerous refiners around the world.

The full blend control application has in-line blending which offers several advantages over sequential blending, especially in the reduced time required to blend large volumes. Some products like gasoline involve many components and the finished product must meet many specifications. The standard blending package addresses the many component/many spec problem and frequently includes the use of an in-line analyzer and blend optimization.

There are a number of blending applications that involve only a few components and don't require all of the advanced functionality that is usually required for gasoline blending. Refineries have some products that fit this simpler application like jet fuel and asphalt. Other industries have processes that fit this simpler blend application. The key value in these applications is the ability to simultaneously blend multiple components--all in the correct ratio--even if one of them becomes constrained. These simpler blend applications still need functions such as: blend recipes, start/stop sequencing, failure monitor, ratio controllers, ramping and pacing.

Lou said to consider the Blend Lite Package if your application has the following:
- In-line blending for up to 5 components with one flowmeter and control valve per component
- Ratio control of current flows or totalized flows with pacing during the blend and ramping at the beginning and end of blend
- Up to 32 blend recipes
- Blend reports
- Start/stop capability for one valve and pump per component
- Basic operator graphics like those provided for the Blend Package

Most process plants have at least one key quality parameter that is not measured in real-time. Traditionally, process manufacturers have relied upon manual lab samples to verify that the process was producing products within the required specifications. The issue with this approach is the time delay between when the sample is drawn and when the lab analysis is complete. If the results are not within the required specifications the product during this period of time must either be reworked or in some processes, disposed of. This reduces the efficiency and profitability of the process.

Over the past decade, neural network technology has been introduced as a way to create models to act as soft sensors for properties where no physical sensors exist. Soft sensors are also used in conjunction with analyzers to fill in gaps between sample points or for validation/backup of expensive analyzers. Some examples include: Kappa analysis in the pulp and paper digestion process, end (cut) points of products in the CDU/VDU columns of a refining process, food properties, end of fermentation process prediction, and emissions analysis.

In spite of the indicated potential and the improved tools, the acceptability of neural net soft sensors has been fairly limited. I spoke with Ashish Mehta, a lead developer in the DeltaV APC technology organization, who feels the complexity (actual and perceived) has been a major factor. He presented a paper, Successfully developing a property estimator with DeltaV Neural (7.8Mb), at the last Emerson Exchange to help alleviate such concerns.

Ashish mentioned the real benefits of using soft sensors:

• They provide real-time online predictions of important quality variables (as fast as 1s)
• They reduce process variability as predictions can be used in feedback
• They improve control as quality parameters can be incorporated into APC/optimization strategies

When it comes to selecting the variables as input to the neural network, make sure you capture the ones causing the dominant effects. More inputs are better, although avoid those offering redundant (highly correlated information.) Use calculated inputs like ratios/first principles-based inputs.

Ashish stressed that neural networks are empirical models where the underlying model knows, and is therefore only as good as the data it is trained on. As a result it's important to collect the data over a wide operating range, and make sure outliers (say due to shutdown) are removed from the training data set. Generally, you should also maintain an additional data set, different from the test set, to verify the model by comparing its prediction with the actual data.

If the soft sensor has been created using DeltaV Neural, it is commissioned by a simple download to an NN function block. The function block approach greatly simplifies the online operations and increases the soft sensor lifetime. For example, it will monitor for any of the inputs being outside its trained range and mark the soft sensor output as uncertain, so that the control strategy can take this uncertain information into account. It continues to use the lab analysis results in a feedback fashion to automatically adapt the prediction value to any process changes after training.

In addition, the NN function block can be used in a closed loop control strategy as the PV of an MPC (or PID) block. According to Ashish, the greatest opportunity is the ability to use the key quality measures, that were available only infrequently, in full closed loop (advanced) control and optimization strategies thereby resulting in significant variability reduction (and likely increasing the operation's profitability.) You should always look ahead to closing the loop on the quality parameters that you want to develop a soft sensor for.

Periodically most continuous running plants must schedule a shutdown to perform the maintenance activities which cannot be done while the process is running. The process of managing this period while the plant is shut down is commonly known as a turnaround.

Turnarounds are very expensive in terms of lost production and the expenses associated with the maintenance activities performed so it's critical to plan and execute these activities as efficiently as possible.

I spoke with Mark Coughran, whom I highlighted in an earlier post, about the planning role the Emerson control performance group can play in a process manufacturer's turnaround planning process. Based on the experience the team has gained working with process manufacturers across many industries, they have collected their best practices into a Pre-Shutdown Automation Service.

With this service, Mark and other consultants help identify and prioritize the control loops and devices that should be improved including control strategies, tuning, control valves, dampers, drives, process sensors, and transmitters. They pre-diagnose the troublesome control loops and devices which most impact process stability and flexibility and that can only be fixed with the process shut down.

The process begins by working with plant staff to assess the performance of critical loops with the most economic impact on the plant. Using tools like Emerson's EnTech Toolkit and Entech Valve