Emerson Process Experts

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.

Tags: process control | diagnostics | AIChE | APC | loop optimization | cascade control | feed forward control | Lambda Tuning | refining | refinery |

May 15, 2008 in Process Optimization, in Refining, in Variability Management | Comments (0)

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.

Tags: natural gas | APC; | advanced process control | project justification | energy savings | boilers | fired heaters | combustion control | distillation |

May 13, 2008 in Boilers, in Distillation Column, in Energy Management, in Fired Heater, in Lime Kiln, in Process Optimization | Comments (0)

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.

Tags: research octane number | benefits estimation | process optimization | advanced control | advanced process control | APC | steady state optimization | distillation column |

May 9, 2008 in Distillation Column, in Process Optimization, in Refining | Comments (0)

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.

Tags: pH modeling | pH control | virtual plant | process simulation | waste water treatment |

April 21, 2008 in Control Strategies, in Process Optimization, in Regulatory Compliance, in pH Control | Comments (0)

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.

Tags: batch manufacturing | APC | advanced control | advanced process control | neural network | batch cycle time | MPC | model predictive control | batch distillation | hydrogenation reactor |

March 7, 2008 in Distillation Column, in Food & Beverage, in Life Sciences, in Process Optimization | Comments (0)

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.

Tags: industrial powerhouse | process equipment | industrial energy | energy efficiency | energy management |

February 4, 2008 in Energy Management, in Plant Equipment, in Process Optimization | Comments (1)

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.

Tags: pump discharge | butterfly valve | control valve | valve sizing | valve positioner | segmented ball valve |

January 31, 2008 in Plant Equipment, in Process Optimization, in Variability Management | Comments (0)

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.

Tags: vessel level | tank level | level control | process variability | load disturbance | integrating process | lambda tuning |

January 10, 2008 in Level Control, in Process Optimization, in Variability Management | Comments (0)

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.

Tags: split range control | feed water | pneumatic instrumentation | level control | I/P converter | Lambda tuning | digital valve positioner |

November 15, 2007 in Feed Water Control, in Power, in Process Optimization, in Variability Management | Comments (0)

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.

Tags: turnaround | control performance | plant assets | control valves | instruments | foundation fieldbus | HART communications |

August 23, 2007 in Asset Optimization, in Plant Equipment, in Process Optimization | Comments (0)

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! Also, make sure to visit a listing of Greg's works over the years in the Process Control Insights section of the EasyDeltaV.com website.

Tags: eBook | Greg McMillan | process control | continuous control | ModelingAndControl.com |

August 22, 2007 in Education, in Process Optimization | Comments (0)

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.

Tags: fired heaters | firetube | fuel firing | flue gas | efficiency calculator |

July 23, 2007 in Analyzers, in Energy Management, in Fired Heater, in Process Optimization | Comments (4)

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.

Tags: boiler | steam demand | backpressure | steam header | lambda tuning |

June 21, 2007 in Boilers, in Process Optimization, in Variability Management | Comments (0)

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.

Tags: distillation column | neural network | property estimator | virtual sensor | lab sample |

June 6, 2007 in Distillation Column, in Process Optimization, in Refining | Comments (0)

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.

Tags: control valve | positioner | cascade control | control loop | loop gain |

May 25, 2007 in Education, in Process Optimization | Comments (0)

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.

Tags: ARC | APC | advanced control | advanced process control | process optimization |

May 11, 2007 in Process Optimization | Comments (0)

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.

Tags: non-linear | control valve | loop tuning | gain scheduling | adaptive tuning | adaptive modeling | process gain |

April 9, 2007 in Education, in Process Optimization | Comments (0)

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.

Tags: loop tuning | process optimization | process dynamics | control performance |

March 7, 2007 in Process Optimization, in Screencast | Comments (2)

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.

Tags: Process Analytical Technology | PAT | Pharmaceutical | Biotech | Life Sciences | operational excellence | six sigma |

March 6, 2007 in Life Sciences, in Process Optimization, in Regulatory Compliance | Comments (0)

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.

Tags: control performance | APC | fuzzy logic | model predictive control | neural networks | advanced control | control strategy |

February 12, 2007 in Process Optimization, in Variability Management | Comments (0) | Trackback (0)

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.

Tags: FDA | Process Analytical Technology | PAT | model predictive control | MPC | neural network | fuzzy logic | bioprocess | life sciences |

January 31, 2007 in Fermentation, in Life Sciences, in Process Optimization, in Simulation | Comments (0) | Trackback (0)

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!

Tags: feasibility study | modernization project | key performance indicators | KPI |

January 22, 2007 in Process Optimization, in Project Services | Comments (2)

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!

Tags: process analytical technology | PAT | pharmaceutical | biotech | life sciences | FDA | performance monitoring |

January 9, 2007 in Life Sciences, in Process Optimization, in Variability Management | Comments (0) | Trackback (0)

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.

Tags: ISA/ANSI 77 | power plant | performance assessment | control performance |

December 13, 2006 in Power, in Process Optimization | Comments (0) | Trackback (0)

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.

Tags: loop tuning | control strategy | control valve | historian |

December 7, 2006 in Process Optimization, in Pulp & Paper, in Variability Management | Comments (0)

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.

Tags: fired heater | economic calculator | refining | refinery | energy efficiency |