Emerson Process Experts

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)

I received an email from Anand Iyer. He's a certified project management professional (PMP) and a project manager in Emerson's engineering center in Pune, India. His project experience covers the gamut from pharmaceuticals, bulk drugs and intermediates to oil, gas and petrochemicals.

He's sent me a paper he's written entitled, Collaborative Measurement Control System Engineering. It describes how measurements close to one another in the process can collaborate with one another to verify their operation. He describes an example around a distillation column:

Now let us take two temperatures (bottom temperatures) in a distillation column and a level measurement. When the level is normal, the two temperatures are same or have a fixed relationship between them. TI1 is placed at a lower level in the column (near bottom) and TC2 is at a higher level (and used for Temp. control). Now TC2 is generally used for control. We can safely say that if Level is normal, and TC2 is under maintenance, TI1 can be used for control (with a minor adjustment to Setpoint if required). Thus Level and Thermocouple TI1 put together can "collaborate" the measurement of Temperature-measurement TC2.

Anand contrasts the traditional approach to a failure with how collaborative measurement strategies can be used in control strategies to avoid outages or process disturbances. In the traditional approach:

…the first thing done if an element were to fail was to swap the elements (either during the shutdown caused by the failure) or by a planned outage or having the loop in manual and doing the swap. At times, we have also used our ingenuity and just swapped the wires at the analog inputs and tuned control setpoints to have the plant up and running in a very short time. And hopefully, in all that chaos, someone had the presence of mind to record the swap on the wiring diagrams.

Using a collaborative measurement strategy:

…says that if level is not low and TC2 is not available then TI1 can be a valid measurement. We alarm the operator that TC2 is not available, fine tune the setpoint if required… All this occurs automatically and there is no outage or disturbance that could result in quality issues.

He extends the thought to Foundation fieldbus devices where the final control elements themselves can perform the logical evaluations and select the available primary or collaborated measurement, increasing the overall robustness of the control strategy. Anand also extends his thinking to wireless devices and how they could be used in a collaborative measurement environment—not as a primary measurement, but as a collaborative measurement to check on other devices nearby.

I hope you'll give Anand's paper a read and add your thoughts.

Tags: collaborative measurement | project management professional | PMP | Foundation fieldbus | distillation column | measurement strategy | control strategy |

March 12, 2008 in Control Strategies, in Distillation Column, in Foundation Fieldbus, in Measurement, in Project Services, in Wireless | 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)

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)

The folks at Mynah Technologies with their Mimic simulation software, virtual I/O network gateways, PLC I/O interfaces and host of drivers for the DeltaV system continue to build conversations through their forums and experts blogs within the forum. Recently Dr. Aleksandr Muravyev a simulation consultant created a Mimic Distillation Modeling package to simplify software acceptance testing and operator training around distillation columns.

Dr. Muravyev joins the automation blogging fray with this post about this distillation modeling package. If you have experiences with this product (or even if you don't) feel free to join in the conversation. It's a forum so you'll need to register first.

I did ask the Mynah folks if the forum software they are using supports RSS feeds so I could get these updates coming to me instead of going out and seeking it. It does and they will soon be adding this functionality.

That's a great thing as some RSS readers like Google Reader support both regular and mobile viewing. It means I can keep up with the automation and technology bloggers whenever a have some spare moments. From playing with the various RSS readers, I seem to be gravitating to using Outlook 2007's embedded RSS reader for my Emerson intranet-based RSS feeds and Google Reader for external RSS feeds. How about you?

Tags: distillation column | process simulation | distillation modeling | operator training | software acceptance testing |

February 19, 2007 in Distillation Column, in Operator Training, in Simulation | Comments (4) | Trackback (0)

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.

Tags: neural network | virtual sensor | lab sample | crude unit | refinery | refining | distillation | ISA EXPO 2006 |

October 31, 2006 in Analyzers, in Distillation Column, in Process Optimization, in Refining | Comments (0)

From my days as a young systems engineer working on offshore oil & gas platforms in the Gulf of Mexico, I know that abnormal situations in our processes are something we all wish to avoid. A 1999 study by the ASM consortium estimated $10 billion USD in losses for U.S. process manufacturers due to abnormal situations. The question is how best to prevent these abnormal situations from occurring in the first place.

Emerson's Ravi Kant and Roger Pihlaja recently presented a paper, "Abnormal Situation Prevention (ASP) in Complex Systems" at the recent NPRA Q&A and Technology Forum.

In their presentation they stress that the potential severity and cost of an incident increases if timely corrective action is not taken. An example cited from a refinery abnormal situation is the failure of a butterfly valve. After going several hours without detection by the automation system or operations personnel, it caused the Cat Cracker (FCCU) to shut down. In a matter of minutes this caused the refinery to shutdown, resulting in more that $1 million USD per day in lost revenue.

Ravi and Roger explained how abnormal situation prevention (ASP) technology embedded in the sensors, actuations, and machinery health are closest to the process and have access to better information. This ASP technology can predict root causes of abnormal situations through high-frequency spectral and statistical data analysis within these smart devices. The main reason for doing this analysis closest to the process is that the sampling frequency is greater--22 samples per second, instead of 1 sample per second to 1 sample per minute typical at the automation system level.

Data analysis at this higher frequency can uncover process anomalies including drift, bias, excessive noise, process spikes, and plugged conditions. Some of the detection and prediction algorithms and techniques which are employed include: polynomial extensible regression, principal component analysis, statistical process control, decision trees, fuzzy logic, and neural networks.

They cited some specific ASP applications in refineries including early detection of catalyst losses, catalyst circulation issues, afterburn conditions, column and heater coking, temperature runaway, and acid levels outside optimal or safe levels. The key to detecting these process conditions is sharing this data analysis at from the field device level, up through the equipment level, up through the process unit level to the operators and plant maintenance staff. Digital communications technologies like Foundation fieldbus and HART provide the information path.

Roger also shared with me other high-frequency data dependant ASP applications in the process including:

• Plugged impulse line detection for DP flow transmitters
• Flame instability
• Stick/slip in FCC solids transfer lines
• Stirred tank vessel agitator diagnostics
• Continuous rotary drum vacuum filter diagnostics
• Fouling & DP level transmitter plugging in evaporators
• Detection of developing ASP issues like arching, bridging, & rat-holing in bulk solids storage vessels
• In-situ proof testing of emergency relief systems

Work continues to refine and extend these predictive ASP technologies to more smart field devices to increase the "eyes and ears" on the process in order to avoid the costs and losses associated with abnormal situations.

Tags: abnormal situation prevention | ASP | refining |

October 26, 2006 in Abnormal Situation Prevention, in Distillation Column, in Fired Heater, in Refining | Comments (0) | Trackback (0)

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.

Tags: distillation column | process optimization | loop tuning | process dynamics |

September 20, 2006 in Distillation Column, in Process Optimization | Comments (0) | Trackback (0)

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.

Tags: model predictive control | APC | MPC | distillation | energy efficiency | steam usage |

June 28, 2006 in Chemical, in Distillation Column, in Energy Management, in Process Optimization | Comments (2)

I heard a story of a tremendous effort from our advanced applied technology experts to help a refiner stabilize his units after a restart. I went to the source, James Beall who is the team leader of our U.S. Control Performance team. It turns out that he is the one responsible for these successful efforts.

He received a call from one of our local business partners late on a recent Wednesday night to assist this U.S. based refiner with the startup of some of their units including three new distillation columns. This refiner had battled the startup for several days but could not stabilize the unit or produce on-spec product. The unit would have to be shut down within 48 hours due to limited storage if stable operation and in-spec product could not be achieved.

James arrived early Thursday morning and began to assess the situation. He installed Emerson’s EnTech Toolkit to be used for process control diagnostics, complex loop dynamics identification and advanced loop tuning. He reviewed the situation with the customer, prioritized and set the order of tuning of the control loops around the columns. Using DeltaV Tune and the EnTech Toolkit to provide coordinated loop tuning, the columns were stabilized by 10:30 pm, 12 hours after arriving on-site.

During this process, several measurement problems were identified and plant personnel began to troubleshoot and correct the problems. Once these instrumentation problems were resolved within a few days, James returned to the site to continue the control performance improvements.

The quantified results? The columns achieved 100% of design production 5 days after James first arrived and nearly another 20% after a total of 7 days.

Based on these results, the refiner is looking to set up a continual process performance improvements program with James and the Control Performance team.

Tags: refining | variability | distillation column | loop tuning | control performance |

March 9, 2006 in Distillation Column, in Refining | Comments (2) | Trackback (2)