Modeling and Control

Is this the time for process and automation system designs to minimize dead time and interactions? Is this the time for university graduates to understand controller modes and parameters and the power of the DCS environment? Is it possible for these graduates to know how to tune a controller with non perfect mixing, measurements, and valves? Is this the time for users to know how to improve control system performance? Is this the time for new engineers to have a virtual mentor? Is this the time for suppliers to be able to demo the value of better measurements, valves, tuning, and advanced control?

This could be the time if modeling is embedded in the DCS and becomes a common tool in universities and industry. It started to happen about 5 years ago. Terry Tolliver and Robert Heider at Washington University use an industrial DCS as a virtual plant and in a computer control lab to teach process modeling and control as part of their chemical and system engineering programs. Atanas Serbezov at Rose-Hulman Institute of Technology uses a DCS in a lab to teach process control to Chemical Engineers. A DCS system has just been installed at Purdue University as part of the Engineering Research Center with Rutgers and the New Jersey Institute of Technology. If you doubt the value, talk to the students. In industry, Broadley-James, Lilly, Lubrizol, Monsanto, and Solutia have started using embedded modeling in a DCS for process design and control.

Modeling was such an integral part of my career, it is difficult for me to imagine how I would have learned and accomplished anywhere near as much without it. Modeling was a key part of my job even in the old days when you had to key punch cards for the IBM Continuous Simulation Modeling Program (CSMP) and submit them for an overnight run in a room full of main frame computers. When I got terminal server access to a computer with the Advanced Continuous Simulation Language Program (ACSL), I thought I was in heaven even though ACSL was designed for the aerospace industry. When graphical flow sheet simulators on a PC came along that we could interface to a DCS, I was blown away even though the interface was slow and cumbersome and the model speedup was rather minimal and inconsistent. I gave this all up to retire in 2002 and set up the virtual plant at Washington University. This wasn’t quite enough so I ended up in Austin in the fall of 2004 to see if I could help the future of modeling by making it more accessible. While only 50% of my 75% part time venture is actually doing modeling, I am at a juncture to see how well it can be used.

Eventually the automation world will evolve to where modeling is an integral tool for learning and 4D processes (development, design, deployment, and diagnostics). While my physics and process modeling background leads me to focus on models based on first principles, data driven models such as neural networks, linear dynamic estimators (e.g. MPC models), and multivariate statistical process control such as projection to latent structures (PLS) have great relevance because they have make no assumptions and detect the inevitable unknowns. I envision a future of hybrid models embedded in the DCS that use the best that each of these modeling technologies offers. Is this the time?

Technorati Tags: education | process control | process design | process models | simulation |

I left the blogsphere to do a beta test of pH modeling and control. I return on July 2 to ask the question "Is This the Time"?

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What if the bench top system used in the research lab that is the basis of core process knowledge had a DCS with all of the major control technologies embedded? What if a dynamic model was embedded in the DCS to form a virtual plant that enabled exploring, discovering, and prototyping optimum operating conditions and advanced controls? What if a process trial run that normally takes 10 days could be completed in 10 minutes? What if the benefits demonstrated could help develop and justify better online analytical measurements, advanced controls, and data analytics? What if the model and control system could be scaled up to the pilot plant and ultimately the commercial plant? What if the same model and control system could be deployed to the industrial facility and be used for operator training and the development of process diagnostics?

A pioneer out west is making this future a reality. Scott Broadley, the president of Broadley-James, asked himself what if the pH and dissolved oxygen electrodes he was selling were packaged with a bench top bioreactor and a DCS optimized to make it more flexible and easier to use by the biochemist in the lab environment. He had the vision and conviction to put a DeltaV DCS with all of its innate capability on bench top bioreactors when others were going for the cheapest lab controller they could find. With determination and insight, Scott grew the business into a state of the art operation. But this was just the beginning. After seeing my demo of a virtual plant at Interphex 2007 in the Emerson booth next door, Scott thought what if a virtual bioreactor could be part of a concept to accelerate process development through dynamic modeling and advanced control embedded in the DCS. Even though it was a major expense and a 2 year commitment to run dozens of 10 to 20 day bioreactor batches, he saw the potential. In the process of the beta test, he found new probes that could measure media components, amino acids, and cell volume, size, and viability online. Even the possibility of measuring product and precursors to cell death online now appears to be within reach opening the door to incredible opportunities to increase product concentration and quality through media and amino acid concentration and batch profile control. Scott is convinced that the results will determine how bioreactors will be run for the next 15 years.

The beta test team of Trish Benton, Broadley-James cell consultant, and Michael Boudreau, Emerson principal consultant, has the right combination of skills and attitude to make it happen. The mammalian model prototype I developed in the fall of 2007 is moving forward in their fully capable hands. Michael scaled down my industrial size bioreactor (15,000 Liters) model to the lab scale (5 Liters) and incorporated the Broadley-James lab optimized control system to form a versatile virtual plant. The team is now in the throes of identifying the process conditions and parameters for an innovative new cell line and a scale up to 100 Liter Single Use Bioreactors (SUB). For more information on the beta test, check out the article “PAT Tools for Accelerated Process Development and Improvement” at http://www.easydeltav.com/news/viewpoint/BioProcess0308.pdf

None of this would have happened without the understanding, support, and encouragement provided by Grant Wilson, vice president of DeltaV Technology, who has the PAT and technical background to see the opportunity. Ever since my first PAT presentation with Grant on bioreactor modeling and control at the 2005 Emerson Exchange, Grant has been the best advocate for advancing PAT through modeling and control.

Technorati Tags: advanced control | bioreactor control | PAT | dynamic simulation | process models |

My core dump of myths went on longer than intended because there were so many stuck in my brain, it was so freeing, and it was so easy to unload them when pressed for time.

My recent spike in work load has put me in danger of being kicked out of various retirement associations including the Retired Automation Professionals (RAP). Apparently, my excursion into 40+ hour weeks means I not retired. Personally I think this is a bad rap. I still take 12 weeks off a year to visit friends, relatives, national parks, and beaches. I am having too much fun being part of the future to give it all up. Plus there are the benefits of active membership in the Adorable Automators Association as noted in my April 2008 Control Talk. http://www.controlglobal.com/articles/2008/118.html

The myths provided a reality check and a basis for looking forward to new tools that address many of the issues raised. Friendlier and more proficient versions of all of the process control technologies are being embedded as standard tools in a DCS. Now you can explore, discover, prototype, justify, and deploy process control improvements in the same configuration environment used for the basic control system. Adaptive tuning, a rich spectrum of PID enhancements, fuzzy logic control, loop performance monitoring, model predictive controllers, neural networks, on-demand tuning (auto tuners), and process dynamics identification are presently embedded. Soon online data analytics (multivariate statistical process control) and process modeling capability will be added. Terry Blevins (principal technologist) and Mark Nixon (chief architect) had this vision at Emerson Process Management and made it happen in DeltaV.

This comes at a turning point in industry where most of expertise in the application and understanding of the value of control opportunities are becoming full time members of RAP. Also, most of the opportunities are now overseas. The change in demographics is obvious when you look at the weekly questions on process automation submitted to Liptak where 95% of the questions are from overseas and cover a wide range of practical and essential application issues.

When I was helping the Instrumentation Systems and Automation (ISA) society in the early stages of the development of the Certification of Automation Professionals (CAP) program, I realized that it was difficult to find a book that addressed the day to day needs. I realized that publications in our field including my own were at too high a level and assumed too much for the new workplace where mentors and company training programs are scarce. Also books on process control were too mathematical and theoretical and the books on instrumentation were often a rote description of the principles of operation. Not much was offered on selection, application, installation, performance, and maintenance for the extensive range of process types and conditions needed by relatively inexperienced professionals to do their job. The Automation Book of Knowledge (ABoK) developed as part of CAP and various handbooks by Liptak, Boyes, and myself help but much remains to be done. Toward this goal, I am looking forward to working with Terry Blevins and Mark Nixon to provide a hands-on learning source/guide employing a full suite of embedded technologies in a virtual plant. This book focuses on the opportunity of doing a better job of process control for both batch and continuous processes.

Technorati Tags: adaptive control | controller tuning | data analytics | fuzzy logic | loop performance | process control | process dynamics | process models | model predictive control | neural networks |

I dug up the following myths from my April 2006 Control Talk column in Control Magazine. I am into recycling and going green. In fact these myths may be a bit moldy.

(26) Auto tuners can compute controller tuning settings with an accuracy of more than one significant figure. Act surprised when unmeasured disturbances, load changes, valve stick-slip, and noise cause each result to be different. Look forward to the opportunity to play bingo with the second digit.

(27) You can just dump all your historical data into a neural network and get wonderful results. Forget about the same stuff that cause auto tuners to have problems and use variables drawing straight lines because anything that smooth or well controlled must be important. Use the controlled variables (process variables) instead of the manipulated variables (controller outputs). Don’t try to avoid extraneous inputs or identification of the control algorithm instead of the process. If you want to purse a career in data processing, use every variable.

(28) Models can predict a process variable that is not measured in the field or lab. Great way to spur creativity in training a neural network and validating a first principal model plus it has the added bonus of the model never being wrong. Wait till your customers figure out something is wrong with the composition of your product. Discount as hearsay any suggestions that even the best models need periodic correction.

(29) To reduce variability in process outputs (temperatures and compositions), keep all the process inputs (flows) constant. Keep believing that you can fix both the process inputs and outputs and don’t accept the notion that process control must transfer variability from process outputs to process inputs to compensate for disturbances.

(30) Positioners should not be used on fast loops. This was true for the good old days of pneumatic positioners and analog controllers. Surely, digital positioners with tuning settings and digital control system scan times can’t make the original theoretical concerns less important than the practical issues of real valves. If you would rather believe the controller outputs are the actual valve positions, and just want valve problems to slip by, save some bucks on your project and only put positioners on slow loops. Just don’t stick around for start up.

Technorati Tags: | loop performance | process control | process dynamics | process models | neural | networks | soft sensors | valve dynamics |

We conclude with the following myths from Appendix D in my guide Models Unleashed - Virtual Plant and Model Predictive Control Applications published by ISA, 2004.

(21) You need an advanced degree to do advanced control. Not so anymore. New software packages used to form a virtual plant automate much of the expertise needed and eliminate the need for special interfaces. The user can now focus mostly on the application and the goal.

(22) Dynamic simulations and model based control are only applicable to continuous processes. Since most of the applications are in the continuous industry, this is a common misconception. While it is true that a steady state simulation is not valid since there is by definition no steady state in batch, dynamic simulation can follow a batch as long as the software can handle zero flows and empty vessels. Model predictive control (MPC), which looks at trajectories, is suitable for the optimization of fed batch processes during particularly important points in the batch cycle. The opportunities to improve a process’s efficiency by MPC are about 25% for batch compared to 5% for continuous operations.

(23) You need consultants to maintain models and advanced control systems. No longer necessarily true. The ease of use of new software allows the user to get much more involved, which is critical to make sure the plant gets the most value out of the models. Previously, the benefits started to drop as soon as the consultant left the job site. Now the user should be able to tune, troubleshoot, and update the models.

(24) You don’t need good operator displays and training for well designed advanced control systems. The operators are the biggest constraint in most plants. Even if the models used for real time optimization and model based control are perfect, operators will take these systems offline if they don’t understand them. The new guy in town is always suspect, so the first time there is an operational problem and there is no one around to answer questions, MPC systems are turned off even if they are doing the right thing. Training sessions and displays should show the individual contribution of the trajectories of each controlled, disturbance, and constraint variable to the observed changes in the manipulated and optimization variables.

(25) You need to know your process before you start a model based control system application. This would be nice, but often the benefits from a model stems from the knowledge discovery during the systematic building and identification procedures. Frequently, the understanding gained from developing models leads to immediate benefits in terms of better set points and instruments. The commissioning of the MPC is the icing on the cake and locks in benefits for varying plant conditions.

Technorati Tags: dynamic simulation | model predictive control | loop performance | process control | process dynamics | virtual plant |

To blog or not to blog, that is the question. When on vacation, I choose not to. Now I am back and ready to blog on. Here are some myths from my days (and nights) at Monsanto improving pH loops.

(16) More frequent buffer calibrations improve pH measurement accuracy – unless an electrode needs to be removed to be cleaned, it is better to leave it in the process and do an adjustment based on a statistical average of process samples. The buffer is not representative of the process in terms of liquid junction potentials and the glass electrode response especially when the process has salts or strong acids and bases. The reference electrode can take up to a day to reach equilibrium with the process when it is reinserted. Also, wiping the glass electrode or soaking it in cleaning solutions reduces its life expectancy. Finally, the effect of temperature on pH is not found via a buffer which leads to the next myth.

(17) The pH electrode temperature compensator corrects for changes in pH with temperature – the standard temperature compensation is the change in millivolts per pH unit per the Nernst equation. The change in the actual solution pH due to the change in dissociation constants with temperature requires another correction. Many smart pH transmitters now have this solution pH temperature compensation but it is up to the user to find the relationship. Information on how the dissociation constants change with temperature and how this would affect a complex solution is scarce to nonexistent so it requires a pH sample’s temperature to be varied in the lab. For the simple case of water and a strong base (e.g. caustic) where the effect is dominated by the change in the water dissociation constant with temperature, the error is about -0.03 pH per degree C.

(18) A second pH electrode always improves measurement accuracy and reliability – a second electrode offers more questions than answers since they never agree unless you are lucky enough to have a problem that a smart transmitter can detect. Every operator has a favorite electrode based on some interesting war stories but its anybodies guess as to which is right. Also, since electrodes can fail or develop errors in almost any direction, a decision to select the best electrode requires some extra intelligence such as changes in electrode resistance or a comparison of response times. At Monsanto we used middle signal selection of three electrodes to automatically and inherently ride out a failure of any type and reduce the noise and short term errors for concentration gradients.

(19) The rangeability of a control valve is per catalog specifications – the rangeability statements by valve suppliers usually does not take into account the stick-slip near the closed position. Many rotary valves carry impressive rangeability statements but don’t deliver the goods because the control at low flows is a big saw tooth from high seal friction and breakaway torque.

(20) A valve with a positioner is a good throttling valve – the response of a control valve depends upon the entire package (valve, seat, seal, actuator, packing, positioner, and feedback mechanism). Putting a positioner on an on-off valve (e.g. block or isolation valve) does not make it a throttling control valve. In fact the feedback mechanism may be lying to the positioner in which case the smart diagnostics mean nothing. For more info on this check out the article “Improve Control Loop Performance”

I have “Final Four” on my mind and I am off to see if my alma Mata Kansas can upset UNC but before I go, if you ever come to Austin check out the Oasis for sunsets and its new Starlight bar for star gazing over Lake Travis. I spent last night at the Oasis Starlight listening to the Eggmen (a great Beatles cover band) and having flashbacks to the 1960s and 1970s. Too bad I didn't wear my plaid bellbottoms.

Technorati Tags: loop performance | pH | process control | process dynamics valve dynamics |