Do-it-yourself model-based control

When regulatory control can’t do the job, and you can’t find an off-the-shelf APC package, your only option may be to build your own. It’s not easy, but it can be done and good ones can make a world of difference.

03/13/2013


There can be situations where conventional regulatory control might not run a process optimally and your company management might want to try some variation of advanced process control (APC). While there are various sub-groups under this umbrella, one common approach is model predictive control (MPC), also more generically known as model-based control. This method uses a mathematical model of a process connecting relationships of relevant parameters.

Building such a model begins with an understanding of what is actually happening to the feedstocks as they are turned into final products. This includes chemical reactions, energy balance, reaction times, and so on. For some processes, it is possible to purchase existing process models that can be added to your control system. The more common the process, the greater likelihood that you will be able to buy one off the shelf. For example, there are many plants that make ethanol from corn and there are basic similarities from one location to another, so there are multiple model platforms available.

However, if your need is more specialized, a pre-packaged solution may simply not be available. In those situations, one option is creating your own process model to drive your DCS. This kind of project is not for the faint of heart, but at the same time it is not impossible. Those who have participated in such projects suggest that the most critical factor for success is deep knowledge of the process and experience with the individual plant.

Deciding strategy

Most models applied to a working plant use actual historical data combined with basic stoichiometric relationships. “There are two different mindsets,” says Chad Harper, CAP, PMP, director of technology for Maverick Technologies. “For one, you can use a recipe scenario where the process may be so deterministic that some first principles approach can be pulled together and get you where you want to be. The other one applies inferred properties using first principles to look at key variables in the plant and go through a regression process utilizing the plant data. You’re looking for the actual plant dynamic models to be able to put in there. We’ve done both.”

In either case, Harper warns that the model has to be adapted to the specific plant in question since every process unit has its own operating peculiarities. He adds, “Even if you can model something in a steady state environment you can rarely say, ‘Here are the numbers that I want to be at. Go!’ Process dynamics, closed-loop control behavior, and bumpless activation all have to be accounted for.”

The approach may be based on the information and resources available in a given situation, effectively using what you have to work with. Ric Snyder, senior product manager, information software and process business at Rockwell Automation, suggests, “Some people like to build empirical models because they have lots of data. Others like to do equation-based models because they have some chemical engineering knowledge or first principles models available. For building the models themselves, there are lots of dynamic identification toolkits that people can use, so once you’ve identified a specific tool and you have the data, building the model or regressing the parameters out of the data is not particularly difficult, it’s more the judgment of knowing what inputs I need and what are the outputs that I think I can predict. This is where some degree of chemical engineering knowledge and background is crucial in order to get good models. It’s more about defining what the model structure should be and what things should relate to that.”

Simulation first?

One way to create a model is to do it first using a process simulator as a means to test your assumptions. When the simulator is working, you can see how closely it follows actual plant operation and vice versa.

“In process plants, you don’t have the luxury of trial and error,” says Tony Lennon, industry marketing manager for industrial automation at MathWorks. “A plant manager’s real job is to make sure product is being shipped out the door while dealing with safety considerations, damage to equipment, downtime, and so on. Simulation is a means of making good design and implementation decisions, so when you do go to the plant manager, you can show what you’ve done with the simulation, reproduce the error, and say, ‘We need to try this,’ and then explain why you think it’s going to work.”

Lennon warns that simulations can’t be created in a vacuum but must reflect the reality of a specific plant environment. “If you’re going to use a simulation tool, you have to model what is happening in your plant today,” he adds. “If you haven’t done that, then don’t even continue because you haven’t captured the real dynamics of your plant. The most effective way to do that is to combine real process data in some sort of system identification process along with some type of first principles model.”


<< First < Previous 1 2 Next > Last >>

No comments
Consulting-Specifying Engineer's Product of the Year (POY) contest is the premier award for new products in the HVAC, fire, electrical, and...
Consulting-Specifying Engineer magazine is dedicated to encouraging and recognizing the most talented young individuals...
The MEP Giants program lists the top mechanical, electrical, plumbing, and fire protection engineering firms in the United States.
2014 Product of the Year finalists: Vote now; Boiler systems; Indirect cooling; Integrating lighting, HVAC
High-performance buildings; Building envelope and integration; Electrical, HVAC system integration; Smoke control systems; Using BAS for M&V
Pressure piping systems: Designing with ASME; Lab ventilation; Lighting controls; Reduce energy use with VFDs
Case Study Database

Case Study Database

Get more exposure for your case study by uploading it to the Consulting-Specifying Engineer case study database, where end-users can identify relevant solutions and explore what the experts are doing to effectively implement a variety of technology and productivity related projects.

These case studies provide examples of how knowledgeable solution providers have used technology, processes and people to create effective and successful implementations in real-world situations. Case studies can be completed by filling out a simple online form where you can outline the project title, abstract, and full story in 1500 words or less; upload photos, videos and a logo.

Click here to visit the Case Study Database and upload your case study.

Protecting standby generators for mission critical facilities; Selecting energy-efficient transformers; Integrating power monitoring systems; Mitigating harmonics in electrical systems
Commissioning electrical systems in mission critical facilities; Anticipating the Smart Grid; Mitigating arc flash hazards in medium-voltage switchgear; Comparing generator sizing software
Integrating BAS, electrical systems; Electrical system flexibility; Hospital electrical distribution; Electrical system grounding
As brand protection manager for Eaton’s Electrical Sector, Tom Grace oversees counterfeit awareness...
Amara Rozgus is chief editor and content manager of Consulting-Specifier Engineer magazine.
IEEE power industry experts bring their combined experience in the electrical power industry...
Michael Heinsdorf, P.E., LEED AP, CDT is an Engineering Specification Writer at ARCOM MasterSpec.