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.
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.
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.”
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.”
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Before the calendar turned, 2016 already had the makings of a pivotal year for manufacturing, and for the world.
There were the big events for the year, including the United States as Partner Country at Hannover Messe in April and the 2016 International Manufacturing Technology Show in Chicago in September. There's also the matter of the U.S. presidential elections in November, which promise to shape policy in manufacturing for years to come.
But the year started with global economic turmoil, as a slowdown in Chinese manufacturing triggered a worldwide stock hiccup that sent values plummeting. The continued plunge in world oil prices has resulted in a slowdown in exploration and, by extension, the manufacture of exploration equipment.
Read more: 2015 Salary Survey