In the ISA Mentor Program, I am providing guidance for extremely talented individuals from countries such as Argentina, Brazil, Malaysia, Mexico, Saudi Arabia, and the USA. This question comes from Damien Hurley.
Damien Hurley is a control and instrumentation (C&I) engineer for Fluor in the UK. He is currently involved in the detailed design phase of a project to build a new energy plant in an existing refinery in Scotland. His chief responsibility is C&I interface coordinator with construction, the existing site C&I contractor and the client.
Damien Hurley’s Question
How can I begin implementing process simulations in my learning? My background is in drone control where all learning has a significant emphasis on simulation and testing, usually via programs such as MATLAB. Upon starting in the oil and gas engineering, procurement and construction (EPC) industry, I began getting to grips with the wide array of final elements and my knowledge of Process simulation has suffered as a result.
I’m also not exposed to simulations on a daily basis, as I was previously in the unmanned aerial vehicle (UAV) industry. How can I get started with simulation again? Specifically is the simulation of processes relevant to our industry? Can you point me in the direction of a good resource to begin getting to grips with this worthwhile subject?
Greg McMillan’s Answer
Dynamic simulation is the key to most of deep learning and significant innovation in my 50-year career. Simulation has played a big role in industrial processes, especially in refining and energy plants. There are a lot of basic and advanced modeling objects for the unit operations in these plants. You can learn a lot about what process inputs and parameters are important in the building of first principle models. Even if the simulations are built for you, the practice of changing process inputs and seeing the effect on process outputs is a great learning experience. You are free to experiment and see results where your desire to learn is the main limit.
You can also learn a lot about what affects process control. Here it is critical to include all of the automation system dynamics often ignored in the literature despite most often being the biggest source of control loop dead time with also a significant contributing effect to the open loop gain and nonlinearity by way of the installed flow characteristic of control valves and variable frequency drives (VFDs).
You need to add variable filter times to simulate sensors particularly thermowell and electrode lags, transmitter damping, and signal filters. You need to add variable dead time blocks to simulate transportation delays associated with injection of manipulated fluids into the unit operation and to the sensor for measurement of the controlled variables. The variable deadtime block is also needed for simulating the effect of positioners with poor sensitivity where the response time increases by two orders of magnitude for changes in signal less than 0.25 percent. You need backlash-stiction blocks to simulate the deadband and resolution limits of control valves as detailed in the Control article How to specify control valves and positioners that don’t compromise control.
VFDs can have a surprisingly large deadband introduced in the setup in a misguided attempt to reduce reaction to noise and a resolution limit caused by an 8-bit signal input card. You also need to add rate of change limits to model slewing rates for large control valves and introduced in the VFD setup in a misguided attempt to reduce motor overload instead of properly sizing the motor. You need software that will provide PID tuning settings with proper identification of total loop dead time. Finally, a performance metrics block to identify the integrated and peak error for load disturbances and the rise time, overshoot, undershoot, and settling time for disturbances is a way of judging how well you are doing.
A couple of years ago I helped develop a dynamic simulation of the control system and the many headers, boilers, and users at a large plant to optimize the cogeneration and minimize the disruption to the steam system from large changes in the steam use and generation in all the headers for the whole plant. ISA Mentor Program resource James Beall and protégé Syed Misbahuddin were part of the team. Over 30 feedforward and decouple signals were developed and thoroughly tested by dynamic simulation resulting in a smooth implementation of much more efficient and safe system. I learned via the simulation in one case that the feedforward I thought was needed for a boiler caused more harm than good due to changes in header pressure preceding the supposedly proactive feedforward to a header letdown valve to compensate for the effect of a change in firing rate demand.
First principle process models material and energy balances of volumes in series can model the many unanticipated changes. I recently was alerted to the fact that the use of a bypass valve around a heat exchanger provides first a fast response from a change in flow bypassing and going through the exchanger but is followed by a delayed response in the opposite direction caused by the same utility flow rate heating or cooling a different flow rate through the exchanger. Unless a feedforward changes the utility flow, the tuning of the PID for temperature of the blended stream must not overreact to the initial temperature change.
Often there are leads besides lags in the temperature response associated with inline temperature control loops for jackets. For heat exchangers in a recirculation line for a volume, the self-regulating response of the exchanger outlet temperature controller is followed by a slow integrating response from recirculation of the changes in the volume temperature. Also, feedforward signals that arrive too soon can create an inverse response or that arrive too late create a second disturbance that makes control worse than the original feedback control. Getting the dynamics right by inclusion of automation besides process dynamic is critical.
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We learn the most by our mistakes. To avoid the price of making them in field, we can use dynamic simulation as a safe way of hands-on learning for exploration and prototyping of existing and new systems finding good and bad effects that offers much more flexibility and is non-intrusive to the process. Dynamic models using the digital twin enables a deeper process understanding to be gained and used to make much more intelligent automation. See the Control Talk blog Simulation breeds innovation for an insightful history and future of opportunities for a safe sandbox allowing creativity by synergy of process and automation system knowledge.
Often simulation fidelity is simply stated as low, medium or high. I prefer defining at least five levels as seen below in the chapter Tip #98: How to Achieve Process Simulation Fidelity in the ISA book 101 Tips for a Successful Automation Career. Note that the term “virtual plant” I have been using for decades should be replaced with the term “digital twin” in my books and articles prior to 2018 to be in tune with the terminology for digitalization and digital transformation.
- Fidelity Level 1: measurements can match setpoints and respond in the proper direction to loop outputs; for operator training.
- Fidelity Level 2: measurements can match setpoints and respond in the proper direction when control and block valves open and close and prime movers (e.g., pumps, fans, and compressors) start and stop; for operator training.
- Fidelity Level 3: loop dynamics (e.g., process gain, time constant, and deadtime) are sufficiently accurate to tune loops, prototype process control improvements, and see process interactions; for basic process control demonstrations.
- Fidelity Level 4: measurement dynamics (e.g., response to valves, prime movers, and disturbances) are sufficiently accurate to track down and analyze process variability and quantitatively assess control system capability and improvement opportunities; for rating control system capability, and conducting control system research and development.
- Fidelity Level 5: process relationships and metrics (e.g., yield, raw material costs, energy costs, product quality, production rate, production revenue) and process optimums are sufficiently accurately modeled for the design and implementation of advanced control, such as model predictive control (MPC) and real time optimization (RTO), and in some cases virtual experimentation.
A lot of learning is possible by using Fidelity Levels 3 models. Fidelity Level 4 and 5 simulations with advanced modeling objects are generally needed for complex unit operations where components are being separated or formed, such as biological and chemical reactors and distillation columns, or to match the dynamic response of trajectories to detail advanced process control including PID control that involves feedforwards, decouplers, and state based control. Developing and testing inferential measurements, data analytics, performance metrics, and MPC and RTO applications, generally requires Level 5.
In all cases I recommend a digital twin that has the blocks addressing nearly every type of automation system dynamics and metrics often neglected in dynamic simulation packages. The digital twin should have the same PID Form, Structure and options used in the process industry and a tool like the Mimic Rough-n-Ready tuner to get started with reasonable PID tuning settings.
Many software packages that were not developed by automation professionals may unfortunately seriously mess you up by not having the many sources of dead time, lags, and nonlinearities, and by employing a PID with a Parallel (Independent) Form working in engineering units instead of percent signals. A fellow protégé also in the UK who is now an automation engineer at Phillips 66 can relate his experiences in using Mimic software. If you pursue this dynamic simulation opportunity, we can do articles and Control Talk blogs together to share the understanding gained to help advance our profession.
For Additional Reference:
McMillan, Gregory K., and Vegas, Hunter, 101 Tips for a Successful Automation Career.
Additional Mentor Program Resources
See the ISA book 101 Tips for a Successful Automation Career that grew out of this Mentor Program to gain concise and practical advice. See the InTech magazine feature article Enabling new automation engineers for candid comments from some of the original program participants. See the Control Talk column How to effectively get engineering knowledge with the ISA Mentor Program protégée Keneisha Williams on the challenges faced by young engineers today, and the column How to succeed at career and project migration with protégé Bill Thomas on how to make the most out of yourself and your project. Providing discussion and answers besides Greg McMillan and co-founder of the program Hunter Vegas (project engineering manager at Wunderlich-Malec) are resources Mark Darby (principal consultant at CMiD Solutions), Brian Hrankowsky (consultant engineer at a major pharmaceutical company), Michel Ruel (executive director, engineering practice at BBA Inc.), Leah Ruder (director of global project engineering at the Midwest Engineering Center of Emerson Automation Solutions), Nick Sands (ISA Fellow and Manufacturing Technology Fellow at DuPont), Bart Propst (process control leader for the Ascend Performance Materials Chocolate Bayou plant), Angela Valdes (automation manager of the Toronto office for SNC-Lavalin), and Daniel Warren (senior instrumentation/electrical specialist at D.M.W. Instrumentation Consulting Services, Ltd.).