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Forget Laplace Transforms….  Industrial process control involves a lot more than just Laplace transforms and loop tuning  Combination of both theory.

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Presentation on theme: "Forget Laplace Transforms….  Industrial process control involves a lot more than just Laplace transforms and loop tuning  Combination of both theory."— Presentation transcript:

1 Forget Laplace Transforms…

2  Industrial process control involves a lot more than just Laplace transforms and loop tuning  Combination of both theory and practice  Understanding of core engineering principles is key (thermodynamics, mass transfer, etc)  Control design requires collaboration with others to understand objectives and provide process design guidance  Importance of both “big picture” and details

3  Maintain the process at the desired state or set of conditions – “keep it out of the ditch” ◦ Safety  Ensure the process conditions minimize risk ◦ Optimal operation  Running at the appropriate operating conditions improves quality, yield, plant capacity, energy consumption, etc ◦ Recover from upsets or disturbances  It’s not just about optimization; it’s about successful operation of the entire plant

4  A primary objective of the process control system is to keep the process running at the desired operating conditions ◦ Presumably these conditions have been chosen appropriately from a safety standpoint (hint, hint, design engineer )  “Cruise control” ◦ The basic process control system should be able to handle many disturbances, but not all ◦ Cruise control on your car can handle hills and curves, but if there’s an accident ahead, you’ll have to stop the car yourself  Safety Instrumented Systems (interlocks)

5  A good process control system will keep the process running stably, even when hit with disturbances or upsets  This results in better efficiency, higher capacity, etc. Improvements to this temp control strategy resulted in a steam savings of $260K/yr, or $1.1M NPV

6  Running at the optimal operating conditions can maximize production rate and yield, improve energy consumption, and is crucial for product quality  However, these objectives often compete ◦ Best product quality may be attained at the cost of additional energy consumption  Advanced Control techniques can help with balancing this tradeoff

7  Advanced control applications provide an additional layer of control, to meet a variety of control objectives ◦ Feed-back composition control based on lab data ◦ Feed-forward to other unit operations or plant areas ◦ Perform complicated online calculations and close the loop to manipulated variables ◦ Plant-wide supervisory control strategies can balance rates, maximize throughput, minimize conversion costs or energy consumption… ◦ Model Predictive Control (MPC) incorporates a process model to optimize operation when there are multiple input, output, and disturbance variables

8 “You’re a chemical engineer first and foremost!”

9  If you truly understand the chemical principles at work in the process, then controlling it is easy! ◦ Or easier, at least…  You have to understand the fundamental stuff that’s going on in order to determine: ◦ What the control objectives are in the first place, and which variables should be controlled ◦ What your “control knobs” are and how they will affect the process as a whole – how it all fits together  If you increase the steam flow to a distillation column’s reboiler, what will happen to the composition on tray 15? What about the distillate? What about the pressure profile?

10  Another way to think about it: the goal is to move variability to some place where you don’t care about it ◦ If the temperature in a reactor cycles or varies, that’s bad ◦ We can control this temperature (keep it stable) by implementing a control loop which manipulates steam flow to the reactor jacket  Who cares if the steam flow moves around? The reactor temperature is constant, and that’s what we want.  Comes back to fundamental process understanding ◦ Must understand where variability is acceptable, and where it’s not ◦ Must understand how everything fits together

11 Distillation Control

12  Need to understand manipulated variables (“control knobs”) available to us  Chemical Engineering knowledge tells us… ◦ Increasing the reflux will help purify the distillate ◦ The hotter the base, the more material will boil overhead  the entire composition profile will shift ◦ The dynamics of liquid effects vs. vapor effects are very different ◦ The temperature on each tray is a function of the tray’s composition and pressure

13  In order to maintain the desired top and bottom compositions, it is important to prevent the composition profile from moving  The temperature profile of a column is indicative of the composition profile ◦ By selecting the right temperature to control, we can actually peg the entire temperature profile ◦ The appropriate temperature control strategy (tray location, manipulated variable, etc) is highly dependent on the individual column design

14  Manage inventory ◦ Need to ensure there is always reflux “available” ◦ Likewise, need sufficient holdup in the column base  Maintain desired product compositions ◦ What are acceptable impurity ranges? ◦ Is one product stream more important?  Other objectives ◦ Pressure control, column loading, minimize steam  Respond to certain upsets ◦ What process upsets is this column likely to see?

15  First, obtain or develop a steady-state model ◦ Need to know target compositions, normal flows, pressures, the column’s temperature profile, etc. ◦ This gives you a snapshot of the desired operation ◦ A steady-state model also yields insight on the “control knobs”  Next, pair controlled variables with manipulated variables ◦ Based on “Chemical Engineering” knowledge ◦ Utilizing information regarding key control objectives and predicted disturbances

16 Steam FFCLC TC Tray 8

17 PC FC LC PC LC VACUUM LINE TO HEADER CONDENSATE FC LC CONDENSATE FEED 600 PSIG STEAM REFLUX RATIO TARGET LC REFLUX DRUM HOT CONDENSER FI FY PRODUCT HC PC LC TO REACTORS FC XC SGI FI TI IX COMPOSITION And more… Plant-wide supervisory control Feed-forward to other unit ops or plant areas Model predictive control (MPC) And so on…

18  Beneficial to create a dynamic simulation of the column using this control strategy ◦ Allows for testing of the strategy under various disturbance scenarios ◦ Gives valuable information regarding dynamic behavior of the column ◦ Provides initial tuning data Steam FFC LC TC Tray 8

19 “Tray 8 – to – Steam” Control Strategy

20 “Tray 42 – to – Reflux” Control Strategy

21 Double-Ended Temperature Control Strategy

22  Once the control strategy framework has been laid out, then you get into the “nuts and bolts” of configuration ◦ Algorithm type ◦ Controller action ◦ Tuning (gain, time constants, etc)

23 Capital Project Involvement

24  For each unit operation, work closely with design engineer and other project/operations representatives to… ◦ Understand design intent, including steady-state flows, desired recoveries, conversions, etc. ◦ Gain insight on potential process disturbances ◦ Define key control objectives ◦ Provide guidance on the actual process design  Determine residence times required for stable operation  Specify instrumentation placement  Other recommendations based on dynamic simulation and other analysis (is desired steady-state operation feasible?)

25  Provide guidance on plant-wide control ◦ Decouple interactions as much as possible ◦ Control valve placement, piping layouts ◦ Inventory management  Instrumentation selection  Safety considerations, interlocks  “Control Narrative” ◦ Detailed document describing control objectives and strategies for each unit operation, the plan for managing inventory plant-wide, etc.

26  Remember: always think about process control from the perspective of Chemical Engineering fundamentals  Understand your process, as well as your control objectives ◦ What needs to be controlled? Which variables effect each other (and how)? Where does variability hurt you most? Etc.  Remember there’s a dynamic component  Think about control early in design phase

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