Industrial Process Control

Forget Laplace Transforms…

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

Control in the “Real World”

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

Why Do Control?

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)

Safety Considerations

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.

Achieving Optimal Operation

Improvements to this temp control strategy resulted in a steam savings of $260K/yr, or $1.1M NPV

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

Achieving Optimal Operation (2)

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

Advanced Control

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

The Key to Process Control

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?

The Key to Process Control (2)

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

The Key to Process Control (3)

ExampleDistillation Control

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

Understanding the Concepts

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

“Composition” Control

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?

Determine Control Objectives

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

Designing the Control Strategy

Steam

FFC

LC

LC

TCTray 8

PC

FC

LC

PC

LC

VACUUM LINE

TOHEADER

CONDENSATE

FC

LC

CONDENSATE

FEED

600 PSIGSTEAM

REFLUXRATIOTARGET

LC

REFLUX DRUM

HOTCONDENSER

FI

FY

PRODUCT

HC

PC

LC

TO REACTORS

FC

FC

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…

Testing the Strategy 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

LC

TCTray 8

Feed Rate Disturbance (1)

“Tray 8 – to – Steam” Control Strategy

Feed Rate Disturbance (2)

“Tray 42 – to – Reflux” Control Strategy

Feed Composition Disturbance

Double-Ended Temperature Control Strategy

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)

Implementing the Control Strategy

ApplicationCapital Project Involvement

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?)

Collaboration with Design Engineer

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.

Other Project Involvement

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

Conclusion

• Next Lecture – March 22– Integration of design and control part I– SSLW 322-340

• Seminar Tomorrow – March 21, 3:00 PM– Dr. Andreas Linninger, University of Illinois

Chicago– Biomedical Engineering Problem Solving with

Systems Engineering Methods

– Reception in ChemE Conference Room at 2:30 PM

– Seminar in McMillan Auditorium 3:00 PM– Please, please attend if at all possible!

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