UNICOS : UNified Industrial COntrol System CPC (Continuous Process Control) SCADA
Industrial Process Control
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Transcript of Industrial Process Control
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