Advanced Process Control Training Presentation

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Advanced Process ControlTraining Presentation

• Lee Smith
• March 29, 2006

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Contents

• Advanced Process Control (APC) Defined
• Applications, Advantages Limitations
• Basic Process Control Discussed
• Feedback Control
• Feedforward Control
• Advanced Process Control Discussed
• Real World Examples
• Process Control Exercise (PID Control)
• Summary
• Readings List

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Advanced Process Control

• State-of-the-art in Modern Control Engineering
• Appropriate for Process Systems and Applications
• APC systematic approach to choosing relevant
  techniques and their integration into a
  management and control system to enhance
  operation and profitability

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Advanced Process Control

• APC is a step beyond Process Control
• Built on foundation of basic process control
  loops
• Process Models predict output from key process
  variables online and real-time
• Optimize Process Outputs relative to quality and
  profitability goals

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How Can APC Be Used?

• APC can be applied to any system or process where
  outputs can be optimized on-line and in real-time
• Model of process or system exist or can be
  developed
• Typical applications
• Petrochemical plants and processes
• Semiconductor wafer manufacturing processes
• Also applicable to a wide variety of other
  systems including aerospace, robotics, radar
  tracking, vehicle guidance systems, etc.

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Advantages and Benefits

• Production quality can be controlled and
  optimized to management constraints
• APC can accomplish the following
• improve product yield, quality and consistency
• reduce process variabilityplants to be operated
  at designed capacity
• operating at true and optimal process
  constraintscontrolled variables pushed against a
  limit
• reduce energy consumption
• exceed design capacity while reducing product
  giveaway
• increase responsiveness to desired changes
  (eliminate deadtime)
• improve process safety and reduce environmental
  emissions
• Profitability of implementing APC
• benefits ranging from 2 to 6 of operating costs
  reported
• Petrochemical plants reporting up to 3 product
  yield improvements
• 10-15 improved ROI at some semiconductor plants

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Limitations

• Implementation of an APC system is time
  consuming, costly and complex
• May require improved control hardware than
  currently exists
• High level of technical competency required
• Usually installed and maintained by vendors
  consultants
• Must have a very good understanding of process
  prior to implementation
• High training requirements
• Difficult to use and operate after implementation
• Requires large capacity operations to justify
  effort and expense
• New APC applications more difficult, time
  consuming and costly
• Off-the-shelf APC products must be customized

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What is Basic Process Control?

• Process control loop control component monitors
  desired output results and changes input
  variables to obtain the result.
• Example thermostat controller

Furnace
House is too cold
furnace turns on heats the house
Is the house too cold?
yes
Thermostat Controller recognized the house is too
cold sends signal to the furnace to turn on and
heat the house
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Basic Control
Controlled variable temperature (desired
output) Input variable temperature (measured by
thermometer in theromostat) Setpoint
user-defined desired setting (temperature) Manipul
ated variable natural gas valve to furnace
(subject to control)
Furnace
House is too cold
furnace turns on heats the house
natural gas
house temperature measured
is temperature below setpoint?
Thermostat Controller recognized the house is too
cold sends signal to the furnace to turn on and
heat the house
setpoint 72F
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Feedback Control Theory

• Output of the system y(t) is fed back to the
  reference value r(t) through measurement of a
  sensor
• Controller C takes the difference between the
  reference and the output and determines the error
  e
• Controller C changes the inputs u to Process
  under control P by the amount of error e

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PID Control

• Error is found by subtracting the measured
  quantity from the setpoint.
• Proportional - To handle the present, the error
  is multiplied by a negative constant P and added
  to the controlled quantity.
• Note that when the error is zero, a proportional
  controller's output is zero.
• Integral - To handle the past, the error is
  integrated (added up) over a time period,
  multiplied by a negative constant I and added to
  the controlled quantity. I finds the process
  output's average error from the setpoint.
• A simple proportional system oscillates around
  the setpoint, because there's nothing to remove
  the error. By adding a negative proportion of the
  average error from the process input, the average
  difference between the process output and the
  setpoint is always reduced and the process output
  will settle at the setpoint.
• Derivative - To handle the future, the first
  derivative (slope) of the error is calculated,
  multiplied by negative constant D, and added to
  the controlled quantity. The larger this
  derivative term, the more rapidly the controller
  responds to changes in the process output.
• The D term dampens a controller's response to
  short term changes.

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Goals of PID Control

• Quickly respond to changes in setpoint
• Stability of control
• Dampen oscillation
• Problems
• Deadtimelag in system response to changes in
  setpoint
• Deadtime can cause significant instability into
  the system controlled

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PI Control Example
I 1.4 gives the best response quickly brings
controller to setpoint without oscillation
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PI Control Example
I 0.6 gives the best response I 1.1 borders
on instability
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PID Control Example
I 0.6 gives the best response I 1.2 1.4
unstable
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Limitations of Feedback Control

• Feedback control is not predictive
• Requires management or operators to change set
  points to optimize system
• Changes can bring instability into system
• Optimization of many input and output variables
  almost impossible
• Most processes are non-linear and change
  according to the state of the process
• Control loops are local

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Feedforward Control
Furnace
Window is open
furnace turns on heats the house
natural gas
house temperature is currently OK
turn on furnace
Feedforward Recognize window is open and house
will get cold in the future Someone reacts and
changes controller setpoint to turn on the
furnace preemptively.
Decrease setpoint to turn furnace on
Pre-emptive move to prevent house from getting
cold
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Feedforward Control

• Feedforward control avoids slowness of feedback
  control
• Disturbances are measured and accounted for
  before they have time to affect the system
• In the house example, a feedforward system
  measured the fact that the window is opened
• As a result, automatically turn on the heater
  before the house can get too cold
• Difficulty with feedforward control effects of
  disturbances must be perfectly predicted
• There must not be any surprise effects of
  disturbances

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Combined Feedforward/Feedback

• Combinations of feedback and feedforward control
  are used
• Benefits of feedback control controlling
  unknown disturbances and not having to know
  exactly how a system will respond
• Benefits of feedforward control responding to
  disturbances before they can affect the system

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Multivariable Control

• Most complex processes have many variables that
  have to be regulated
• To control multiple variables, multiple control
  loops must be used
• Example is a reactor with at least three control
  loops temperature, pressure and level (flow
  rate)
• Multiple control loops often interact causing
  process instability
• Multivariable controllers account for loop
  interaction
• Models can be developed to provide feedforward
  control strategies applied to all control loops
  simultaneously

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Internal Model-Based Control

• Process models have some uncertainty
• Sensitive multivariate controller will also be
  sensitive to uncertainties and can cause
  instability
• Filter attenuates unknowns in the feedback loop
• Difference between process and model outputs
• Moderates excessive control
• This strategy is powerful and framework of
  model-based control

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Important Data Issues

• Inputs to advanced control systems require
  accurate, clean and consistent process data
• garbage in garbage out
• Many key product qualities cannot be measured
  on-line but require laboratory analyses
• Inferential estimation techniques use available
  process measures, combined with delayed lab
  results, to infer product qualities on-line
• Available sensors may have to be filtered to
  attenuate noise
• Time-lags may be introduced
• Algorithms using SPC concepts have proven very
  useful to validate and condition process
  measurement
• With many variables to manipulate, control
  strategy and design is critical to limit control
  loop interaction

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Distillation Tower Example

• Simple distillation column with APC
• Column objective is to remove pentanes and
  lighter components from bottom naphtha product
• APC input
• Column top tray temperature
• Top and bottom product component laboratory
  analyses
• Column pressures
• Unit optimization objectives
• APC controlled process variables
• Temperature of column overhead by manipulating
  fuel gas control valve
• Overhead reflux flow rate
• Bottom reboiler outlet temperature by
  manipulating steam (heat) input control valve
• Note that product flow rates not controlled
• Overhead product controlled by overhead drum
  level
• Bottoms product controlled by level in the tower
  bottom
• APC anticipates changes in stabilized naphtha
  product due to input variables and adjusts
  relevant process variables to compensate

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Distillation Tower APC Results
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APC Application in Wafer Fab
Source Carl Fiorletta, Capabilities and
Lessons from 10 Years of APC Success, Solid
State Technology, February 2004, pg
67-70.
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Exercise in PID Control

• To give a better understanding concerning
  problems encountered in typical control schemes
• Use embedded excel spreadsheet on next slide to
  investigate response to a change in set point
• Double click on graph to open
• Graph shows controller output after a maximum of
  50 iterations
• Simulates the response of PI (proportional
  integral) controller
• Performance of control parameter given by sum of
  errors in controller output versus setpoint after
  50 iterations
• Deadtime is the process delay in observing an
  output response to the controller input
• SP is the setpoint change

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Exercise in PID Control

• Questions
• 1. Set Deadtime 0
• With P 0.4, what is the optimal I to obtain the
  optimal controller response (minimum Sum of
  Errors)?
• With P 1.0, what is the optimal I to obtain the
  optimal controller response?
• 2. Set Deadtime 1
• With P 0.4, what is the optimal I to obtain the
  optimal controller response?
• With P 1.0, what is the optimal I to obtain the
  optimal controller response?
• What are the optimum values for P and I to obtain
  the optimal controller response?
• Is the controller always stable (are there values
  of P and I that make the controller response
  unstable)?
• 3. Set Deadtime 3
• With P 0.4, what is the optimal I to obtain the
  optimal controller response?
• With P 1.0, what is the optimal I to obtain the
  optimal controller response?
• What are the optimum values for P and I to obtain
  the optimal controller response?
• Is the controller always stable (are there values
  of P and I that make the controller response
  unstable)?
• 4. How does increasing the deadtime affect
  the capability of the controller?
• 5. What control schemes are available to
  optimize controller capability?

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Summary

• Local PID controllers only concerned with
  optimizing response of one setpoint in one
  variable
• APC manipulates local controller setpoints
  according to future predictions of embedded
  process model
• Hierarchal and multiobjective controller
  philosophy
• Optimizes local controller interactions and
  parameters
• Optimized to multiple economic objectives
• Benefits of APC ability to reduce process
  variation and optimize multiple variables
  simultaneously
• Maximize the process capacity to unit constraints
• Reduce quality giveaway as products closer to
  specifications
• Ability to offload optimization responsibility
  from operator

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Recommended References

• Camacho E F Bordons C, Model Predictive
  Control, Springer, 1999.
• Dutton K, Thompson S Barraclough B, The Art of
  Control Engineering, Addison Wesley, 1997.
• Marlin T, Process Control Designing Processes
  and Control Systems for Dynamic Performance,
  McGraw Hill, 1995.
• Ogunnaike B A Ray W H, Process Dynamics,
  Modelling and Control, Oxford University Press,
  1994.

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Useful Websites

• http//www.onesmartclick.com/engineering/chemical-
  process-control.html
• http//www.aspentech.com/
• http//www.apc-network.com/apc/default.aspx
• http//www.hyperion.com.cy/EN/services/process/apc
  .html
• http//ieee-ias.org/
• http//en.wikipedia.org/wiki/Advanced_process_cont
  rol