Advanced Process Control
Training Presentation
Lee Smith
March 29, 2006
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
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
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
Management
Objectives
Key process
variables
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.
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 variability—plants to be operated at designed capacity
– operating at true and optimal process constraints—controlled 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
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
APC Benefits 
Optimum
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   Methodology 
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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
Basic Control
Controlled variable: temperature (desired output)
Input variable: temperature (measured by thermometer in theromostat)
Setpoint: user-defined desired setting (temperature)
Manipulated variable: natural gas valve to furnace (subject to control)
Furnace
natural
gas
House is too cold
furnace turns on
heats the house
is temperature
below setpoint?
Thermostat Controller
recognized the house is too cold
sends signal to the furnace to turn on
and heat the house
house temperature
measured
setpoint = 72°F
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
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.
Goals of PID Control
•
•
•
•
Quickly respond to changes in setpoint
Stability of control
Dampen oscillation
Problems:
– Deadtime—lag in system response to changes
in setpoint
– Deadtime can cause significant instability into
the system controlled
PI Control Example
I = 1.4 gives the best response: quickly brings
controller to setpoint without oscillation
PI Control Example
I = 0.6 gives the best response
I = 1.1 borders on instability
PID Control Example
I = 0.6 gives the best response
I = 1.2 & 1.4 unstable
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
Feedforward Control
Furnace
natural
gas
Window is open
furnace turns on
heats the house
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.
house temperature
is currently OK
Decrease
setpoint to turn
furnace on
Pre-emptive move
to prevent house from
getting cold
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
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
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
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
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
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
Distillation Tower APC Results
APC Application in Wafer Fab
APC Applied to a High-Mix, High-Volume Wafer Fab
Before APC
After APC
12% Capacity
2% Probe Yield
Improvement
Improvement
Wafers/month [1]
45,000
50,400
50,400
Die/water
5000
5000
5000
Die revenue ($/die )[2]
0.07
0.07
0.07
Process Yield (%)
95
95
95
Multiprobe Yield (%) [3]
90
90
92
Revenue/wafer ($)
299
299
306
Revenue/month ($)
13,466,250
15,082,200
15,417,360
Increase in Revenues/month ($)
-1,615,950
335,160
Total increase in revenue due to implementation of APC: $1,951,110/month or $23,413,320/year
Notes:
1. Capacity improvement due to reduced equipment downtime and reduced time running test
wafers. Reduction in test wafer expenses is typically 2-4%.
2. Based on good die in wafer form; potential value of die once packaged and tested is typically 5x.
3. Yield improvement due to improved parametric process control, Cpk.
Source: Carl Fiorletta, “Capabilities and Lessons from 10 Years of APC Success,” Solid State Technology,
February 2004, pg 67-70.
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
Exercise in PID Control
Questions:
1.
Set Deadtime = 0
a.
b.
2.
Set Deadtime = 1
a.
b.
c.
d.
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)?
Set Deadtime = 3
a.
b.
c.
d.
4.
5.
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?
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)?
How does increasing the deadtime affect the capability of the controller?
What control schemes are available to optimize controller capability?
160
140
CONTROLLER OUTPUT
120
100
80
60
40
20
0
1
3
5
7
9
11
13
15
17
19
21
23
25
27
29
CONTROLLER ITERATION
Proportional
Integral
Deadtime
SP
0.4
0.9
2
100
SUM OF EFFORS = 610
31
33
35
37
39
41
43
45
47
49
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
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.
Useful Websites
• http://www.onesmartclick.com/engineering/chemical-processcontrol.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_control