In- APC Training Presentation

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Advanced Process Control

Training Presentation

Lee SmithMarch 29, 2006



• 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

Contents



• 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





Key process

variables

Management

Objectives

• 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



• 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.

How Can APC Be Used?



• Production quality can be controlled and optimized tomanagement 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

Advantages and Benefits



• Implementation of an APC system is time consuming, costly andcomplex

– 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 toimplementation

• 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

Limitations



%

yMethodolog

ProjectAPC

of yReliabilit

%

Team

tionImplementa

of Expertise

%

BenefitsCapture

toTechnology

of Capability

OperationCurrent-Optimum

BenefitsAPC



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

House is too coldFurnace

Thermostat Controller

recognized the house is too cold

sends signal to the furnace to turn on and heat the house

furnace turns on

heats the house

Is the house too cold?

yes



Basic Control

House is too coldFurnace

Thermostat Controller

recognized the house is too cold

sends signal to the furnace to turn on

and heat the house

furnace turns on

heats the housenatural

gas

house temperature

measured

is temperature

below setpoint?

setpoint = 72°F

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)



• 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 referenceand the output and determines the error e

• Controller C changes the inputs u to Process under

control P by the amount of error e

Feedback Control Theory



• Error is found by subtracting the measured quantity from the setpoint.

• Proportional - To handle the present, the error is multiplied by a negativeconstant 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 controlledquantity. I finds the process output's average error from the setpoint.

– A simple proportional system oscillates around the setpoint, because there'snothing to remove the error. By adding a negative proportion of the average errorfrom 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 controlledquantity. The larger this derivative term, the more rapidly the controllerresponds to changes in the process output.

– The D term dampens a controller's response to short term changes.

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

Goals of PID Control



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 responseI = 1.1 borders on instability



PID Control Example

I = 0.6 gives the best responseI = 1.2 & 1.4 unstable



• 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 outputvariables almost impossible

– Most processes are non-linear and changeaccording to the state of the process

• Control loops are local

Limitations of Feedback Control



Feedforward Control

Window is openFurnace

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.

furnace turns on

heats the housenatural

gas

house temperature

is currently OKturn on furnace

Decrease

setpoint to turnfurnace on

Pre-emptive move

to prevent house from

getting cold



• Feedforward control avoids slowness of feedbackcontrol

• Disturbances are measured and accounted for beforethey have time to affect the system

– In the house example, a feedforward system measured thefact 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 ofdisturbances must be perfectly predicted

– There must not be any surprise effects of disturbances

Feedforward Control



• 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 theycan affect the system

Combined Feedforward/Feedback



• Most complex processes have many variables that haveto be regulated

• To control multiple variables, multiple control loopsmust 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 controlstrategies applied to all control loops simultaneously

Multivariable 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-basedcontrol

Internal Model-Based Control



• Inputs to advanced control systems require accurate, clean andconsistent process data – ―garbage in garbage out‖

• Many key product qualities cannot be measured on-line butrequire 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 andcondition process measurement

• With many variables to manipulate, control strategy and design iscritical to limit control loop interaction

Important Data Issues



• 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 manipulatingsteam (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 processvariables to compensate

Distillation Tower Example



Distillation Tower APC Results



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.

APC Applied to a High-Mix, High-Volume Wafer FabBefore APC After APC

12% Capacity

Improvement

2% Probe Yield

Improvement

Wafers/month [1] 45,000 50,400 50,400

Die/water 5000 5000 5000Die 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 53. Yield improvement due to improved parametric process control, Cpk.



• To give a better understanding concerning problemsencountered 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 incontroller output versus setpoint after 50 iterations

– Deadtime is the process delay in observing an output responseto the controller input

– SP is the setpoint change

Exercise in PID Control



Questions:

1. Set Deadtime = 0a. With P = 0.4, what is the optimal I to obtain the optimal controller response (minimum Sum of Errors)?

b. With P = 1.0, what is the optimal I to obtain the optimal controller response?

2. Set Deadtime = 1

a. With P = 0.4, what is the optimal I to obtain the optimal controller response? b. With P = 1.0, what is the optimal I to obtain the optimal controller response?

c. What are the optimum values for P and I to obtain the optimal controller response?

d. Is the controller always stable (are there values of P and I that make the controller response unstable)?

3. Set Deadtime = 3a. With P = 0.4, what is the optimal I to obtain the optimal controller response?

b. With P = 1.0, what is the optimal I to obtain the optimal controller response?

c. What are the optimum values for P and I to obtain the optimal controller response?

d. 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?

Exercise in PID Control



Proportional 0.4 SUM OF EFFORS = 610

Integral 0.9

Deadtime 2

SP 100

0

20

40

60

80

100

120

140

160

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

CONTROLLER ITERATION

C O N T R O L L E R O U T P U



• Local PID controllers only concerned with optimizingresponse 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 variationand 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

Summary



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-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_control

Documents

In- APC Training Presentation