Advanced Process Control Training Presentation

1. Advanced Process ControlTraining Presentation Lee Smith March 29, 2006

2. 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

3. 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

4. Management Objectives Key process variables 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

5. 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.

6. 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

7. 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

9. 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

10. 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 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 = 72°F

11. 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

12. 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.

13. 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

14. PI Control Example I = 1.4 gives the best response: quickly brings controller to setpoint without oscillation

15. PI Control Example I = 0.6 gives the best response I = 1.1 borders on instability

16. PID Control Example I = 0.6 gives the best response I = 1.2 & 1.4 unstable

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. Distillation Tower Example • Simple distillation column with APC • Column objective is to remove pentanesand lighter componentsfrom 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

25. Distillation Tower APC Results

26. 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.

27. 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

28. 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?

30. 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

31. 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.

32. 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