Tuning and control loop performance /

Tuning and control loop performance /

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The proportional-integral-derivative (PID) controller is the heart of every control system in the process industry. Given the proper setup and tuning, the PID has proven to have the capability and flexibility needed to meet nearly all of industry's basic control requirements. However, the infor...

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Bibliographic Details
Main Author: McMillan, Gregory K., 1946- (Author)
Format: Electronic eBook
Language:English
Published: New York, [New York] (222 East 46th Street, New York, NY 10017) : Momentum Press, 2015.
Edition:Fourth edition.
Series:Manufacturing and engineering collection.
Subjects:
Process control.
Feedback control systems.
adaptive control
advanced regulatory control
analyzer response
auto tuner
automation system
batch optimization
bioreactor control
cascade control
compressor control
control loop performance
control valve response
external reset feedback
feedforward control
inverse response
lambda tuning
level control
measurement response
pH control
PID control
PID execution rate
PID filter
PID form
PID structure
PID tuning
pressure control
process control
process disturbances
process dynamics
process interaction
process metrics
process nonlinearity
process performance
process response
proportional-integral-derivative controller
reactor control
runaway reaction
temperature control
valve deadband
valve position control
valve resolution
variable frequency drive response
wireless control
wireless response
Online Access:An electronic book accessible through the World Wide Web; click to view
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Table of Contents:
  • 1. Fundamentals
  • 1.1 Introduction
  • 1.1.1 Perspective
  • 1.1.2 Overview
  • 1.1.3 Recommendations
  • 1.2 PID controller
  • 1.2.1 Proportional mode
  • 1.2.2 Integral mode
  • 1.2.3 Derivative mode
  • 1.2.4 ARW and output limits
  • 1.2.5 Control action and valve action
  • 1.2.6 Operating modes
  • 1.3 Loop dynamics
  • 1.3.1 Types of process responses
  • 1.3.2 Dead times and time constants
  • 1.3.3 Open loop self-regulating and integrating process gains
  • 1.3.4 Deadband, resolution, and threshold sensitivity
  • 1.4 Typical mode settings
  • 1.5 Typical tuning methods
  • 1.5.1 Lambda tuning for self-regulating processes
  • 1.5.2 Lambda tuning for integrating processes
  • 1.5.3 IMC tuning for self-regulating processes
  • 1.5.4 IMC tuning for integrating processes
  • 1.5.5 Skogestad internal model control tuning for self-regulating processes
  • 1.5.6 SIMC tuning for integrating processes
  • 1.5.7 Traditional open loop tuning
  • 1.5.8 Modified Ziegler-Nichols reaction curve tuning
  • 1.5.9 Modified Ziegler-Nichols ultimate oscillation tuning
  • 1.5.10 Quarter amplitude oscillation tuning
  • 1.5.11 SCM tuning for self-regulating processes
  • 1.5.12 SCM tuning for integrating processes
  • 1.5.13 SCM tuning for runaway processes
  • 1.5.14 Maximizing absorption of variability tuning for surge tank level
  • 1.6 Test results
  • 1.6.1 Performance of tuning settings on dead time dominant processes
  • 1.6.2 Performance of tuning settings on near-integrating processes
  • 1.6.3 Performance of tuning settings on true integrating processes
  • 1.6.4 Performance of tuning settings on runaway processes
  • 1.6.5 Slow oscillations from low PID gain in integrating and runaway processes
  • 1.6.6 Performance of tuning methods on various processes
  • Key points
  • 2. Unified methodology
  • 2.1 Introduction
  • 2.1.1 Perspective
  • 2.1.2 Overview
  • 2.1.3 Recommendations
  • 2.2 PID features
  • 2.2.1 PID form
  • 2.2.2 External reset feedback
  • 2.2.3 PID structure
  • 2.2.4 Split range
  • 2.2.5 Signal characterization
  • 2.2.6 Feedforward
  • 2.2.7 Decoupling
  • 2.2.8 Output tracking and remote output
  • 2.2.9 Setpoint filter, lead-lag, and rate limits
  • 2.2.10 Enhanced PID for wireless and analyzers
  • 2.3 Automation system difficulties
  • 2.3.1 Open loop gain problems
  • 2.3.2 Time constant problems
  • 2.3.3 Dead time problems
  • 2.3.4 Limit cycle problems
  • 2.3.5 Noise problems
  • 2.3.6 Accuracy and precision problems
  • 2.4 Process objectives
  • 2.4.1 Maximize turndown
  • 2.4.2 Maximize safety and environmental protection
  • 2.4.3 Minimize product variability
  • 2.4.4 Maximize process efficiency and capacity
  • 2.5 Step-by-step solutions
  • 2.6 Test results
  • Key points
  • 3. Performance criteria
  • 3.1 Introduction
  • 3.1.1 Perspective
  • 3.1.2 Overview
  • 3.1.3 Recommendations
  • 3.2 Disturbance response metrics
  • 3.2.1 Accumulated error
  • 3.2.2 Peak error
  • 3.2.3 Disturbance lag
  • 3.3 Setpoint response metrics
  • 3.3.1 Rise time
  • 3.3.2 Overshoot and undershoot
  • Key points
  • 4. Effect of process dynamics
  • 4.1 Introduction
  • 4.1.1 Perspective
  • 4.1.2 Overview
  • 4.1.3 Recommendations
  • 4.2 Effect of mechanical design
  • 4.2.1 Equipment and piping dynamics
  • 4.2.2 Common equipment and piping design mistakes
  • 4.3 Estimation of total dead time
  • 4.4 Estimation of open loop gain
  • 4.5 Major types of process responses
  • 4.5.1 Self-regulating processes
  • 4.5.2 Integrating processes
  • 4.5.3 Runaway processes
  • 4.6 Examples
  • 4.6.1 Waste treatment pH loops (self-regulating process)
  • 4.6.2 Boiler feedwater flow loop (self-regulating process)
  • 4.6.3 Boiler drum level loop (integrating process)
  • 4.6.4 Furnace pressure loop (near-integrating process)
  • 4.6.5 Exothermic reactor cascade temperature loop (runaway process)
  • 4.6.6 Biological reactor biomass concentration loop (runaway process)
  • Key points
  • 5. Effect of controller dynamics
  • 5.1 Introduction
  • 5.1.1 Perspective
  • 5.1.2 Overview
  • 5.1.3 Recommendations
  • 5.2 Execution rate and filter time
  • 5.2.1 First effect via equation for integrated error
  • 5.2.2 Second effect via equations for implied dead time
  • 5.3 Smart reset action
  • 5.4 Diagnosis of tuning problems
  • 5.5 Furnace pressure loop example (near-integrating)
  • 5.6 Test results
  • Key points
  • 6. Effect of measurement dynamics
  • 6.1 Introduction
  • 6.1.1 Perspective
  • 6.1.2 Overview
  • 6.1.3 Recommendations
  • 6.2 Wireless update rate and transmitter damping
  • 6.2.1 First effect via equation for integrated error
  • 6.2.2 Second effect via equations for implied dead time
  • 6.3 Analyzers
  • 6.4 Sensor lags and delays
  • 6.5 Noise and repeatability
  • 6.6 Threshold sensitivity and resolution limits
  • 6.7 Rangeability (turndown)
  • 6.8 Runaway processes
  • 6.9 Accuracy, precision, and drift
  • 6.10 Attenuation and deception
  • 6.11 Examples
  • 6.11.1 Waste treatment pH loop (self-regulating process)
  • 6.11.2 Boiler feedwater flow loop (self-regulating process)
  • 6.11.3 Boiler drum level loop (integrating process)
  • 6.11.4 Furnace pressure loop (near-integrating process)
  • 6.11.5 Exothermic reactor cascade temperature loop (runaway process)
  • 6.11.6 Biological reactor biomass concentration loop (runaway process)
  • 6.12 Test results
  • Key points
  • 7. Effect of valve and variable frequency drive dynamics
  • 7.1 Introduction
  • 7.1.1 Perspective
  • 7.1.2 Overview
  • 7.1.3 Recommendations
  • 7.2 Valve positioners and accessories
  • 7.2.1 Pneumatic positioners
  • 7.2.2 Digital positioners
  • 7.2.3 Current to pneumatic (I/P) transducers
  • 7.2.4 Solenoid valves
  • 7.2.5 Volume boosters
  • 7.3 Actuators, shafts, and stems
  • 7.3.1 Diaphragm actuators
  • 7.3.2 Piston actuators
  • 7.3.3 Linkages and connections
  • 7.4 VFD system design
  • 7.4.1 Pulse width modulation
  • 7.4.2 Cable problems
  • 7.4.3 Bearing problems
  • 7.4.4 Speed slip
  • 7.4.5 Motor requirements
  • 7.4.6 Drive controls
  • 7.5 Dynamic response
  • 7.5.1 Control valve response
  • 7.5.2 VFD response
  • 7.5.3 Dead time approximation
  • 7.5.4 Deadband and resolution
  • 7.5.5 When is a valve or VFD too slow?
  • 7.5.6 Limit cycles
  • 7.6 Installed flow characteristics and rangeability
  • 7.6.1 Valve flow characteristics
  • 7.6.2 Valve rangeability
  • 7.6.3 VFD flow characteristics
  • 7.6.4 VFD rangeability
  • 7.7 Best practices
  • 7.7.1 Control valve design specifications
  • 7.7.2 VFD design specifications
  • 7.8 Test results
  • Key points
  • 8. Effect of disturbances
  • 8.1 Introduction
  • 8.1.1 Perspective
  • 8.1.2 Overview
  • 8.1.3 Recommendations
  • 8.2 Disturbance dynamics
  • 8.2.1 Load time constants
  • 8.2.2 Load rate limit
  • 8.2.3 Disturbance dead time
  • 8.2.4 Disturbance oscillations
  • 8.3 Disturbance location
  • 8.4 Disturbance troubleshooting
  • 8.4.1 Sources of fast oscillations
  • 8.4.2 Sources of slow oscillations
  • 8.5 Disturbance mitigation
  • 8.6 Test results
  • Key points
  • 9. Effect of nonlinearities
  • 9.1 Introduction
  • 9.1.1 Perspective
  • 9.1.2 Overview
  • 9.1.3 Recommendations
  • 9.2 Variable gain
  • 9.2.1 Cascade control
  • 9.2.2 Reversals of process sign
  • 9.2.3 Signal characterization
  • 9.2.4 Gain scheduling
  • 9.2.5 Adaptive control
  • 9.2.6 Gain margin
  • 9.3 Variable dead time
  • 9.4 Variable time constant
  • 9.5 Inverse response
  • 9.6 Test results
  • Key points
  • 10. Effect of interactions
  • 10.1 Introduction
  • 10.1.1 Perspective
  • 10.1.2 Overview
  • 10.1.3 Recommendations
  • 10.2 Pairing
  • 10.2.1 Relative gain array
  • 10.2.2 Distillation column example
  • 10.2.3 Static mixer example
  • 10.2.4 Hidden control loops
  • 10.2.5 Relative gains less than zero
  • 10.2.6 Relative gains from zero to one
  • 10.2.7 Relative gains greater than one
  • 10.2.8 Model predictive control
  • 10.3 Decoupling
  • 10.4 Directional move suppression
  • 10.5 Tuning
  • 10.6 Test results
  • Key points
  • 11. Cascade control
  • 11.1 Introduction
  • 11.1.1 Perspective
  • 11.1.2 Overview
  • 11.1.3 Recommendations
  • 11.2 Configuration and tuning
  • 11.3 Process control benefits
  • 11.4 Process knowledge benefits
  • 11.5 Watch-outs
  • 11.6 Test results
  • Key points
  • 12. Advanced regulatory control
  • 12.1 Introduction
  • 12.1.1 Perspective
  • 12.1.2 Overview
  • 12.1.3 Recommendations
  • 12.2 Feedforward control
  • 12.2.1 Opportunities
  • 12.2.2 Watch-outs
  • 12.3 Intelligent output action
  • 12.3.1 Opportunities
  • 12.3.2 Watch-outs
  • 12.4 Intelligent integral action
  • 12.4.1 Opportunities
  • 12.4.2 Watch-outs
  • 12.5 Dead time compensation
  • 12.5.1 Opportunities
  • 12.5.2 Watch-outs
  • 12.6 Valve position control
  • 12.6.1 Opportunities
  • 12.6.2 Watch-outs
  • 12.7 Override control
  • 12.7.1 Opportunities
  • 12.7.2 Watch-outs
  • 12.8 Test results
  • Key points
  • 13. Process control improvement
  • 13.1 Introduction
  • 13.1.1 Perspective
  • 13.1.2 Overview
  • 13.1.3 Recommendations
  • 13.2 Unit operation metrics
  • 13.3 Opportunities
  • 13.3.1 Variability
  • 13.3.2 Increasing capacity and efficiency
  • 13.3.3 Effective use of models
  • 13.3.4 Sizing and assessment
  • 13.4 Key questions
  • Key points
  • 14. Auto tuners and adaptive control
  • 14.1 Introduction
  • 14.1.1 Perspective
  • 14.1.2 Overview
  • 14.1.3 Recommendations
  • 14.2 Methodology
  • Key points
  • 15. Batch optimization
  • 15.1 Introduction
  • 15.1.1 Perspective
  • 15.1.2 Overview
  • 15.1.3 Recommendations
  • 15.2 Cycle time
  • 15.3 Profile
  • 15.4 End point
  • Key points
  • Appendix A. Automation system performance top 10 concepts
  • Appendix B. Basics of PID controllers
  • Appendix C. Controller performance
  • Appendix D. Discussion
  • Appendix E. Enhanced PID for wireless and analyzer applications
  • Appendix F. First principle process relationships
  • Appendix G. Gas pressure dynamics
  • Appendix H. Convective heat transfer coefficients
  • Appendix I. Interactive to noninteractive time constant conversion
  • Appendix. Jacket and coil temperature control
  • Appendix K. PID forms and conversion of tuning settings
  • Appendix L. Liquid mixing dynamics
  • Appendix M. Measurement speed requirements for SIS
  • References
  • Bibliography
  • About the author
  • Index.

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