Chemical Process Dynamics and Controls

by Peter Woolf et al.

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This textbook is a student-contributed open-source text covering the materials used in the senior level controls course. The authors cover a variety of approaches to reduce or manage this uncertainty through the use of robust designs, dynamic systems theory, nonlinear dynamics, control theory, and statistics.

Description from LibreTexts.

Publisher
University of Michigan
Subjects
Engineering, Industrial and Systems Engineering
Licence
CC BY 3.0, as LibreTexts states it
Who can read
Tweens profiles and older, judged from its title and subjects

Contents

LibreTexts writes equations in TeX, and here they show as TeX; the PDF has them typeset.

  1. Front Matter
  2. TitlePage
  3. InfoPage
  4. Table of Contents
  5. Licensing
  6. 1: Overview
  7. 1.1: Introduction to Controls- Background and design methodology
  8. 1.2: Introduction to DCS- Control system hardware
  9. 1.3: Current Significance- Process controls and you
  10. 1.4: Failures in Process Control- Bhopal, Three Mile Island
  11. 1.5: Process Controls in Everyday Life- Applying process control thinking to everyday situations
  12. 2: Modeling Basics
  13. 2.1: Verbal Modeling- process description, control specifications, and connections
  14. 2.2: Degrees of Freedom- importance, calculation procedure, and examples
  15. 2.3: Incidence Graphs -Interpretations, Consistency, and Inconsistency
  16. 2.4: Excel Modeling - logical models, optimization with solver for nonlinear regression, sampling random numbers
  17. 2.5: Noise Modeling - White, Pink, and Brown Noise, Pops and Crackles
  18. 2.6: Numerical ODE solving in Excel- Euler’s method, Runge Kutta, Dead time in ODE solving
  19. 2.7: Solving ODEs with Mathematica- How to find numerical and analytical solutions to ODEs with Mathematica
  20. 2.8: Fitting ODE parameters to data using Excel- Using regression to fit complex models in Excel
  21. 2.9: Helpful Mathematica Syntax- Hints on how to use Mathematica to model chemical processes
  22. 3: Sensors and Actuators
  23. 3.1: Control Systems - Measurement Devices
  24. 3.2: Temperature Sensors
  25. 3.3: Pressure Sensors
  26. 3.4: Level Sensors
  27. 3.5: Flow Sensors
  28. 3.6: Composition Sensors
  29. 3.7: pH and Viscosity Sensors
  30. 3.8: Miscellaneous Sensors
  31. 3.9: Valves - Types, Kinds, and Selection
  32. 3.10: Valves - Modeling Dynamics
  33. 4: Piping and Instrumentation Diagrams
  34. 4.1: PandID General Information
  35. 4.2: Piping and Instrumentation Diagram Standard Notation
  36. 4.3: Piping and Instrumentation Diagrams - Location of Controls and Standard Control Structures
  37. 4.4: Piping and Instrumentation Diagrams - Standard Pitfalls
  38. 4.5: Safety Features in Piping and Instrumentation Diagrams
  39. 4.6: Regulatory Agencies and Compliance
  40. 5: Logical Modeling
  41. 5.1: Boolean Models - Truth Tables and State Transition Diagrams
  42. 5.2: Logical Control Programs - IF... THEN… WHILE…
  43. 6: Modeling Case Studies
  44. 6.1: Surge Tank Model
  45. 6.2: Heated Surge Tank
  46. 6.3: Bacterial Chemostat
  47. 6.4: ODE and Excel CSTR model with heat exchange
  48. 6.5: ODE and Excel model of a Simple Distillation Column
  49. 6.6: ODE and Excel model of a Heat Exchanger
  50. 6.7: ODE and Excel Model of an Adiabatic PFR
  51. 6.8: Modeling and PID Controller Example - Cruise Control for an Electric Vehicle
  52. 6.9: Blood Glucose Control in Diabetic Patients
  53. 7: Mathematics for Control Systems
  54. 7.1: Dirac delta (impulse) function
  55. 7.2: First-order Differential Equations
  56. 7.3: Second-order Differential Equations
  57. 7.4: Taylor Series
  58. 7.5: Laplace Transforms
  59. 8: Optimization
  60. 8.1: Introduction to Optimization
  61. 8.2: Linear Optimization
  62. 8.3: Non-linear Optimization
  63. 9: Proportional-Integral-Derivative (PID) Control
  64. 9.1: Constructing Block Diagrams- Visualizing control measurements
  65. 9.2: P, I, D, PI, PD, and PID control
  66. 9.3: PID Tuning via Classical Methods
  67. 9.4: PID tuning via Frequency Responses with Bode Plots
  68. 9.5: PID tuning via Optimization
  69. 9.6: PID Downsides and Solutions
  70. 10: Dynamical Systems Analysis
  71. 10.1: Finding fixed points in ODEs and Boolean models
  72. 10.2: Linearizing ODEs
  73. 10.3: Eigenvalues and Eigenvectors
  74. 10.4: Using Eigenvalues and Eigenvectors to Find Stability and Solve ODEs
  75. 10.5: Phase Plane Analysis - Attractors, Spirals, and Limit cycles
  76. 10.6: Root Locus Plots - Effect of Tuning
  77. 10.7: Routh Stability - Ranges of Parameter Values that are Stable
  78. 11: Control Architectures
  79. 11.1: Feedback Control
  80. 11.2: Feed Forward Control
  81. 11.3: Cascade Control
  82. 11.4: Ratio Control
  83. 11.5: Common control loops and model for liquid pressure and liquid level
  84. 11.6: Common Control Loops and Model for Temperature Control
  85. 11.7: Common control architectures and model for reactors
  86. 12: Multiple Input, Multiple Output (MIMO) Control
  87. 12.1: Determining if a system can be decoupled
  88. 12.2: MIMO control using RGA
  89. 12.3: MIMO using Model Predictive Control
  90. 12.4: Neural Networks for automatic model construction
  91. 12.5: Understanding MIMO Control Through Two Tanks Interaction
  92. 13: Statistics and Probability Background
  93. 13.1: Basic statistics- mean, median, average, standard deviation, z-scores, and p-value
  94. 13.2: SPC- Basic Control Charts- Theory and Construction, Sample Size, X-Bar, R charts, S charts
  95. 13.3: Six Sigma- What is it and what does it mean?
  96. 13.4: Bayes Rule, Conditional Probability and Independence
  97. 13.5: Bayesian Network Theory
  98. 13.6: Learning and analyzing Bayesian networks with Genie
  99. 13.7: Occasionally Dishonest Casino? Markov Chains and Hidden Markov Models
  100. 13.8: Continuous Distributions- normal and exponential
  101. 13.9: Discrete Distributions - Hypergeometric, Binomial, and Poisson
  102. 13.10: Multinomial Distributions
  103. 13.11: Comparisons of two means
  104. 13.12: Factor analysis and ANOVA
  105. 13.13: Correlation and Mutual Information
  106. 13.14: Random sampling from a stationary Gaussian process
  107. 14: Design of Experiments
  108. 14.1: Design of Experiments via Taguchi Methods - Orthogonal Arrays
  109. 14.2: Design of experiments via factorial designs
  110. 14.3: Design of Experiments via Random Design
  111. 14.4: Summary- Summary on Control Architectures’ philosophies, advantages, and disadvantages.
  112. Back Matter
  113. Index
  114. Detailed Licensing