2007Text

Welcome to the University of Michigan Chemical Engineering Process Dynamics and Controls Open Textbook. This electronic textbook is a student contributed opensource text covering the materials used at Michigan in our senior level controls course.

Click here for the 2006 version of the text

Information about this course

Other texts that may also be of interest:
 * ECOSSE HyperCourse on control theory from the university of Edinburgh, Scotland
 * David MacKay's text on Information Theory, Inference, and Learning Algorithms.  This text provides a solid foundation for probability theory.
 * Video lectures on linear algebra from MIT's open courseware. If you want to learn more about eigenvalues, eigenvectors, determinants, and SVD, here is a good place to go!

 Golden Globe Valve Site!  Results of the 2007 Golden Globe Valve competition here!

Authors and Editors

Chemical Process Dynamics and Controls

= Modeling Basics=
 * 1)  Verbal modeling: process description, control specifications, and connections
 * 2)  Incidence graphs: interpretations, consistency, and inconsistency
 * 3)  Excel modeling: logical models, optimization with solver for nonlinear regression, sampling random numbers
 * 4)  Numerical ODE solving in Excel: Euler’s method, Runga Kutta, Dead time in ODE solving
 * 5) [[Image:new.gif]] Solving ODEs with Mathematica: How to find numerical and analytical solutions to ODEs with Mathematica
 * 6) [[Image:new.gif]]Fitting ODE parameters to data using Excel: Using regression to fit complex models in Excel

=Sensors and Actuators=
 * 1)  Temperature sensors
 * 2)  Pressure sensors
 * 3)  Level sensors
 * 4)  Flow sensors
 * 5)  Composition sensors
 * 6)  pH and viscosity sensors
 * 7)  Valves:  types, kinds, and selection
 * 8)  Valves:  modeling dynamics

More information on sensors and actuators at ECOSSE

=Piping and Instrumentation Diagrams=
 * 1)  P&ID standard notation
 * 2)  P&ID standard structures, location of features
 * 3)  P&ID standard pitfalls
 * 4)  Safety features in P&ID

=Logical Modeling=
 * 1)  Boolean models: truth tables and state transition diagrams
 * 2)  Logical control programs: IF.. THEN.., WHILE..

=Modeling Case Studies=
 * 1)  Surge tank model
 * 2)  Heated surge tank see also ECOSSE
 * 3)  Bacterial chemostat
 * 4)  ODE & Excel CSTR model w/ heat exchange
 * 5)  ODE & Excel model of a simple distillation column
 * 6)  ODE & Excel model of a heat exchanger
 * 7)  ODE & Excel model of an adiabatic PFR

More information on chemical process modeling in general at ECOSSE example 1 and ECOSSE example 2

=Chemical Process Controls=

PID control

 * 1)  P, I, D, PI, PD, and PID control see also ECOSSE
 * 2)  PID tuning via classical methods See also ECOSSE
 * 3)  PID tuning via optimization
 * 4)  PID downsides and solutions  See also ECOSSE

Dynamical Systems Analysis

 * 1)  Finding fixed points in ODEs and Boolean models
 * 2)  Linearizing ODEs
 * 3)  Eigenvalues and Eigenvectors
 * 4)  Using eigenvalues and eigenvectors to find stability and solve ODEs
 * 5)  Phase plane analysis: attractors, spirals, limit cycles
 * 6)  Root locus plots: effect of tuning
 * 7)  Routh stability: ranges of parameter values that are stable

Control Architectures

 * 1)  Feedback control: What is it?  When useful?  When not?  Common usage.
 * 2)  Feed forward control: What is it?  When useful?  When not?  Common usage.  see also ECOSSE
 * 3)  Cascade control: What is it?  When useful?  When not?  Common usage.
 * 4)  Ratio control: What is it?  When useful?  When not?  Common usage.
 * 5)  Common control loops / model for liquid pressure and liquid level  see also ECOSSE
 * 6)  Common control loops / model for temperature control
 * 7)  Common control architectures / model for reactors

MIMO Control

 * 1)  Determining if a system can be decoupled
 * 2)  MIMO control using RGA see also ECOSSE
 * 3)  MIMO using model predictive control

=Statistical Analysis for Chemical Process Control=

Statistics and Probability Background

 * 1)  Basic statistics: mean, median, average, standard deviation, z-scores, and p-value
 * 2) [[Image:new.gif]] Six Sigma: What is it and what does it mean?
 * 3)  Bayes Rule, conditional probability, independence
 * 4)  Occasionally dishonest casino?: Markov chains and hidden Markov models
 * 5)  Continuous Distributions: normal and exponential
 * 6)  Discrete Distributions: hypergeometric, binomial, and poisson
 * 7) [[Image:new.gif]] Multinomial distribution
 * 8)  Comparisons of two means
 * 9) [[Image:new.gif]] Factor analysis and ANOVA
 * 10) [[Image:new.gif]] Correlation and mutual information
 * 11)  Random sampling from a stationary Gaussian process

Statistical Process Control

 * 1)  SPC: Basic Control Charts: Theory and Construction, Sample Size, X-Bar, R charts, S charts

Design of Experiments

 * 1)  Design of experiments via Taguchi methods: orthogonal arrays
 * 2)  Design of experiments via factorial designs

Bayesian Networks

 * 1) [[Image:new.gif]] Bayesian network theory
 * 2) [[Image:new.gif]] Learning and analyzing Bayesian networks with Genie