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Chemical Process Dynamics and Controls Text

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{| class="wikitable" style="border-style:solid; border-width:2px; border-color:#d0d0d0; background-color:#f0f0f0; padding:5px" = Process Control Introduction =
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Overview

 * 1)  Introduction to Controls: Background and design methodology
 * 2)  Introduction to DCS: Control system hardware
 * 3)  Current Significance: Process controls and you
 * 4)  Failures in Process Control: Bhopal, Three Mile Island
 * 5)  Process Controls in Everyday Life: Applying process control thinking to everyday situations.

Modeling Basics

 * 1)  Verbal modeling: process description, control specifications, and connections
 * 2)  Degrees of Freedom: importance, calculation procedure, and examples
 * 3)  Incidence graphs: interpretations, consistency, and inconsistency
 * 4)  Excel modeling: logical models, optimization with solver for nonlinear regression, sampling random numbers
 * 5)  Noise modeling: more detailed information on noise modeling: white, pink, and brown noise, pops and crackles
 * 6)  Numerical ODE solving in Excel: Euler’s method, Runge Kutta, Dead time in ODE solving
 * 7)  Solving ODEs with Mathematica: How to find numerical and analytical solutions to ODEs with Mathematica
 * 8) Fitting ODE parameters to data using Excel: Using regression to fit complex models in Excel
 * 9) Helpful Mathematica Syntax: Hints on how to use Mathematica to model chemical processes

Sensors and Actuators

 * 1)  Control Systems: Measurement devices
 * 2)  Temperature sensors
 * 3)  Pressure sensors
 * 4)  Level sensors
 * 5)  Flow sensors
 * 6)  Composition sensors
 * 7)  pH and viscosity sensors
 * 8)  Miscellaneous sensors
 * 9)  Valves:  types, kinds, and selection
 * 10)  Valves:  modeling dynamics

More information on sensors and actuators at ECOSSE

Piping and Instrumentation Diagrams

 * 1)  P&ID general information
 * 2)  P&ID standard notation
 * 3)  P&ID standard structures, location of features
 * 4)  P&ID standard pitfalls
 * 5)  Safety features in P&ID
 * 6)  Regulatory Agencies and Compliance



Logical Modeling

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



Modeling Case Studies
More information on chemical process modeling in general at ECOSSE example 1 and ECOSSE example 2
 * 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
 * 8)  Cruise control for an electric vehicle
 * 9)  Blood Glucose Control in Diabetic Patients
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{| class="wikitable" style="height:100%; border-style:solid; border-width:2px; border-color:#d0d0d0; background-color:#f0f0f0; padding:5px" =Chemical Process Controls=

Mathematics for Control Systems

 * 1)  Dirac delta (impulse) function (10/09)
 * 2)  First-order differential equations (12/14/09)
 * 3)  Second-order differential equations (10/13)
 * 4)  Taylor Series
 * 5) Laplace Transforms

Optimization

 * 1)  Optimization
 * 2)  Linear Optimization
 * 3)  Non-linear Optimization

PID control

 * 1)  Constructing Block Diagrams: Visualizing control measurements
 * 2)  P, I, D, PI, PD, and PID control see also ECOSSE
 * 3)  PID tuning via classical methods See also ECOSSE
 * 4)  PID tuning via Frequency Response w/ Bode Plots
 * 5)  PID tuning via optimization
 * 6)  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)  Summary: Summary on Control Architectures' philosophies, advantages, and disadvantages.
 * 6)  Common control loops / model for liquid pressure and liquid level  see also ECOSSE
 * 7)  Common control loops / model for temperature control
 * 8)  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
 * 4)  Neural Networks for automatic model construction
 * 5)  Understanding MIMO Control Through Two Tanks Interaction


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{| class="wikitable" style="width:100%; border-style:solid; border-width:2px; border-color:#d0d0d0; background-color:#f0f0f0; padding:5px" =Statistical Analysis for Chemical Process Control=
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Statistics and Probability Background

 * 1)  Basic statistics: mean, median, average, standard deviation, z-scores, and p-value
 * 2)  SPC: Basic Control Charts: Theory and Construction, Sample Size, X-Bar, R charts, S charts
 * 3)  Six Sigma: What is it and what does it mean?
 * 4)  Bayes Rule, conditional probability, independence
 * 5)  Bayesian network theory
 * 6)  Learning and analyzing Bayesian networks with Genie
 * 7)  Occasionally dishonest casino?: Markov chains and hidden Markov models
 * 8)  Continuous Distributions: normal and exponential
 * 9)  Discrete Distributions: hypergeometric, binomial, and poisson
 * 10)  Multinomial distributions
 * 11)  Comparisons of two means
 * 12)  Factor analysis and ANOVA
 * 13)  Correlation and mutual information
 * 14)  Random sampling from a stationary Gaussian process
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Design of Experiments

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