Process Control

This page is managed by Dr. John Hedengren (Brigham Young University) and Dr. Thomas Edgar(The University of Texas at Austin)

Syllabi, schedules, course notes

• University of Texas at Austin: Process Dynamics and Control Course notes and PowerPoint slides for the book, Process Dynamics and Control by D. E. Seborg, T. F. Edgar, D. A. Mellichamp, and F.J. Doyle.
• Brigham Young University: Course notes, computer tools and demo code are available on the web-pages
  • Process Dynamics and Control (Primarily MATLAB and Simulink)
  • Process Dynamics and Control with Python
  • Computational Tools – Introduction to Programming
  • Optimization Theory and Applications
  • Dynamic Optimization
• McMaster University: Comprehensive undergraduate Process Control course material along with exercises. Recommended book – Marlin, T., Process Control: Designing Processes and Control Systems for Dynamic Performance (free PDF version of textbook available).
  • Flipping the Chemical Engineering Process Control Class with e-Lessons – by Thomas Marlin
• Arizona State University: Extensive course notes on a variety of topics by Dr. Dan Rivera.
• Control Loop Foundation: Teaching examples and simulation material on process control presented from an industrial perspective (Slides and YouTube Video).
• University of Notre Dame: GitHub repository comprising more than 70 Jupyter/Python notebooks by Dr. Jeffrey Kantor. The interactive examples support the Chemical Process Control course at Notre Dame.

Textbooks

Screencasts

• University of Colorado Boulder: More than 80 short screencasts videos for process control. Also available on Youtube.
• Georgia Institute of Technology (with LearnChemE): 17 process control screencasts and accompanying MATLAB script
  Screencasts with embedded questions can be found here
   
• Python for Process Control (Brigham Young University): 20 screencasts

Software

• Model Predictive Control of an Adhesive Coater: A Matlab project in which students design a model predictive control system for a multivariable adhesive coating process. The project description includes a self-contained introduction to model predictive control needed for the project.
• Interactive On-line Optimization: An automated system that adjusts the operation of a plant based on product scheduling and production control to maximize profit and minimize emissions.
• Penicillin Simulation: PenSim© v1.0: A Web Based Program for Dynamic Simulation of Fed-Batch Penicillin Production
  Department of Chemical and Environmental Engineering, Illinois Institute of Technology, Chicago
• GlucoSim: A web-based educational simulation package for glucose-insulin levels in the human body
• Nonlinear Model Database: A collaborative environment where chemical process models can be documented and shared. The database consists of about 20 nonlinear models that include chemical reactors, binary distillation columns, and simple mechanical systems.
• Online PID Process Control Tuner: An interactive PID faceplate that generates open- and closed-loop responses for a given transfer function model and control tuning parameters selected by the user.
• Introduction to LabVIEW for Control Design and Simulation: The Control Design and Simulation toolbox in LabVIEW can be used to analyze dynamic open- and closed-loop systems.
• Introduction to the MATLAB SIMULINK Program: SIMULINK is a part of MATLAB that can be used to simulate dynamic systems.
• RPI: Simulink Tutorial
• Control Station Laboratory: Research, Training and Technology Transfer in Automatic Process Control
• Process Dynamics: Modeling, Analysis and Simulation and Model-Based Process Control: Learn about B. Wayne Bequette’s, Rensselaer Polytechnic Institute, work on Process Control and Design.
• Tennessee Eastman Challenge Process (case study problem)
• Aspen HYSYS Teaching Modules: The modules provide step-by-step tutorials for reactors, thermodynamics/flash, distillation, material/energy balance, dynamics, and design. This includes simulation for plug flow reactors, continuously stirred tank reactors, flash calculations, equation of state, simple and complex distillation, steady state and dynamic flowsheets, and basic process design.
• MATLAB and Simulink Tutorials: Interactive tutorials provided by MathWorks that are designed to get students of any programming background quickly familiarized with the MATLAB programming and simulation environment. Includes references to books and resources that offer more comprehensive in-depth knowledge about the MATLAB environment.
• BUS (Biomass Utilization Superstructure): An optimization-based web application for synthesis and analysis of biomass-to-fuel strategies.

Optimization Software

• ​AMPL: A comprehensive and powerful algebraic modeling language for linear and nonlinear optimization problems, in discrete or continuous variables.Developed at Bell Laboratories, AMPL lets you use common notation and familiar concepts to formulate optimization models and examine solutions, while the computer manages communication with an appropriate solver. AMPL’s flexibility and convenience render it ideal for rapid prototyping and model development, while its speed and control options make it an especially efficient choice for repeated production runs.
• APOPT: NLP / MINLP solver for large-scale optimization, available in AMPL, APMonitor, Gekko, MATLAB, Python, and Julia.
• CasADi: An open-source tool for nonlinear optimization and algorithmic differentiation.
• Computational Infrastructure for Operations Research (COIN-OR): An initiative to spur the development of open-source software for the operations research community. It provides a list of open-source tools available for operations research and optimization.
• CONOPT: A solver for large-scale nonlinear optimization (NLP) developed and maintained by ARKI Consulting & Development A/S in Bagsvaerd, Denmark.
• The General Algebraic Modeling System (GAMS): A high-level modeling system for mathematical programming problems. This site has documentation and related user publications and contributions.
• Gekko: Optimization software for estimation and predictive control with machine learning and first principles modeling
• IPOPT: (Interior Point OPTimizer, pronounced eye-pea-Opt) A software package for large-scale nonlinear optimization
• OPTI Toolbox: A free MATLAB toolbox for constructing and solving linear, nonlinear, continuous, and discrete optimization problems.
• Pyomo: Python-based, open-source optimization modeling language with a diverse set of optimization capabilities.

Hardware

• Arduino Temperature Lab for Modeling and PID Control (Python or MATLAB/Simulink)
• Arduino Temperature Lab for Modeling and MPC Control
• Microcontrollers to Improve Learning and Engagement in Chemical Engineering Classrooms (Webinar)
• MIT Feedback Control Systems with Palm-sized Drones

Useful links

Optimization Websites

• NEOS Guide Website: The NEOS Optimization Guide provides information about the field of optimization and its sub-disciplines. It focuses on the resources available for solving optimization problems, including the solvers available on the NEOS server.
• ChE Optimization course website – University of Texas at Austin: The course website provides lecture notes, homework materials and solutions for optimization problems that are relevant in Chemical Engineering.
• Chemical Complex Analysis System: Used to demonstrate optimization of a chemical complex. The System incorporates economic, environmental and sustainable costs and solves a MINLP for the best configuration of plants.
• Minerals Processing Research Institute: Optimization software and textbook by Prof. Ralph W. Pike, Louisiana State University.
• Decision Tree for Optimization Software: Has solutions to optimization problems, collection of test results and performance tests, example files ready to use with existing software, softwares which helps formulating an optimization problem or simplifying its solution, and many other helpful materials for optimization.
• Center for Optimization and Statistical Learning: Optimization Technology Center of Northwestern University and Argonne National Laboratory
• AlphaOpt: Introduction to optimization (YouTube channel).

Process Systems Research Consortia

• Chemical Process Modeling & Control Research Center, Lehigh Uniersity: Aims to educate a special group of M.S. and Ph.D. graduates and to develop advanced technologies in process modeling, monitoring and controller design that enable industries to reduce process and product variability, improve process productivity and operability and enhance product quality.
• The McMaster Advanced Control Consortium (MACC): Established to promote and advance process automation and related process systems engineering technologies through academe-industry interactions.
• Arizona State Control Systems Engineering Laboratory: The lab is committed to broadly-applicable research in the areas of system identification and advanced process control. The goal of the research program is to develop fundamentally-oriented identification and control methodologies that ultimately improve the efficiency, profitability, safety and environmental compliance of process systems.
• Center for Process Analysis and Control (CPAC), University of Washington: Objective is to develop real-time measurement and relevant data handling techniques. Through its consortium of industrial sponsors, national laboratories, and government agencies, CPAC addresses challenges in monitoring production processes for effective modeling and control.
• Center for Advanced Process Decision-Making, Carnegie Mellon: Advanced Computer-based techinques for Process Synthesis, Process Optimization, Planning and Scheduling, Process Control, Safety and Reliability.
• Texas – Wisconsin – California Control Consortium (TWCCC): A consortium for carrying out joint industry-academic research. It emphasises on Control System Monitoring and Diagnosis, Dynamic Modeling of Chemical Processes, Nonlinear Model-Predictive Control, Materials Processing
• Process and Systems Engineering Centre (PROSYS), Technical University of Denmark : PROSYS is committed to research work and educational activities. Its research objectives include Development of Computer Aided Systems for Process Simulation, Design, Analysis and Control/Operation for Chemical, Petrochemical, Pharmaceutical and Biochemical Industries.
• Process Systems Engineering Consortium (PSEC): A collaboration between the Process Design and Control Center at the University of Massachusetts Amherst and process systems engineering faculty at the Massachusetts Institute of Technology and the University of California, Santa Barbara. The mission of PSEC is to create systematic methods for the rapid invention, development, and operation of industrial processes to manufacture high-value products including specialty chemicals, pharmaceuticals (including excipients), home and personal health care products, processed foods, etc.