CAPD - Center for Advanced Process Decision-making

Short Course - Detailed Course Topics

Optimization Modeling, Conceptual Design, and Integrated Process Operations

May 15-26, 2023

The material in each module is independent and self-contained and can be taken in any combination.

For more information, please contact
Carl Laird - claird@andrew.cmu.edu

The detailed course schedule for the 2022 short course is shown below. The updated detailed schedule for 2023 is coming soon

Short Course Links

Overview

Faculty

Detailed Course Topics

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Registration

Conceptual Design (Module A)

May 2 Monday	Module A: Process Synthesis Jeffrey Siirola
8:30 to 10:15 AM	Introduction Systematic process synthesis Heat and power integration
10:30 to Noon	Heat exchanger network synthesis Distillation sequences
1:00 to 3:15 PM	Complex column configurations Heat integrated distillation Azeotropic and extractive distillation Reactive distillation Process intensification
3:30 to 5:30 PM	Nonideal separation synthesis Residue curve theory Synthesizing azeotropic and extractive distillation systems Strategic process synthesis Plant network synthesis Process synthesis implementation

Optimization Modeling (Module B-E)

May 3 Tuesday	Module B: Nonlinear Programming Larry Biegler
8:30 to 10:00 AM	Nonlinear Programming Unconstrained Newton-type Methods Karush Kuhn Tucker Conditions Linear Programming, Quadratic Programming
10:30 to Noon	Nonlinear Programming Algorithms Successive Quadratic Programming Interior Point/Barrier Methods (IPOPT) Reduced Gradient Methods (MINOS, CONOPT)
1:00 to 3:15 PM	Process Optimization Flowsheet Optimization for Design Real-time Optimization Sensitivity of Optimal Solutions
3:30 to 5:30 PM	GAMS Workshop

May 4 Wednesday	Module C: Mixed Integer and Disjunctive Programming Ignacio Grossmann
8:30 to 10:00 AM	Mixed-integer Linear Programming Major Types Integer and Mixed-integer Constraints Algorithms: Branch and Bound Example: Synthesis Separation Network
10:15 to Noon	Logic Based Optimization and Disjunctive Programming Propositional Logic for Formulating Constraints Examples of Logic Constraints Convex Hull and Big-M Reformulations
1:00 to 2:15 PM	Generalized Disjunctive Programming Logic-based Methods and Reformulation (LOGMIP) Examples: Distillation Columns, Sequences Flowsheet Synthesis
2:30 to 3:30 PM	Global Optimization: Bilinear MINLP Application Water Networks Global Optimization GDP Problems Use of Basic Steps to Strengthen Lower Bounds
3:45 to 5:45 PM	Practice Session of Mixed-Integer Modeling

May 5 Thursday	Module D: Global Optimization and Optimization under Uncertainty Nikolaos Sahinidis
8:30 to 10:00 AM	Applications of Global Optimization Building Blocks of Global Optimization Algorithms Branch and Bound Algorithms for Global NLP and MINLP
10:15 to Noon	Separable and Factorable Programming Techniques Range Reduction Techniques Branch and Reduce Algorithm (BARON)
1:00 to 3:00 PM	Applications of branch-and-reduce BARON global optimization system Stochastic Linear Programming Decomposition and Sampling Algorithms Stochastic Integer Programming Planning the Supply Chain under Uncertainty Probabilistic and Fuzzy Programming Robust Optimization Portfolio Optimization
3:30 to 5:30 PM	GAMS Workshop

May 8 Friday	Module E: Modeling Extensions and Solution of Large-Scale Problems in Pyomo Carl Laird
8:30 to 11:00 AM : Introduction to Pyomo and Advanced Modeling Capabilities	Overview of Modeling Components Generalized Disjunctive Programming in Pyomo Dynamic optimization with Pyomo.DAE Improving performance of large-scale models Examples
11:15 to Noon : Parallel Algorithms in Python	Introduction to parallel architectures Parallel algorithms and scalability Python tools for parallel algorithm development
1:00 to 2:30 PM : Advanced Algorithms and Analysis with PyNumero	Introduction to PyNumero NLP algorithm interfaces Linear algebra structures Examples
2:45 to 4:15 PM : Optimization and Machine Learning Toolkit (OMLT)	Introduction to OMLT Optimization formulations for trained ML models OMLT Block and formulation objects Examples
4:15 to 5:00 PM : Parallel NLP Algorithms	Parallel NLP Algorithms for stochastic and dynamic problems Introduction to ParaPint

Integrated Process Operations (Module F-I)

May 9 Monday	Module F: Mixed Integer Models for Planning and Scheduling Ignacio Grossmann
8:30 to 10:00 AM	Introduction and Roadmap to Scheduling Problems Overview Mathematical Programming Models Batch Scheduling: Single Stage, Parallel Units
10:15 to Noon	Batch Scheduling: Multi-stage Plants Flowshop Scheduling State-task and Resource-task Network Discrete and Continuous Time Models
1:00 to 2:15 PM	Scheduling of Continuous Multistage Plants Refinery Scheduling and Blending Multi-site Production Planning Supply Chain Models
2:30 to 3:45 PM	Constraint Programming Hybrid Methods for Scheduling: Single Stage and STN Decomposition Methods for Planning and Scheduling: Lagrangean and Bi-level Methods Use of Basic Steps to Strengthen Lower Bounds
3:45 to 5:15 PM	Practice Session Modeling Scheduling Problems

May 10 Tuesday	Module G: Models and Algorithms for Supply Chain Chrysanthos Gounaris
8:30 to 10:00 AM	Introduction to Supply Chain Optimization Supply Chain Network Optimization
10:15 to 11:45 AM	Traveling Salesman Problem Vehicle and Inventory Routing Exact Solution Algorithms (Branch-and-Cut/-and-Price)
1:00 to 2:15 PM	Introduction to Metaheuristics Construction Heuristics and Local Search for Routing Trajectory-based and Population-based Methods
2:30 to 3:45 PM	Network Planning under Uncertainty Preparation for Workshop Activities
4:00 to 5:30 PM	Workshop
May 11 Wednesday	Module H: Differential/Algebraic Models for Real Time Optimization Larry Biegler
8:30 to 10:00 AM	Differential algebraic optimization Sequential methods based on ODE solvers Optimal control methods Case studies and examples
10:15 to Noon	Optimization of dynamic systems Collocation-based methods Optimal control and high index DAEs Optimization of unstable systems Nonlinear model predictive control
1:00 to 3:15 PM	Parameter estimation Trust region methods Constrained least squares and SQP variants Robust least squares and outlier detection Steady state date and Dynamic data reconciliation
3:30 to 5:00 PM	Workshop

May 12 Thursday	Module I: Process Analytics using Multivariate Analysis Techniques in Python Salvador Garcia Muñoz
8:30 to 10:00 AM	From univariate to multivariate thinking Fundamentals of Principal Components Analysis (PCA) Process understanding and process troubleshooting tools Multivariate Statistical Process Control (MSPC)
10:15 to Noon	Workshop: Case studies Fundamentals of Projection to Latent Structures (PLS)
1:00 to 3:15 PM	Development of soft sensors with process data Multi-block PLS Development of soft sensors from spectra (Chemometrics) Workshop: Case studies
3:30 to 5:30 PM	Process and product design using inverse PLS models and non-linear programming Workshop: Case studies