—  Universitat Politècnica de Catalunya (UPC), Chemical Engineering Department - CEPIMA

Three day advanced postgraduate course on

Process Design and Optimization: Discrete/Continuous Optimization Models and Techniques and their application in Process Systems Engineering

on June 6-8, by
Professor Ignacio Grossmann
Department of Chemical Engineering, Carnegie Mellon University

Course Venue
Chemical Engineering Department, Universitat Politècnica de Catalunya, Higher Technical School of Industrial Engineering, Diagonal 647, Pavelló G, planta 2
08028 Barcelona, Spain

Audience
This is a two credit course offered by the UPC PhD program Chemical Process Engineering to pre-doctoral students. The course is sponsored by the Ministry of Education and Sciences of Spain. Tuition is free. Applicants must send their CV and accreditation as registered doctoral students as well as a letter of recommendation.

Content
This course provides an overview of modeling and solution techniques for discrete/continuous optimization, illustrating their application with examples at various level of complexity. The course will consist of lectures (9:30-11:00, 11:30-13:00, 14:30-16:00) and workshops (16:30-18:30) in which students will solve exercises. The specific topics that will be covered are the following:

  1. Review of mixed-integer linear programming (MILP).
  2. Propositional logic for modeling 0-1 constraints. Reformulation of disjunctions as mixed-integer constraints.
  3. Application of MILP to separation synthesis, metabolic flux analysis, batch scheduling.
  4. Review of mixed-integer nonlinear programming (MINLP).
  5. Application of MINLP to structural parameter estimation and synthesis of heat exchanger networks.
  6. Review of generalized disjunctive programming (GDP).
  7. Applications of GDP and MINLP to synthesis of complex distillation systems.
  8. Global optimization of disjunctive programs.
  9. Application of global optimization to synthesis batch process design and water networks.
  10. Review of constraint programming and hybrid methods.
  11. Application of hybrid methods to batch scheduling problems