Continuous and Discrete Optimization in Process Synthesis and Integration

The design and optimization of process plants (including industrial energy systems) can be formulated as mathematical optimization problems where the choice of structure or topology (selection, sequence and interconnection of unit operations) can be modeled by discrete (normally binary) variables, while operating conditions, flowrates, temperatures, etc. can be modeled by continuous variables.
The course will give an introduction to and knowledge about the use of Mathematical Programming which is a class of deterministic methods to solve constrained optimization problems, including Linear Programming (LP), Mixed-Integer Linear Programming (MILP), Non-Linear Programming (NLP) and Mixed-Integer Linear Programming (MINLP) problems. With such a broad spectre of topics, it will not be possible to go into too much details about each topic.
The numerical solution stage will only be briefly handled, with focus on some of the simpler algorithms, such as the Simplex method for LP problems and the Branch & Bound method for MILP problems.

To provide a theoretical and practical background to the formulation and solution of optimization problems which are relevant in Process Synthesis, Process Design and Process Integration. Typically, these optimization problems contain both continuous and discrete variables and considerable degree of non-linearity.

A few lectures, self studies, study groups and voluntary assignments.