RECENT TRENDS IN-PROCESS OPTIMIZATION

The objective of this paper is to present different optimization techniques used in process optimization and synthesis. The first part defines the process synthesis problem as a mixed integer programming problem and discusses the main approaches which were used to solve it. In the discrete approach, where the operating conditions of the unit operation are fixed, combined heuristic and algorithmic procedures, such as the recent method of Simulated Annealing (SA), are promising tools. The SA procedure is illustrated by the synthesis of sharp separation sequences. In the continuous approach, the synthesis or the optimization problem is a nonlinear programming problem (NLP), where the operating conditions are optimized. Since efficient large-scale NLP codes are now available, this approach will be a great success. A recent alternative consists in merging the two previous approaches to implement large-scale Mixed-Integer Nonlinear Programming (MINLP) codes. This method has wide applications in complex process synthesis, or in model identification. An application to a network heat exchanger design and the model identification of a settling tank are given.