Process Structure Optimization Using a Hybrid Disjunctive-Genetic Programming Approach

Abstract

Discrete optimization problems, which give rise to the conditional modelling of equations through representations as logic based disjunctions, are very important and often appear in all scales of chemical engineering process network design and synthesis. Disjunctive-Genetic Programming (D-GP), based on the integration of Genetic Algorithm (GA) with the disjunctive formulations of the Generalized Disjunctive Programming (GDP) for the optimization of process networks, has been proposed in this work. With the increase in the problem scale, dealing with such alternating routes becomes difficult due to increased computational load and possible entanglement of the results in sub-optimal solutions due to infeasibilities in the MILP space. In this work, the genetic algorithm (GA) has been used as a jumping operator to the different terms of the discrete search space and for the generation of different feasible fixed configurations. This proposed approach eliminates the need for the reformulation of the discrete /discontinuous optimization problems into direct MINLP problems, thus allowing for the solution of the original problem as a continuous optimization problem but only at each individual discrete and reduced search space.