Genetic Algorithms for Chemical Engineering Optimization Problems

Abstract

Chemical engineering processes are frequently composed of multiple complex phenomena. These systems can be represented by a set of several equations, which are referred to as mathematical model of the process. Optimization in chemical engineering utilizes specialized techniques to determine the values of the decision variables at which the performance of the process, measured as the objective function(s), is minimum or maximum. The profitability of the process improves remarkably as a result of this selection. This benefit has encouraged the broad application of optimization for important industrial challenges. However, many problems in chemical engineering processes are hard to find the optimum using gradient-based algorithms. For example, the cases when the objective functions of the processes are multimodal, discontinuous, or implicit. Genetic algorithms (GAs) are a kind of metaheuristic searching optimization methods, which are inspired by nature, the mechanics of natural evolution and genetics. Genetic algorithms have received significant attention due to their remarkable advantages over classical algorithms. Compared with traditional optimization approaches, GAs are straightforward, robust, capable of handling the non-differentiable, discontinuous, or multimodal problems. The purpose of this paper is to give several case studies using genetic algorithms in chemical engineering optimization problems.