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