Application of stochastic evolutionary optimization techniques to chemical processes

Abstract The chemical industry is experiencing significant changes due to global market competition, strict bounds on product specifications, pricing pressures, and environmental issues. This made the industry to enhance its performance with the implementation of modifications in design and operating procedures and the application of methods and tools with an emphasis of reducing costs, improving efficiency and increasing profitability. Optimization is the most important approach that addresses the performance issues related to several areas of chemical process engineering. Stochastic and evolutionary optimization techniques are widely used to solve complex engineering problems related to analysis, design, modeling, identification, operation, and control of chemical processes that are highly nonlinear and high dimensional or the problems that are not easily solved by classical deterministic methods of optimization. This chapter focuses on the design and implementation of various stochastic global optimization strategies to real chemical engineering applications concerning to multistage dynamic optimization of polymerization reactors, multiloop tuning of proportional–integral controllers that account multivariable interactions and nonlinear process dynamics in reactive distillation, and stochastic optimization–based nonlinear model predictive controllers for efficient control of highly nonlinear reactive distillation columns. The results evaluated for different case studies show the advantages of stochastic global optimization methods for solving complex optimization problems in chemical engineering domain.