Process optimization using a dynamic self-adaptive constraint handling technique coupled to a Differential Evolution algorithm.

Nowadays, chemical processes are projected to obtain their best performance in energy and water consumption, pollutant emissions and total annual costs, while still meeting quality of products and good operational performance. These goals are accomplished through adequate optimization of the fitness function by manipulating the operational variables (decision variables) of the process. However, a successful optimization process depends completely on the constraint handling established in the modeling of the process. The weighted summation of constraint violations (weighting function technique, WF) is one of the most common approaches for handling constraints in optimization problems. Nevertheless, in spite of this technique yielding good results, in this work we show a novel self-adaptive constraint handling technique (SA) based on a self-adaptation dynamic threshold and self-adaptation (weight) factors. This technique deals with real and discrete variables and converts equality constraints into inequality constraints through a dynamic threshold. Both penalization techniques (WF and SA) were, respectively, coupled to a Differential Evolution (DE) algorithm to optimize some benchmark functions and chemical engineering optimization problems. In addition, the rigorous model of a distillation train was optimized in Aspen One for the first time with a self-adaptive constraint handling technique in chemical engineering. Although both penalization techniques were coupled to the same DE algorithm and both cases were run under the same conditions, the results show that the dynamic self-adaptive constraint handling technique coupled to DE (DE-SA) achieves considerably better best-solutions than the best-solutions obtained by the weighting function technique coupled to DE (DE-WF). In addition, DE-SA led to substantial reductions of numerical effort in relation to DE-WF. These conclusions are supported by statistical analysis of the results of 30 runs of the optimization process for each constraint handling technique, for a distillation train. [Display omitted] • Differential Evolution was coupled with a Dynamic self-Adaptive constraint handling. • This optimization strategy tackled several kinds of constrained optimization problems. • Dynamic constraint showed better performance than weighted constraint. • Dynamic constraint led to obtain the best optimal values of the fitness function. • Dynamic constraint led to use less computational effort than weighted constraint. [ABSTRACT FROM AUTHOR]