A hybrid DNA editing-inspired gray wolf algorithm for kinetic parameter estimation of oxidation in supercritical water

A chemical kinetics model can be used to analyze the influence of various factors (such as concentrations, pressures, temperatures, and catalysts.) on reaction rates. It is the basis of process design, optimization and control. However, the chemical kinetics model often contains multiple adjustable parameters, which are very difficult to estimate accurately through experimental data by using traditional methods. In this paper, a hybrid DNA-GWO algorithm is proposed to obtain an accurate kinetics model. Inspired by the genetic operation, a new DNA crossover operator and mutation operator are designed to enhance the diversity of individuals and prevent prematurity. The effectiveness is proven by numerical experiments on benchmark functions, and the operators are compared with the basic GWO and PSO operators. The kinetic parameter estimation results on experimental supercritical water oxidation data also indicate the better search capability of the model.