Modified particle swarm optimisation to determine the kinetic parameters of 2-chlorophenol oxidation in supercritical water

Le résumé fourni par la source

An analytical chemical kinetics model can investigate the impact of concentrations, pressures, temperatures, and catalysts on the rates of reactions. It serves as the foundation for process design, effectiveness, and control. Nevertheless, the chemical kinetics model presents numerous dynamic characteristics that pose challenges in terms of prediction based on experimental empirical evidence. The present work employed three particle swarm optimisation (PSO) algorithms in order to determine the optimal parameters of the kinetic model. We wanted to reduce root mean squared errors. The operators’ efficacy is shown by numerical tests on benchmark functions and comparison to the fundamental GWO and ABC operators. The computational findings demonstrate that m-PSO shows a least RMSE value of 0.043 in comparison to other models and also significantly enhances both accuracy and convergence rate compared to other described methods. The model's improved search capabilities are shown by the kinetic parameter estimate findings utilising supercritical water oxidation experimental data.

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