Practical approach to identify electrochemical parameter in aqueous Potassium ferricyanide by solving the multi-variable Cottrell equation via genetic algorithms

Parameters optimization of emerging electrochemical processes is crucial for improving efficiency and cost. Herein, a procedure based on the genetic algorithms (GA) search method was proposed to solve the fundamental Cottrell equation. First, a basic GA background is provided to help students and researchers understand the basic concepts of such evolutional algorithms and how this can be applied in their field of expertise. Then, the study was conducted considering measured chronoamperometry data of potassium ferricyanide, as electroactive species, at the platinum working electrode. The number of electrons (n), species concentration (C), and diffusion coefficient (D) were the three unknown solved variables that were obtained through optimization. The crossover function and population size effect were deeply studied on the final value of these electrochemical parameters. The results show that Intermediate and Heuristic crossover stochastic functions had the best performance in solving the multi-objective function, according to the root mean square error (RMSE) outcome. Thus, it was concluded that GA is a feasible tool that can be adopted to solve complex and multivariable problems in electrochemistry. Furthermore, it should be highlighted that GA can be adopted to determine chemical and electrochemical parameters in emerging technologies such as energy conversion devices.

Keywords: Genetic algorithm, chronoamperometric, Cottrell equation, crossover functions, heuristic.

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