Integrating machine learning and electrochemistry: A hybrid SA-DE-RF approach for optimizing electrode composition in water treatment

Abstract

The imperative to address the burgeoning challenge of industrial water pollution has catalyzed the pursuit of sophisticated treatment methodologies capable of neutralizing non-biodegradable contaminants. This investigation focuses on the optimization of Ti/SnO2-Sb-Ni anode composition, leveraging a synergistic hybrid machine learning strategy that integrates Simulated Annealing (SA), Differential Evolution (DE), and Random Forest (RF) algorithms. This triad of algorithms is meticulously applied to ascertain the optimal doping concentrations of antimony (Sb) and nickel (Ni), pivotal in maximizing the efficacy of electrochemical oxidation processes. Our findings elucidate that the meticulously optimized Ti/SnO2-Sb-Ni anode, with a specific doping ratio of 100:3.74:5.36, engenders a degradation efficiency of benzoic acid (BA) that approximates completeness within a constrained temporal framework of 60 minutes. The RF regression model, exemplifying a robust R² value of 0.9565 and an RMSE of 0.0576, surpasses its counterparts in prognostication accuracy, underscoring its preeminence in electrode composition optimization for the amelioration of electrochemical water treatment matrices. The convergence of SA, DE, and RF methodologies not only refines the predictive fidelity of the degradation kinetics but also extricates the optimization process from the conventional constraints, thereby enhancing the scalability and precision of the model. This research advances the frontiers of electrochemical oxidation and etches a pioneering niche for the amalgamation of machine learning within environmental engineering paradigms. The hybrid algorithmic scaffolding proffered herein is suggested as a robust and versatile framework, propelling forward the discourse on the remediation of polluted water bodies and offering a springboard for future investigative endeavors.