In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of bandgap, dye concentration, photocatalyst dosage, solution volume, specific surface area, and time duration on photocatalytic degradation rate constant of cationic dyes was discerned using selected ML models, i.e., ensembled learning tree (ELT), gaussian process regression (GPR), support vector machine (SVM), and decision tree (DT). Thus, the data points were sourced from literature studies recently published in 2024 and 2023 on materials related to working on fundamental principles of photocatalysis. The ELT-PSO hybrid model outperformed all models with R² = 0.992 and RMSE = 2.6408e^-04, followed by DT, GPR, and SVM. The partial dependence plots and Shapley's analysis demonstrate that the type of dye, bandgap, dye initial concentration, and time duration are essential parameters for photocatalytic degradation, while sensitivity analysis further displayed solution volume and time duration to be the most influential parameters for rate constant determination. The optimized ML model's prediction was also experimentally validated using as-synthesized different compositions of Cu₂O/WO₃ heterostructures and ZnO nanoparticles. The results suggest that an ML-optimized study can be used in designing photocatalysts with optimum properties desired for the removal of cationic dyes at high rates from wastewater, thus saving energy and cost for a sustainable environment.

Keywords: Cationic dyes; Machine learning; Optimization removal; Photocatalytic degradation; Wastewater treatment.

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在这项研究中，结合了遗传算法（GA）和粒子群优化（PSO）的机器学习（ML）模型被用来预测光催化染料去除实验参数的相对影响。具体来说，使用选定的ML模型——集成学习树（ELT）、高斯过程回归（GPR）、支持向量机（SVM）和决策树（DT），分析了带隙、染料浓度、光催化剂剂量、溶液体积、比表面积以及时间持续性对阳离子染料光催化降解速率常数的影响。因此，数据点来自最近2024年和2023年发表的相关于光催化基础原理的文献研究。ELT-PSO混合模型的表现优于所有其他模型，其R²值为0.992且RMSE为2.6408e⁻⁰⁴，其次是DT、GPR和SVM。部分依赖图和Shapley分析表明，染料类型、带隙、初始染料浓度以及时间持续性是光催化降解的重要参数，而敏感性分析进一步显示溶液体积与时间持续性为确定速率常数的最重要因素。通过合成不同组分的Cu₂O/WO₃异质结构和ZnO纳米颗粒进行了优化ML模型预测的实验验证。结果表明，基于机器学习优化的研究可用于设计具有最佳性能以高效去除废水中的阳离子染料的光催化剂，从而节省能源和成本，为可持续环境做出贡献。

关键词： 阳离子染料；机器学习；优化去除；光催化降解；污水处理。