Response surface methodology (RSM), multi-layer perceptron trained by Levenberg-Marquardt (MLP-LM); multi-layer perception and Sigma-Pi neural networks trained by particle swarm optimization (PSO) were used to effectively and reliably predict the performance of Classical-Fenton and Photo-Fenton processes. H₂O₂ doses, Fe(II) doses, and H₂O₂/Fe(II) rates were determined as independent variables in batch reactors. The performance of models was compared by using RMSE and MAE error criteria. The performance of models was also evaluated in terms of some properties of regression analysis and scatter that showed high linear relationship between the predictions of SP-PSO and the actual removal values. As a distinctive aspect of this study, SPNN trained by PSO was used for the first time in the literature in this area and the best predictive results for almost all cases were generated. Moreover, the genetic algorithm (GA) was applied for SP-PSO model results to determine the optimum values of the study. According to the results of GA, under the optimum conditions Photo-Fenton processes had higher performance in each experiment. Thereby, SP-PSO produced satisfactory prediction results without the need for any additional experiments in the case that experimental designs are difficult or costly for wastewater treatment.

响应面方法 (RSM)，由 Levenberg-Marquardt (MLP-LM) 训练的多层感知器；使用粒子群优化 (PSO) 训练的多层感知和 Sigma-Pi 神经网络有效且可靠地预测 Classical-Fenton 和 Photo-Fenton 过程的性能。H ₂ O ₂剂量、Fe(II) 剂量和 H ₂ O ₂/Fe(II) 速率被确定为间歇反应器中的独立变量。模型的性能通过使用 RMSE 和 MAE 误差标准进行比较。模型的性能也根据回归分析和散点的一些特性进行了评估，这些特性显示了 SP-PSO 的预测与实际去除值之间的高度线性关系。作为本研究的一个独特方面，PSO 训练的 SPNN 在该领域的文献中首次被使用，并且对几乎所有案例都产生了最佳预测结果。此外，将遗传算法 (GA) 应用于 SP-PSO 模型结果以确定研究的最佳值。根据遗传算法的结果，在最佳条件下，Photo-Fenton 工艺在每个实验中都具有更高的性能。从而，