Studies on the direct application of the photo-Fenton process (PFOP) to disinfect and decontaminate textile wastewater are rare. The output of the artificial neural network (ANN) models applied to the wastewater of a textile factory producing woven fabrics, which is used to assess the efficiency of the PFOP process, are investigated and compared with each other in this study. The highest PFOP efficiency is obtained at a pH of 3. Chemical oxygen demand (COD), suspended solids (SS) and color removal rates are 94%, 90%, and 96%, respectively. The data are modeled with ANNs and nonlinear external input autoregressive ANNs (NARX-ANN) using the MATLAB R2020a software program. Both Levenberg–Marquardt (trainlm) and scaled conjugate gradient (trainscg) algorithms are employed in the ANN and NARX-ANN models, whereas hyperbolic tangent sigmoid (Tansig) and logistic sigmoid (Logsig) functions are superimposed on the hidden layer in the ANN model, and Tansig functions are superimposed on the NARX-ANN model. It is determined that the developed ANN models are more effective in estimating the PFOP efficiency. The mean squared error is 0.000 953, and the coefficient of determination (R²) is 0.96 661.

中文翻译：

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Photo-Fenton 过程作为单一单元在纺织废水处理中的应用和建模

直接应用光芬顿过程（PFOP）消毒和净化纺织废水的研究很少。人工神经网络 (ANN) 模型的输出应用于生产机织织物的纺织厂的废水，用于评估 PFOP 工艺的效率，在本研究中进行了调查和相互比较。在 pH 值为 3 时获得最高的 PFOP 效率。化学需氧量 (COD)、悬浮固体 (SS) 和颜色去除率分别为 94%、90% 和 96%。使用 MATLAB R2020a 软件程序使用 ANN 和非线性外部输入自回归 ANN (NARX-ANN) 对数据进行建模。Levenberg–Marquardt (trainlm) 和缩放共轭梯度 (trainscg) 算法都用于 ANN 和 NARX-ANN 模型，而双曲正切 sigmoid (Tansig) 和 logistic sigmoid (Logsig) 函数叠加在 ANN 模型的隐藏层上，而 Tansig 函数叠加在 NARX-ANN 模型上。确定开发的 ANN 模型在估计 PFOP 效率方面更有效。均方误差为 0.000 953，决定系数 (R ² ) 为 0.96 661。