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Abstract：
In this research, an upgraded and environmentally friendly process involving WO3/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation. The primary goal of applying different models was to achieve the best conditions with high accuracy in Cefixime degradation. Based on R analysis, the quadratic factorial model in RSM was selected as the best model, and the regression coefficients obtained from it were used to evaluate the performance of artificial intelligence models. According to the quadratic factorial model, interactions between pH and time, pH and catalyst amount, as well as reaction time and catalyst amount were identified as the most significant factors in predicting results. In a comparison between the different models based on Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2 Score) indices, the SVR model was selected as the best model for the prediction of the results, with a higher R2 Score (0.98), and lower MAE (1.54) and RMSE (3.91) compared to the ANN model. Both ANN and SVR models identified pH as the most important parameter in the prediction of the results. According to the Genetic Algorithm, interactions between the initial concentration of Cefixime with reaction time, as well as between the initial concentration of Cefixime and catalyst amount, had the greatest impact on selecting the optimal values. Using the Genetic Algorithm and SOLVER models, the optimum values for the initial concentration of Cefixime, pH, time, and catalyst amount were determined to be (6.14 mg L-1, 3.13, 117.65 min, and 0.19 g L-1) and (5 mg L-1, 3, 120 min, and 0.19 g L-1), respectively. Given the presented results, this research can contribute significantly to advancements in intelligent decision-making and optimization of the pollutant removal processes from the environment.
摘要：
在这项研究中,涉及WO3/Co-ZIF纳米复合材料的升级和环境友好的工艺用于从水溶液中去除头孢克肟。智能决策使用各种模型，包括支持向量回归(SVR)，遗传算法(GA)，人工神经网络(ANN)，可视化Excel结果的模拟优化语言(SOLVER)，和响应面法(RSM)。SVR,ANN,和RSM模型用于建模和预测结果，同时采用GA和SOLVER模型来实现头孢克肟降解的最佳条件。应用不同模型的主要目标是在头孢克肟降解中实现高精度的最佳条件。基于R分析,RSM中的二次阶乘模型被选为最佳模型，并将由此获得的回归系数用于评估人工智能模型的性能。根据二次阶乘模型，pH和时间之间的相互作用，pH值和催化剂用量,以及反应时间和催化剂量被确定为预测结果的最重要因素。在基于平均绝对误差(MAE)的不同模型之间的比较中，均方根误差(RMSE),和确定系数(R2评分)指数，选择SVR模型作为预测结果的最佳模型，具有较高的R2评分（0.98），与ANN模型相比，MAE(1.54)和RMSE(3.91)较低。ANN和SVR模型都将pH值确定为结果预测中最重要的参数。根据遗传算法,头孢克肟的初始浓度与反应时间之间的相互作用，以及头孢克肟的初始浓度和催化剂量之间，对选择最优值的影响最大。利用遗传算法和SOLVER模型，头孢克肟初始浓度的最佳值，pH值,时间,和催化剂量确定为（6.14mgL-1，3.13，117.65分钟，和0.19gL-1）和（5mgL-1，3，120分钟，和0.19gL-1)，分别。鉴于呈现的结果，这项研究可以显着促进智能决策和优化从环境中去除污染物的过程。
