Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO₃ (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R² = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

废水中的铜（Cu）离子被认为是要量化的关键危险元素之一。这项研究旨在使用计算机辅助模型预测凹凸棒石粘土从水溶液中吸附铜离子（Ad）的过程。为此，开发了三种人工智能（AI）模型，包括基于网格优化的随机森林（Grid-RF），人工神经网络（ANN）和支持向量机（SVM）。主成分分析（PCA）用于从不同变量中选择模型输入，包括铜的初始浓度（IC），凹凸棒石粘土的剂量（Dose），接触时间（CT），pH和NaNO _3的添加（SN） 。发现ANN模型可预测具有最小均方根误差（RMSE = 0.9283）和最大确定系数（R ²）的Ad= 0.9974），而所有变量（即IC，Dose，CT，pH，SN）均视为输入变量。当使用少量预测变量时，发现Grid-RF模型的预测精度与ANN模型相似。根据预测精度，可以将模型排列为：ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5。总体而言，对结果进行的统计分析表明，可以将ANN和Grid-RF模型用作计算机辅助模型，以监测和模拟凹凸棒石粘土从水溶液中的吸附。