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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 NaNO 3 (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. [Display omitted] • Aqueous solutions percentage copper (Cu) ions adsorption is predicted. • The feasibility of the artificial intelligence (AI) models is adopted in this study. • Five different modeling scenarios based on the related parameters are investigated. • Uncertainty analysis using bootstrap method is applied. • Results are evidence the potential of the AI models for the Cu prediction. [ABSTRACT FROM AUTHOR]

The main objective of the present study is the investigation of the adsorption efficiency of raw and heat-treated attapulgite clay for removing Pb and Cu from aqueous solutions. The removal of each metal was studied separately with the use of one-substance solutions. The effect of certain factors, including solution pH and ionic strength, contact time, adsorbent concentration, temperature of treatment of the adsorbent, and initial metal concentration, was studied. In alkaline conditions, pH > 8.0, precipitation of Pb(OH)2 and Cu(OH)2 takes place, whereas at pH range 4.0–8.0 removal of metals is probably due to adsorption processes. Metal removal increases by 20% for Pb and by 80% for Cu with the increase of attapulgite content from 2 g·L−1 to 15 g·L−1. The removal of metals decreases with increasing solution ionic strength due to competition of Na with Pb and Cu for the available sites. Significant changes in the adsorption capacity of the used attapulgite clay occur after calcination in temperatures >550 °C due to destruction of the crystal lattice of the material and nano-porosity change. Finally, Pb adsorption is described well by both Langmuir and Freundlich isotherm models. According to the Langmuir model, the maximum adsorption capacity for Pb is 30 mg·g−1 and 4.41 mg·g−1 for Cu. The Freundlich model fitted better the experimental data of Cu. [ABSTRACT FROM AUTHOR]