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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]

A wide range of dyes are being disposed in water bodies from several industrial runoff and the quantity is rapidly increasing over the years. From an environmental safety point of view, it is urgent to improve the removal process of dyes. It is important to understand the stochastic and highly redundant behavior of the process of dye removal (D R) in wastewater treatment. This leads to better utilization of Machine Learning (ML) models for both optimization as well as prediction of the D R process efficiency. In this review, 200 papers (Years, 2006–2021) have been systematically reviewed from the Web of Science and Scopus-indexed journals, covering a total of 84 journals. All applied ML models have been thoroughly studied in the review and analyzed in terms of their architecture setup, hyper-parameters selection, performance, advantages, and disadvantages. A wide range of optimization methods for hyper-parameters tuning were analyzed and discussed scientifically. Explicit information about the data sizes, splitting structure for training-validation-testing along with input and output selection approaches have been logically reviewed and discussed. Data availability, transparency, and reusability have been reported adequately. Various software for data-driven modeling have been discussed with their pros and cons. Trends in statistical evaluators (among about 60 types) have been discussed with their pros and cons including their sensitivity with the data fluctuations. Moreover, the most popular performance metrics have reported. In addition, the D R mechanism has reviewed and discussed inclusively. Extensive media used for the decolorization were discussed thoroughly, including their physical and chemical characteristics, along with feasibility and equilibrium data based on Langmuir model. The cost of the applied media in the decolorization process reported adequately. Finally, the research gap and future road map of the next 5 years, which bridge the gap of the domain are scientifically drafted along with the limitations. This critical review not only provides the appraisal of growth of D R research integrated with ML in the last couple of decades but also scouts the potential studies where all experimental, chemical and modeling processes should be taken under consideration. [Display omitted] • An extensive review on dye removal modeling established over 2 decades. • All machine learning predictive models and optimizers are exhibited and assessed. • Data availability, application of hyper-parameter optimization are analyzed. • Removal media, dye color, and economical global assessment are appraised. • 5 years of future prospective road map are suggested to be explored. [ABSTRACT FROM AUTHOR]