Novel modeling and optimization framework for Navy Blue adsorption onto eco-friendly magnetic geopolymer composite

The disproportionate potency of dyes in textile wastewater is a global concern that needs to be contended. The present study comprehensively investigates the adsorption of Navy-Blue dye (NB) onto bentonite clay based geopolymer/Fe3O4 nanocomposite (GFC) using novel statistical and machine learning frameworks in the following steps; (1) synthesis and characterization of GFC, (2) experimental testing and modelling of NB adsorption onto GFC following Box-Behnken design and three response surface prediction models namely stepwise regression analysis (SRA), Support vector regression (SVR) and Kriging (KR), (3) parametric, sensitivity, thermodynamic and kinetic analysis of pH, GFC dose and contact time on adsorption performance, and (4) finding global parametric solution of the process using Latin Hypercube, Sobol and Taguchi orthogonal array sampling and combining SRA-SVR-KR predictions with novel hybrid simulated annealing (SA)-desirability function (DF) approach. Under the given testing range, parametric/sensitivity analysis revealed the critical role of pH over others accounting ∼37% relative effect and primarily derived the NB adsorption. The statistical evaluation of models revealed that all models could be utilized for elucidating and predicting the NB removal using GFC, however, SVR accuracy was better among others for this particular work, as the overall computed root mean squared error was only 0.55 while the error frequency counts remained