One of the most essential chemical processes that is utilized in the manufacturing of a great deal of contemporary goods is called heterogeneously catalyzed reactions, and it is also one of the most fascinating. Metallic nanostructures are heterogeneous catalysts for range reactions due to their huge surface area, large assembly of active surface sites, and quantum confinement effects. Unprotected metal nanoparticles suffer from irreversible agglomeration, catalyst poisoning, and limited life cycle. To circumvent these technical disadvantages, catalysts are frequently spread on chemically inert materials like as mesoporous Al2O3, ZrO2, and different types of ceramic material. In this research, plentiful bauxite residue is used to create a low-cost alternative catalytic material. We have hydrogenated p-Nitrophenol to p-Aminophenol on bauxite residue (BR) supported silver nanocomposites (Ag NCs). The phase and crystal structure, bond structure and morphological analysis of the developed material will be done XRD, FTIR, and SEM-EDX respectively. The ideal conditions were 150 ppm of catalyst, 0.1 mM of p-NP, and 10 minutes overall up-to 99% conversion of p-NP to p-AP. A multi-variable predictive model created using Response Surface Methodology (RSM) and a data-based Artificial Neural Network (ANN) model were found to be the best ways to predict the maximum conversion efficiency. ANN models predicted efficiency more accurately than RSM models, and the strong agreement between model predictions and experimental data was indicated by their low relative error (RE0.10), high regression coefficient (R2>0.97), and Willmott-d index (dwill-index > 0.95) values.
Keywords: Bauxite residue; catalytic conversion; nanocomposites; optimization; organic molecules.