Response Surface Methodology and Deep Learning Modelling for the Removal of Pb2+ ions from Wastewater Using Apple Pomace
Great C. Chazuza, Felicia Omolara Afolabi, Paul Musonge · Journal of Environmental & Earth Sciences · 2025
Recent studies have demonstrated a growing global interest in utilising agricultural waste to remediate wastewater. This stems from growing apprehensions about high levels of heavy metals, especially Pb2+ ions, in wastewater produced by industrial processes such as mining, paint production, oil refining, smelting, and electroplating. This study examined apple pomace's Pb2+ ions adsorption from wastewater. Response Surface Methodology (RSM) was employed, utilising the central composite face-centred design (CCFD) with three variables: initial concentration (1–50 mg/L), adsorbent dosage (0.1–1 g), and particle size (75–425 µm) to formulate a mathematical model for the biosorption of Pb2+ ions on apple pomace. An artificial neural network (ANN) was developed using data generated from the RSM design. The CCFD and ANN models showed considerable efficacy in the adsorption process, exhibiting correlation coefficient values of 0.9921 and 0.9999, respectively. The isotherm and kinetic studies were performed, and the Freundlich Isotherm model best fitted the equilibrium data, with a correlation coefficient of 0.972 and a qe of 5.145 mg/g. Additionally, the pseudo-second-order model proved to be the most appropriate for the kinetic data, with an R2 of 0.9996. These results confirm that apple pomace functions as an effective, low-cost, and environmentally and sustainably biosorbent for the removal of Pb2+ ions from wastewater. Both RSM and ANN models exhibited high predictive capability for the biosorption process. While ANN provides more flexibility in modelling complex non-linear relationships, it is prone to overfitting, particularly with limited datasets, and this was addressed through a 5-fold cross-validation technique.