Data-driven prediction of chemical oxygen demand in dairy wastewater coagulation using a magnetic Moringa Oleifera–derived coagulant

Resumen

The increasing environmental impacts of industrial effluents have intensified the search for alternative wastewater treatment technologies. In this study, a magnetized natural coagulant based on Moringa oleifera–CoFe₂O₄ composite was synthesized and evaluated for the treatment of dairy wastewater. A Box–Behnken experimental design comprising 27 runs was employed to investigate the effects of coagulant dosage (9–16 g), pH (4–12), settling time (15–30 min), and mixing rate (50–150 rpm) on chemical oxygen demand (COD) removal efficiency. Experimental results showed COD removal ranging from 70.13% to 89.45%. To model the nonlinear relationships between operational parameters and COD removal, a multilayer perceptron artificial neural network (ANN) was developed using 27 experimental observations, divided into training (77.8%) and testing (22.2%) datasets. The optimized ANN architecture (4–2–1 topology) demonstrated high predictive accuracy, achieving relative errors of 6.6% and 6.3% for training and testing phases, respectively. The model exhibited excellent agreement between predicted and experimental values, with a coefficient of determination (R²) of 0.9946. Variable importance analysis revealed that pH was the dominant factor controlling coagulation performance, followed by settling time, while coagulant dosage and mixing rate showed comparatively lower influence. The results confirm that magnetized M. oleifera represents a promising eco-friendly alternative to conventional chemical coagulants and that ANN-based modeling provides a robust and reliable tool for process predictive and potential scale-up of natural coagulant-based wastewater treatment systems.

Keywords: artificial neural network (ann), chemical oxygen demand (cod), dairy wastewater treatment, magnetized Moringa oleifera, multilayer perceptron, natural coagulant.