Integrated kinetic, isotherm, ANN, and RSM-based optimization modelling of acid brown 14 removal using ethylenediamine-decorated Delonix regia pod biochar

Abstract

The increasing discharge of recalcitrant azo dyes from textile and industrial effluents poses serious ecological and human health risks, necessitating the development of sustainable and cost-effective treatment strategies. The objective of the present study is to examine the adsorption performance of ethylenediamine-functionalized Delonix regia pod biochar (DRPB-ED) for the efficient removal of Acid Brown (AB14) 14 dye from aqueous solutions. The approach integrates the valorization of agricultural waste with surface amine functionalization to enhance adsorption performance. Batch experiments demonstrated a maximum removal efficiency of 98.6% under optimized conditions (pH 4.0, 1.0 g/L dosage, 100 mg/L initial concentration, 60 min). Equilibrium data were best described by the Langmuir model, with a maximum monolayer adsorption capacity (qmax) of 60.61 mg/g, indicating favorable monolayer adsorption. Kinetic analysis followed a pseudo-second-order model (R2 = 0.99), suggesting dominant chemisorption interactions facilitated by amine functional groups. Process optimization using response surface methodology (RSM) achieved high desirability (D = 0.97), while artificial neural network (ANN) modeling demonstrated strong predictive capability (R2 = 0.98). Unlike many conventional biochar-based adsorbents that rely solely on physical adsorption, the present study introduces targeted amine functionalization combined with integrated statistical and machine learning modeling, providing both enhanced adsorption performance and robust process predictability. The findings highlight the dual environmental and technological significance of transforming low-cost biomass into high-efficiency adsorbents for sustainable wastewater treatment applications.