Advanced Fuzzy Neural Network Modeling of Ni(II) Ion Adsorption on Modified Biochar: Synergizing Experimental and Computational Analysis

Due to the necessary functioning of society, the contamination of water bodies by heavy metals, in particular, by nickel (Ni(II)), is a serious threat to the environmental and human health. In this work, we explore the capability of adsorbing Ni(II) ion by surface modified biochar through an extensive experimental and computational approach. Agro Waste was processed to prepare biochar and chemically modified for increase in surface functionality and adsorption performance. Key parameters on batch adsorption experiment, including pH, time, ion concentration and biochar dosage were evaluated. A deep fuzzy neural network (DFNN) model was developed and validated using experimental data to predict Ni(II) adsorption behavior under diverse conditions, demonstrating excellent predictive accuracy. The conventional modeling techniques could not capture non-linear interactions of adsorption variables, whereas DFNN model was successful to determine non-linear interactions among variables of adsorption including temperature, pressure and water concentration. Comparative analysis confirmed the model's validity, showing a close correlation between experimental outcomes and predicted values. The result of this integrated framework shows that the surface engineered biochar can be integrated with the advanced artificial intelligence tools to optimize heavy metal remediation processes. Combining experimental and computational insight fosters understanding about metal adsorption mechanism as well as implementation of scalable and sustainable water treatment strategies for Ni(II) as well as other toxic metal contaminants.