Machine learning and mechanistic studies on p-nitrophenol remediation using sustainable activated carbon

This study presents a sustainable approach for p-nitrophenol (pNP) removal by synthesizing activated carbon with a large surface area from waste Pistacia vera shells using H₃PO₄ activation. Comprehensive characterization confirmed a high specific surface area (670.25 m² g^{- 1}) and a heterogeneous structure rich in functional groups, which are beneficial for adsorption. Adsorption followed pseudo-second-order kinetics, and the equilibrium dataset followed the Langmuir isotherm, with a maximum adsorption capacity of 142.93 mg g^{- 1}. The thermodynamic results showed that the adsorption was a spontaneous and exothermic (- 13.43 kJ mol^{- 1}) process. ANN and ANFIS models were developed to predict the adsorption behavior. The ANFIS model exhibited the best predictive performance, with an R² of 0.9935. ANFIS sensitivity analysis identified that contact time and initial pNP concentration were the key factors. Regeneration tests demonstrated that the adsorbent could be reused for five cycles, supporting its practical applicability. Real water matrix studies have demonstrated robust pNP removal in real-world water scenarios, highlighting its environmental relevance. Thus, waste-derived adsorbents present a low-cost and sustainable option for pNP removal.