K. Shah, S. Mandal
Texas State University,
United States
Keywords: agro-waste, adsorption, wastewater treatment, AI-driven optimization
Summary:
This study explores a novel valorization route for plum pit biomass through pyrolysis to produce low-cost, high-efficiency biochar-based biosorbents (PPC and PPB) for dye-contaminated industrial wastewater treatment. The biosorbents were evaluated against structurally distinct dyes anionic Congo Red (CR) and cationic Crystal Violet (CV) demonstrating exceptional adsorption capacities of 100.0 mg/g for CV and 83.3 mg/g for CR, following Langmuir isotherm behavior and pseudo-second-order kinetics (R² > 0.98). A central innovation of this work is the integration of machine learning, using a Random Forest regression model, which achieved remarkable predictive accuracy (R² = 0.89% PPB and R² = 94 % PPC). The AI model efficiently guided experimental design by predicting dye removal performance under untested conditions, reducing the need for extensive laboratory trials. Feature importance and partial-dependence analyses identified the most influential parameters, allowing focused and effective experimentation. This AI-driven approach enhances operational efficiency, minimizes resource consumption, and accelerates process optimization, making it highly customer-friendly for industries seeking rapid, reliable, and cost-effective solutions. In performance comparisons, PPC outperformed commercial activated carbon for cationic dye removal and showed comparable performance for anionic dyes. Its reusability was excellent, with minimal loss of adsorption efficiency over multiple cycles. Additionally, PPC is significantly more cost-effective than commercial activated carbon, offering substantial economic advantages without compromising performance. The environmental implications of this research are significant. By converting plum pit agricultural waste into functional biosorbents, the study supports circular economy principles, reduces landfill burden, and mitigates toxic dye pollution, protecting aquatic ecosystems and human health. PPC’s scalability, high adsorption efficiency, cost-effectiveness, and AI-optimized performance provide a sustainable, green alternative for industrial wastewater treatment. Overall, plum pit-derived biochar represents a practical, eco-friendly, and economically attractive solution for advanced environmental remediation. Its combination of high efficiency, low cost, reusability, and AI-driven process optimization positions it as a customer-centric material for industries aiming to achieve sustainable water management, reduce operational costs, and promote circular economy practices. This work exemplifies how innovative, data-driven approaches can transform agricultural waste into high-value, intelligent materials for environmental sustainability.