Thermochemical valorization of mixed biomass into activated hydrochar for rapid Congo red removal: Integrating adsorption studies with machine learning

Azo dye pollution from compounds such as Congo red (CR) remains a major environmental concern due to their persistence, toxicity, and resistance to conventional treatment methods. This study demonstrates a sustainable biomass waste valorization strategy by converting mixed lignocellulosic residues into NaOH-activated hydrochar through hydrothermal carbonization and evaluating its efficiency for CR removal. Characterization confirmed the formation of an amorphous, porous structure enriched with oxygen-containing functional groups that enhanced adsorption performance. Batch experiments showed rapid adsorption, achieving nearly 99% CR removal within 10 min under acidic conditions at 30°C. Kinetic data followed the pseudo-second-order model, while equilibrium data were best described by the Langmuir isotherm, indicating favorable monolayer adsorption with a maximum capacity of 59.85 mg g⁻¹ . Thermodynamic analysis confirmed that the process was spontaneous and exothermic. The hydrochar maintained over 80% removal efficiency after three regeneration cycles, demonstrating good reusability. Adsorption mechanisms involved electrostatic attraction, hydrogen bonding, π–π interactions, and pore-filling effects. To support process optimization, machine learning (ML) models were developed using experimental data. ML analysis (Random Forest regression) identified initial dye concentration, pH, and temperature as the most influential factors affecting adsorption performance. By Leave-One-Out Cross-Validation which showed satisfactory predictive capability for the experimental datasets investigated The integration of hydrothermal biomass valorization with AI-driven analysis provides an effective and sustainable approach for wastewater remediation and resource recovery.