journal article·2025-09Open access

Experimental optimization and machine learning modeling for sustainable Congo red dye removal from wastewater using activated goat bone biochar

Ghazala Muteeb, Adil Alshoaibi, Khalid Ansari

Abstract

This study explores the use of goat bone-based activated biochar (GB PAC ) synthesized from animal waste, as an efficient and sustainable adsorbent for removing Congo Red (CR) dye from aqueous solutions. GB PAC , prepared through chemical activation with phosphoric acid, was tested in batch adsorption experiments. FTIR analysis revealed key functional groups such as hydroxyl (O-H), carboxyl (C=O), and phosphate groups, which play a crucial role in the adsorption of CR dye through interactions like hydrogen bonding and electrostatic attraction. BET surface area analysis showed that GB PAC exhibited a surface area of 91.27 m 2 /g, with a mesoporous structure that enhances its adsorption capacity. The study systematically analyzed factors such as dye concentration (10-50 mg/L), adsorbent dosage (0.15-0.75 g/100 mL), pH (7.5), and contact time (30-180 min). The maximum adsorption capacity of GB PAC for CR dye was 83.33 mg/g, and the adsorption process followed the Langmuir isotherm model (R 2 = 0.9907) and pseudo-second-order kinetics. Process Optimization was performed using Response Surface Methodology (RSM) which enabled statistically guided experimental design and optimization of influential variables. Optimal conditions were identified as 48.596 mg/L dye concentration, 0.398 g adsorbent dose, and 88.23 minutes contact time, achieving a predicted removal efficiency of 94.34%. To enhance prediction capabilities, machine learning (ML) models specifically Decision Tree and Random Forest were trained using experimental data. These models demonstrated strong predictive accuracy, with R 2 values of 0.91 and 0.87, respectively. This dual-framework approach, combining RSM for optimization and ML for predictive modeling, underscores the novelty of using waste-derived GB PAC for wastewater treatment applications. The findings support GB PAC as a cost-effective, sustainable, and data-driven solution for CR dye removal from contaminated water.
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Research context

OpenAlex topics: Water Quality Monitoring and Analysis

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