Comparative Study of Machine Learning Algorithms for Cr(VI) Adsorption Optimization: A Case Study Using KOH-Activated Wood Charcoal - Moh. Azhar Afandy; Fikrah Dian Indrawati Sawali (Equilibrium Journal of Chemical Engineering, 2026)
The removal of toxic Cr(VI) ions from industrial wastewater remains a pressing environmental concern due to their high mobility and carcinogenic nature. This study presents a data-driven approach for modeling and optimizing Cr(VI) adsorption onto KOH-activated wood charcoal using various machine learning (ML) algorithms. A dataset derived from batch adsorption experiments was used, involving three operational parameters: initial Cr(VI) concentration (10–50 mg/L), contact time (40–120 min), and adsorbent dose (0.5–1.5 g). Six supervised regression models such as Linear Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, and k-Nearest Neighbors (kNN) were evaluated. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) dan mean square error (MSE). Gradient Boosting and Decision Tree showed superior predictive accuracy, with R² values of 0.89 and 0.87, respectively. Feature importance analysis revealed initial concentration as the most influential factor, followed by contact time and adsorbent dosage. These findings highlight the potential of ML as an effective tool for predicting and optimizing adsorption processes in environmental remediation. The integration of ML methods supports efficient decision-making, particularly under constraints of limited experimental data, and aligns with digital transformation strategies in wastewater treatment.