Modeling and Optimization of Activated Carbon Yield From Sugarcane Bagasse Using RSM and Machine Learning

Abstract

The growing demand for sustainable and efficient water treatment solutions underscores the importance of high-quality activated carbon (AC) derived from renewable resources. In this study, AC was produced from sugarcane bagasse using sulfuric acid as an activating agent. A hybrid approach combining experimental design and advanced computational modeling was employed to optimize the production process and model the relationship between operational parameters and AC yield. A Box-Behnken design was used to systematically investigate the effects of four key variables: temperature, activation time, raw material-to-activating agent ratio, and acid concentration. The generated experimental data were used to develop and compare predictive models based on response surface methodology (RSM), support vector machine (SVM), and artificial neural networks (ANNs). All models demonstrated strong predictive capabilities, with ANN achieving R = 0.989 ± 0.003, outperforming SVM (R = 0.950 ± 0.004), while RSM showed a slightly higher overall fit (R = 0.996 ± 0.002). This study demonstrates that integrating experimental design with machine learning techniques enhances both the precision and efficiency of process optimization. The proposed approach offers a robust, scalable, and sustainable pathway for producing high-quality AC from agricultural waste, with significant potential for industrial applications in environmental remediation and water purification technologies.