RSM-optimized biochar production from young coconut waste (Cocos nucifera): Multivariate analysis of non-linear interactions between temperature, time, and activator concentration
Talitha Philofia Sopandi, Akhmad Adi Sulianto, Fajri Anugroho, Mohd Zulkhairi Mohd Yusoff, Mohd Shamzi Mohamed, Mohammed Abdillah Ahmad Farid, Hendrix Yulis Setyawan
Abstract
Organic waste, particularly from young coconut husks, comprises over 60 % of landfill material, presenting significant environmental challenges. This study presents a biochar production method that integrates Response Surface Methodology (RSM) to optimize the carbonization process. The optimization framework uncovers non-linear interactions between carbonization temperature, duration, and KOH concentration, improving prediction accuracy. Carbonization temperature emerged as the most influential factor, with the optimal conditions, 400°C, 165.8 min, and 4.07 M KOH, yielding biochar with an iodine number of 1398.2 mg/g and an MB adsorption efficiency of 93.9 %. The enhanced model achieved an attractiveness index of 90.2 %, a significant improvement compared to conventional methods (84.5 %). Validation experiments confirmed the model’s precision, with actual results closely matching predictions: a biochar yield of 25.7 %, an iodine number of 1395.7 mg/g, and MB adsorption of 94.3 %, all within a variance of less than 2 %. This represents a nearly identical iodine number, with only a 0.2 % difference, and a 6.3 % improvement in MB adsorption capacity compared to commercial biochar. This approach significantly advances biochar production from young coconut waste and expands its applications in sustainable waste management. • Young coconut husks make up over 60 % of landfill waste, requiring management. • RSM identified optimal biochar production conditions via non-linear interactions. • Produced biochar had an iodine number of 1398.2 mg/g and 93.9 % MB adsorption. • Achieved a 90.2 % attractiveness index, outperforming conventional methods. • Results matched predictions closely, confirming the model's accuracy.