Hybrid Analysis of Biochar Production from Pyrolysis of Agriculture Waste Using Statistical and Artificial Intelligent-Based Modeling Techniques - Hani Hussain Sait; Ramesh Kanthasamy; Bamidele Victor Ayodele (Agronomy)
Biochar is gaining recognition as a sustainable material, with several applications in soil amendment, carbon sequestration, nutrient dynamics, the remediation of organic contaminants from soil, and water filtration. However, understanding its characteristics is limited due to its intricate structure. This study used response surface methodology (RSM) and artificial neural networks (ANNs) to optimize and predict the production of biochar from the pyrolysis of palm kernel shells. To determine how residence time, nitrogen flow rate, and pyrolysis temperature affected biochar production, a Box–Behnken experimental design was employed. The prediction of the biochar yield was modeled using four different models of ANNs: narrow, medium, wide, and optimum. The physicochemical properties of the biochar produced at pyrolysis temperatures ranging from 400 to 800 °C were determined using X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), nitrogen (N2) physisorption analysis, and field emission scanning electron microscopy (FESEM). With a p-value significantly lower than 0.05, the response surface quadratic model was found to be the most suitable to optimize the biochar yield obtained from the PKS pyrolysis. Biochar production was very sensitive to the three operating parameters: pyrolysis temperature, nitrogen flow rate, and pyrolysis time. With a coefficient of determination (R2) of 0.900, root mean square error (RMSE) of 0.936, and mean absolute error (MAE) of 0.743, the optimized ANN outperformed the other three ANN models tested. When compared to the optimized ANN, the response surface quadratic model with an R2 of 0.989 had better prediction of biochar yield. At optimized experimental conditions for nitrogen flow rate (150.01 mL/min), temperature (799.71 °C), and pyrolysis time (107.61 min), a biochar yield of 37.87% was obtained at a desirability function of 1.