Pyrolysis behavior of low value biomass (Sesbania bispinosa) to elucidate its bioenergy potential: Kinetic, thermodynamic and prediction modelling using artificial neural network

Abstract

Energy needs are dynamic, and the increasing demand for energy prompted the completion of this study to explore the thermal degradation characteristics of Sesbania bispinosa (SB) biomass in order to assess its pyrolytic performance for biofuel generation. The physicochemical analysis revealed lower moisture (6.30%), higher volatile (79.35%), and lower ash (3.95%). The results of the TG investigation indicated that the maximum devolatilization temperature range during the thermal deterioration of SB was 297–650 °C. The model-free approaches of Ozawa Flynn Wall (OFW), Kissinger Akahira Sunose (KAS), Tang (TG), Starink (SK), and Vyazovkin (VZK) were used to predict kinetic parameters. The average activation energy obtained was 181.37, 180.63, 180.91, 180.90, and 161.31 kJ/mol using the OFW, KAS, TG, SK, and VZK methods, respectively. The comprehensive pyrolysis index (CPI) showed a greater value at higher heating rates (50 °C/min), indicating the appropriateness of SB pyrolysis at higher heating rates. Further, artificial neural network (ANN) was employed for the prediction of thermal degradation data. Results revealed a strong correlation between actual and predicted values, which are much nearer to one. The investigation demonstrated the importance of ANN model and suitability of SB biomass as a potential feedstock for bioenergy production via pyrolysis.