Thermo-kinetic analysis of pyrolysis of chickpea stalk using thermogravimetric analysis and artificial neural network

This research used artificial neural networks (ANN) to predict the complex pyrolysis behaviour of chickpea stalk (CS) using factors such as temperature (degrees C) and heating rate (degrees C/min). This is the first comprehensive kinetic and thermodynamic analysis of CS during pyrolysis thermal degradation, using thermogravimetric analysis (TGA) at four different heating rates. Kinetic parameters were determined using the Flynn-Wall-Ozawa (FWO), KissingerAkahira-Sunose (KAS), and Starink methods. Results revealed that CS undergoes mass loss in three stages and major volatile degradation occurring between 143 and 374 degrees C. The average activation energies for FWO, KAS, and Starink models were 301.01, 291.11, and 306.42 kJ/mol, respectively, with no significant differences. Thermodynamic parameters such as enthalpy, entropy, and Gibbs free energy were critically explained. The master plot shows strong agreement with the order-based, diffusion, and power-law models. This leads to the conclusion that chickpea stalk contains potential as feedstock.