Synergistic effect on co-pyrolysis of rice husk and sewage sludge by thermal behavior, kinetics, thermodynamic parameters and artificial neural network

•Co-pyrolysis of RH, SS and their blends were evaluated using thermogravimetric study.•Coats-Redfern integral method successfully applied on five major reaction mechanisms to estimate co-pyrolysis kinetics.•Thermodynamic parameters of pure biomass and their blends were elucidated using kinetic data at two temperature regions.•ANN is, for the first time, applied to co-pyrolysis process. This study investigates the thermal decomposition, thermodynamic and kinetic behavior of rice-husk (R), sewage sludge (S) and their blends during co-pyrolysis using thermogravimetric analysis at a constant heating rate of 20 °C/min. Coats-Redfern integral method is applied to mass loss data by employing seventeen models of five major reaction mechanisms to calculate the kinetics and thermodynamic parameters. Two temperature regions: I (200–400 °C) and II (400–600 °C) are identified and best fitted with different models. Among all models, diffusion models show high activation energy with higher R2(0.99) of rice husk (66.27–82.77 kJ/mol), sewage sludge (52.01–68.01 kJ/mol) and subsequent blends (45.10–65.81 kJ/mol) for region I and for rice husk (7.31–25.84 kJ/mol), sewage sludge (1.85–16.23 kJ/mol) and blends (4.95–16.32 kJ/mol) for region II, respectively. Thermodynamic parameters are calculated using kinetics data to assess the co-pyrolysis process enthalpy, Gibbs-free energy, and change in entropy. Artificial neural network (ANN) models are developed and employed on co-pyrolysis thermal decomposition data to study the reaction mechanism by calculating Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R2). The co-pyrolysis results from a thermal behavior and kinetics perspective are promising and the process is viable to recover organic materials more efficiently.