Investigation of pyrolysis potential of date seeds for bioenergy production: kinetic study by thermal analysis and activation energy prediction using advance predictive models

The pyrolysis potential of date seeds (DS) (an abundant agricultural residue that can support sustainable and resilient energy systems) as a renewable bioenergy feedstock was examined. Thermogravimetric analysis (TGA) of date seeds was performed from ambient temperature to 1000 °C under nitrogen at heating rates of 6, 9, 12, and 15 °C/min. The feedstock showed high volatile matter and a higher heating value (HHV) of 20.185 MJ/kg, confirming its suitability for thermochemical conversion. Isoconversional model-free methods such as Friedman (FR), Kissinger Akahira-Sunose (KAS), Ozawa-Flynn-Wall (OFW) and an advanced Vyazovkin (VZ) approach were applied over a conversion range of 0.2 to 0.8. A linear combined kinetics analysis gave an apparent activation energy (E a ) of 272.4 ± 11.8 kJ/mol with a correlation coefficient of 0.9948 with the reaction order of 7.75 ± 0.27. Four machine learning (ML) models, including Artificial Neural Networks (ANN), Classification and Regression Trees (C&RT), Boosted Regression Trees (BRT), and Multivariate Adaptive Regression Splines (MARS), were used to predict E a obtained from thermogravimetric data. The ANN achieved the best performance metrics, with a coefficient of determination (R2) of 0.985 and a Root Mean Squared Error (RMSE) of 3.84. The integrated kinetic and machine-learning framework provides reliable estimates of E a for DS pyrolysis. The predicted E a determines the temperature sensitivity of pyrolysis, setting the required heating rate, residence time, and temperature profile in the reactor. These results provide process-level input for reactor design, scaling-up, and optimizing bioenergy production from DS waste.