Thermogravimetric characteristics and predictive modelling of oil sand bitumen pyrolysis using artificial neural network

Abstract

Abstract This study investigated the pyrolysis of oil sand bitumen as an alternative energy source in response to the global energy crisis. Thermogravimetric analysis was performed at different heating rates in a nitrogen atmosphere. Low moisture and ash content and high volatile matter content were found by proximate analysis, indicating the possibility for effective pyrolysis and ignition. Fourier-transform infrared spectroscopy identified functional groups, and X-ray diffraction confirmed the absence of problematic expandable clay minerals. The Friedman, Flynn–Wall–Ozawa, and Kissinger–Akahira–Sunose models were used to calculate the kinetic parameters. The activation energies of the models were primarily between 100 and 200 kJ/mol, with the Friedman model having higher values. It was discovered that the process was non-spontaneous and endothermic. An artificial neural network model demonstrated the potential for future energy applications by accurately predicting the behaviour of pyrolysis.

Keywords

• Artificial neural network
• Kinetics
• Oil sand bitumen
• Pyrolysis
• Thermodynamics