Machine learning to predict the production of bio-oil, biogas, and biochar by pyrolysis of biomass: a review

Abstract

The world energy consumption has increased by + 195% since 1970 with more than 80% of the energy mix originating from fossil fuels, thus leading to pollution and global warming. Alternatively, pyrolysis of modern biomass is considered carbon neutral and produces value-added biogas, bio-oils, and biochar, yet actual pyrolysis processes are not fully optimized. Here, we review the use of machine learning to improve the pyrolysis of lignocellulosic biomass, with emphasis on machine learning algorithms and prediction of product characteristics. Algorithms comprise regression analysis, artificial neural networks, decision trees, and the support vector machine. Machine learning allows for the prediction of yield, quality, surface area, reaction kinetics, techno-economics, and lifecycle assessment of biogas, bio-oil, and biochar. The robustness of machine learning techniques and engineering applications are discussed.