Machine learning applications in biomass pyrolysis: From biorefinery to end-of-life product management

Abstract

The thermochemical conversion of biomass is a promising technology due to its cost-effectiveness and feedstock flexibility, with pyrolysis being a particularly noteworthy method for its diverse product range. Despite the potential of pyrolysis, commercialization remains elusive, and there is a growing need to fully understand its dynamics to facilitate process scaling up. However, waste biomass pyrolysis is complex, time-consuming, and capital-intensive. Machine Learning (ML) has emerged as a possible means of supporting and accelerating pyrolysis research despite these challenges. This study provides a comprehensive overview of the use of ML in pyrolysis, from biorefinery to end-of-life product management. In addition, the success of ML in process optimization and control, predicting product yield, real-time monitoring, life-cycle assessment (LCA), and techno-economic analysis (TEA) during biomass pyrolysis is highlighted. Several ML methods have been utilized in a bid to study pyrolysis; the potentiality of artificial neural networks (ANNs) to learn extremely non-linear input-output correlations has led to the widespread adoption of these networks. Furthermore, the current knowledge gaps in ML research in pyrolysis and future recommendations for its application are identified. Finally, this study demonstrates the potential of ML in accelerating research and development as well as the scalability of pyrolysis of biomass.