Applications of machine learning in thermochemical conversion of biomass-A review

Thermochemical conversion of biomass has been considered a promising technique to produce alternative renewable fuel sources for future energy supply. However, these processes are often complex, labor-intensive, and time-consuming. Significant efforts have been made in developing strategies for modeling thermochem-ical conversion processes to maximize their performance and productivity. Among these strategies, machine learning (ML) has attracted substantial interest in recent years in thermochemical conversion process optimi-zation, yield prediction, real-time monitoring, and process control. This study presents a comprehensive review of the research and development in state-of-the-art ML applications in pyrolysis, torrefaction, hydrothermal treatment, gasification, and combustion. Artificial neural networks have been widely employed due to their ability to learn extremely non-linear input-output correlations. Furthermore, the hybrid ML models out-performed the traditional ML models in modeling and optimization tasks. The comparison between various ML methods for different applications, and insights about where the current research is heading, is highlighted. Finally, based on the critical analysis, existing research knowledge gaps are identified, and future recommen-dations are presented.

Automated summary

Thermochemical conversion of biomass has been recognized as a promising technique for producing renewable fuels. Machine learning has gained significant interest in optimizing and controlling these processes. This study provides a comprehensive review of state-of-the-art machine learning applications in various thermochemical conversion processes and highlights the advantages of hybrid models over traditional models.