Applications of machine learning in thermochemical conversion of biomass-A review. (15th January 2023) - khan, Muzammil; Raza Naqvi, Salman; Ullah, Zahid; Ali Ammar Taqvi, Syed; Nouman Aslam Khan, Muhammad; Farooq, Wasif; Taqi Mehran, Muhammad; Juchelková, Dagmar; Štěpanec, Libor
Graphical abstract: Highlights: Machine learning models can accurately model thermal conversion methods. Classification, regression, and optimization are involved in thermal conversion. Artificial neural networks have been the most commonly employed algorithm. Optimization methods were used for ANN network selection and hyper-parameters. Hybrid and novel ML algorithms (deep learning) with large databases are expected. Abstract: 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 thermochemical 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 optimization, 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 outperformed 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 recommendations are presented.