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.
Bibliographical noteFunding Information:
The authors would like to acknowledge the National University of Sciences & Technology for support and Pakistan Science Foundation (grant No. PSF/CRP/C-NUST/T-Helix (47) for financial support.
© 2022 Elsevier Ltd
- Artificial intelligence
- Climate change
- Machine learning
ASJC Scopus subject areas
- Chemical Engineering (all)
- Fuel Technology
- Energy Engineering and Power Technology
- Organic Chemistry