Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review

Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Pre-vious ML applications were classified into three categories based on their objectives - material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identi-fying critical factors for optimizing BDM systems, predicting material features and performances, reverse engi-neering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and similar to 75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.