Machine learning and woody biomasses: Assessing wood chip quality for sustainable energy production

This paper explores the integration of Machine Learning techniques in assessing the quality of wood chips, a key biomass source for sustainable energy production. Biomass, specifically wood chips, plays a critical role in transitioning from fossil fuels, which are the primary contributors to global carbon emissions. Traditional methods of evaluating wood chip quality, such as laboratory analysis for moisture content, ash content, nitrogen levels, and heating value, face limitations due to time constraints and variability in material composition. Machine Learning offers a solution by providing real-time, accurate predictions that can optimize combustion efficiency and reduce environmental impact. This study reviews various Machine Learning models like support vector machines, decision trees, artificial neural networks, and partial least squares regression, which have demonstrated high predictive accuracy for parameters like moisture content and heating value. However, challenges remain, particularly in predicting nitrogen and trace elements like chlorine and sulfur, due to biomass heterogeneity. The integration of Machine Learning with remote sensing technologies is proposed as a promising avenue for enhancing real-time quality monitoring throughout the wood chip production chain. Future advancements in model refinement and data acquisition are essential for further optimizing biomass as a renewable energy source. • The paper focuses on the integration of machine learning (ML) techniques for assessing the quality of wood chips, a key biomass source for sustainable energy production. • ML offers a promising alternative to wood chip quality assessment by providing accurate predictions to optimize combustion efficiency. • Support vector machines (SVM), artificial neural networks (ANN), and partial least squares regression (PLS), have shown high predictive accuracy for parameters like moisture content and heating value. • Challenges persist in predicting nitrogen and trace elements due to the heterogeneous nature of biomass, suggesting the need for further refinement of these models.