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.

中文翻译：

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机器学习和木质生物质：评估木片质量以实现可持续能源生产


本文探讨了机器学习技术在评估木片质量方面的整合，木片是可持续能源生产的关键生物质来源。生物质，特别是木屑，在从化石燃料过渡中发挥着关键作用，而化石燃料是全球碳排放的主要贡献者。评估木片质量的传统方法，例如对水分含量、灰分含量、氮含量和热值的实验室分析，由于时间限制和材料成分的变化而面临限制。机器学习通过提供实时、准确的预测来提供解决方案，从而优化燃烧效率并减少对环境的影响。本研究回顾了各种机器学习模型，如支持向量机、决策树、人工神经网络和偏最小二乘回归，这些模型对水分含量和热值等参数的预测准确性很高。然而，由于生物量的异质性，挑战仍然存在，特别是在预测氮和微量元素（如氯和硫）方面。将机器学习与遥感技术相结合被认为是增强整个木片生产链实时质量监控的有前途的途径。模型优化和数据采集的未来进展对于进一步优化生物质作为可再生能源至关重要。