This in-depth examination explores the pioneering potential revealed by the convergence of Machine Learning (ML) and Artificial Intelligence (AI) in the biomass energy sector. Biomass energy appears as a robust answer amid rising climate change worries and the urgent demand for cleaner energy sources. Biomass feedstock is derived from organic materials like agricultural waste and forestry wood. The process of deriving energy from biomass sources through processes like gasification, pyrolysis, and hydrothermal liquefication is complex and nonlinear. The conventional approaches to modeling these processes are time-consuming and laborious. In this context, data-derived machine learning (ML) and artificial intelligence (AI) technologies become drivers of innovation in the domain of biomass energy. From nearly perfect energy yield projections to improving complex conversion techniques, these technologies are making an impact in almost all fields of biomass energy. The present article discusses these aspects by providing a detailed discussion of emerging methods like explainable artificial intelligence to improve the perception of stakeholders. However, data security and AI ethics remain a challenge.

中文翻译：

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利用生物质能：通过机器学习和 AI 应用程序实现可持续性和效率的进步


这项深入的考察探讨了机器学习 （ML） 和人工智能 （AI） 在生物质能领域的融合所揭示的开创性潜力。在对气候变化的担忧日益加剧和对清洁能源的迫切需求中，生物质能源似乎是一个强有力的解决方案。生物质原料来自农业废弃物和林业木材等有机材料。通过气化、热解和热液化等过程从生物质来源获取能源的过程是复杂且非线性的。对这些过程进行建模的传统方法既费时又费力。在此背景下，数据衍生的机器学习 （ML） 和人工智能 （AI） 技术成为生物质能领域的创新驱动力。从近乎完美的能源产量预测到改进复杂的转换技术，这些技术正在对生物质能源的几乎所有领域产生影响。本文通过详细讨论新兴方法（如可解释的人工智能）来讨论这些方面，以改善利益相关者的看法。然而，数据安全和 AI 伦理仍然是一个挑战。