人工智能在生物质热解中的作用:通过机器学习推进预测建模和机械理解 - - 综合性综述
Luchen Yang1, Brahim El Allaoui1, Xinyun Wu1
1Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo 315100, China.
Bioresource technology
|December 6, 2025
概括
人工智能 (AI) 和机器学习 (ML) 通过改进产品产量的预测建模来增强生物质热解优化. 可解释的人工智能 (XAI) 为高效和可扩展的可再生燃料生产提供了机械洞察力.
科学领域:
- 热化学能量转换热化学能量转换
- 可再生能源技术可再生能源技术
- 化学工程中的人工智能.
背景情况:
- 生物质热解是一种可再生燃料和化学品的关键热化学过程.
- 由于原料,条件和反应堆设计之间的复杂相互作用,优化受到阻碍.
- 人工智能 (AI),特别是机器学习 (ML),为预测建模和理解提供了先进的解决方案.
研究的目的:
- 审查ML在优化生物质热解中的应用.
- 检查各种 pyrolysis 模式和先进的战略.
- 讨论可解释AI (XAI) 在提供机械洞察力方面的作用.
主要方法:
- 审查监督的ML模型 (RF,XGBoost,ANNs) 用于预测产品产量.
- 对无监督和强化学习进行分析,以发现趋势和控制过程.
- 整合XAI框架 (SHAP,PDP) 用于机械解释.
- 检查不同的热解模式 (缓慢,中等,快速,闪光) 和技术 (联合热解,催化,微波).
主要成果:
- 监督的ML模型在预测热解产品产量方面表现出高准确度.
- 无监督和强化学习揭示了隐藏的模式,并使适应性控制成为可能.
- XAI方法提供了关键的机械洞察力,将数据与反应途径联系起来.
- 人工智能与反应堆优化集成有助于高效和可扩展的热解系统.
结论:
- 人工智能和机器学习是优化生物质热解的变革性工具.
- XAI对于机理学理解和将数据与反应途径联系起来至关重要.
- 未来的研究应该专注于可解释和可泛化的人工智能模型,用于大规模部署热解技术.
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