一个小型审查,以确定未来的方向,以建模可持续废物管理的取暖值
Dan Wang1, Yu-Ting Tang2, Jun He3
1Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, School of Life Sciences, Taizhou University, Zhejiang, China.
概括
城市固体废物 (MSW) 中未充分利用的能源阻碍了循环经济目标. 结合社会和环境因素的先进人工智能 (AI) 模型可以改善加热值 (HV) 预测,以更好地进行废物转化能源管理.
科学领域:
- 废物管理 废物管理
- 恢复能源 恢复能源
- 人工智能的人工智能
背景情况:
- 全球城市固体废物 (MSW) 的能量含量仍然未得到充分利用,影响了减少二氧化碳排放和循环经济倡议.
- 垃圾废弃物的加热值 (HVs) 对废物转化为能源 (WTE) 的可行性和废物流管理至关重要.
- 传统的HV预测统计模型存在局限性,包括线性假设和忽视非可燃材料的假设.
研究的目的:
- 探索先进的方法来预测城市固体废物的加热值 (HVs).
- 突出目前HV预测模型的局限性,并提出改进方案.
- 展示人工智能 (AI) 和社会环境因素在提高废物管理的HV预测方面的价值.
主要方法:
- 对MSW加热值的传统统计建模方法的审查.
- 探索基于人工智能 (AI) 的模型用于HV预测.
- 在预测建模中考虑物理组成,环境和社会经济因素.
主要成果:
- 传统模型往往无法捕捉MSW加热值的复杂性,这是由于非线性关系和非可燃组件的影响.
- 人工智能模型显示了改善HV预测的潜力,但需要在燃烧理论中仔细解释.
- 纳入类别内变化和社会/环境因素提供了一个更细致的方法来预测MSW能量含量.
结论:
- 利用人工智能技术是推动MSW加热价值预测的一个有希望的方向.
- 将社会和环境因素纳入模型可以解释不断变化的废物成分.
- 这些先进的方法可以导致对废物转化为能源的过程进行更准确的评估,并促进可持续的废物管理.
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