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Fast Pyrolysis of Biomass Residues in a Twin-screw Mixing Reactor
Published on: September 9, 2016
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使用LIBS和ML技术,快速表征MSW和RDF原料,用于废物转化为能源的过程
Jincheng Liu1, Oluwabunmi Iwakin2, Carlos E Romero1
1Energy Research Center, Lehigh University, Bethlehem 18015 PA, USA; Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem 18015 PA, USA.
Waste management (New York, N.Y.)
|August 16, 2025
概括
这项研究引入了一种快速方法,将激光诱导分解光谱 (LIBS) 和机器学习 (ML) 结合起来,以分析废物衍生燃料 (RDF). 这种方法准确地预测了关键的燃料参数,提高了生物燃料生产效率.
科学领域:
- 废弃物管理和回收利用
- 分析化学 分析化学
- 计算科学 计算科学
背景情况:
- 城市固体废物 (MSW) 的异质性使生物燃料和生物产品的生产复杂化.
- 对于MSW衍生的废物衍生燃料 (RDF) 的准确表征对于高效的加工至关重要.
- 传统的废物分析方法是劳动密集型和耗时的.
研究的目的:
- 为RDF开发一种快速而准确的表征技术.
- 提高生物燃料生产废物分析的效率.
- 将激光诱导分解光谱 (LIBS) 与机器学习 (ML) 集成,用于预测RDF属性.
主要方法:
- 使用LIBS从RDF样本获得光谱数据.
- 应用数据预处理技术到LIBS频谱.
- 使用域和基于理论的光谱特征开发和训练ML模型.
- 在预测关键过程参数时验证模型性能.
主要成果:
- 在预测高加热值 (HHV),碳含量和挥发性物质方面取得了很高的准确性.
- 显示了2.13%的平均相对平方根平均误差 (RRMSE).
- 在测试数据上,所有预测参数的确定系数 (R2) 为0.98或更高.
结论:
- 结合LIBS-ML方法为传统的RDF表征提供了一个快速而准确的替代方案.
- 这种方法显著提高了废物分类和加工效率.
- 该方法支持在废物管理中加强环境遵守.
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