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通过高光谱成像和机器学习对聚烯中胺阻燃剂的定量鉴定

Frederik Sprotte Reese¹, Georgiana Amariei¹, Martin Lahn Henriksen¹

¹Plastic and Polymer Engineering, Department of Biological and Chemical Engineering, Aarhus University, Aabogade 40, DK-8200 Aarhus N., Denmark.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|February 14, 2025

概括

这项研究引入了一种使用SWIR高光谱成像和机器学习的新方法,以准确检测塑料废物中的胺 (MEL). 这项技术对于含有这种阻燃剂的塑料的安全回收和处理至关重要.

科学领域:

材料科学材料科学材料科学
分析化学分析化学
聚合物科学聚合物科学

背景情况:

胺 (MEL) 是一种非化阻燃剂,用于替代有毒化化合物.
欧洲化学品署 (ECHA) 已将MEL确定为非常令人担忧的物质,需要在塑料废物中识别它.
准确检测MEL对于安全处理和有效回收LDPE和PP等塑料材料至关重要.

研究的目的:

开发和验证低密度聚乙烯 (LDPE) 和聚烯 (PP) 中MEL的工业内线定量识别技术.
使用短波红外 (SWIR) 超光谱成像与机器学习相结合,用于实时MEL检测.
建立在塑料回收过程中低于危险值的MEL度监测方法.

主要方法:

用不同的MEL负荷组合LDPE和PP样本.
使用元素分析,ATR-FTIR,TGA和DSC进行表征.
使用SWIR高光谱成像和机器学习开发预测模型 (回归SWIR带面积比和主要组成部分一).

主要成果:

基于SWIR带面积比率和主要组件1的预测模型显示,测量和预测MEL度之间存在强烈的相关性.
SWIR带面积比率模型准确量化了LDPE (0.829.8重%,R2=0.975) 和PP (1.426.3重%,R2=0.995) 中的MEL.
主要组件一个模型在LDPE (0.829.8重%,R2=0.978) 和PP (0.926.3重%,R2=0.988) 中表现出可比的精度.
在线超光谱模型与FTIR频段比率分析相似地执行.

结论:

开发的SWIR高光谱成像和机器学习方法提供了可靠的,实时量化LDPE和PP中的MEL.
这些技术可以检测MEL低于危险值,支持安全的塑料废物管理和回收利用.
该研究促进了塑料回收行业的工业应用,以提高塑料回收行业的安全性和可持续性.

关键词:

超光谱成像技术的使用.在线定量识别在线定量识别机器学习是机器学习.氨酸是什么意思氨酸是什么意思非酸阻燃剂是非酸阻燃剂.聚烯是一种多烯.

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