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机器学习模型的适用性,用于评估固体废物材料中长期污染物漏的情况

Amirhossein Ershadi¹, Michael Finkel¹, Bernd Susset¹

¹Center for Applied Geoscience, University of Tübingen, Schnarrenbergstraße 94-96, 72076 Tübingen, Germany.

Waste management (New York, N.Y.)

|September 12, 2023

概括

机器学习模型显著减少了评估建筑垃圾漏的测试时间. 这些模型准确地预测化学度,使材料的重复利用速度更快.

科学领域:

环境科学环境科学
地质化学地质化学
材料科学材料科学材料科学

背景情况:

柱子漏试验是评估受污染的土壤和废物再利用的标准.
目前的方法需要大约7天的时间来准确评估长期的漏行为.
减少测试时间对于建筑中高效的材料再利用至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测水度.
为了减少柱子漏试验所需的时间.
为了能够准确地预测各种液体-固体 (LS) 比率的化学泄漏情况.

主要方法:

使用参数和非参数ML技术开发线性和组合模型.
在来自德国建筑和拆除废弃物 (CDW) 的82列漏试验数据集上训练ML模型.
使用Shapley添加式解释 (SHAP) 进行模型特征的灵敏度分析.

主要成果:

获得的高R平方值:硫酸盐 (0.94), (0.97), (0.82),铜 (0.92),以及15种美国环保署PAH (0.98).
模型根据较低的LS值准确地预测了LS2和LS4的漂水度.
电导率和pH值被确定为关键预测特征,以及初始化合物度.

结论:

ML模型为传统的柱子浸试验提供了更快的替代方案.
精确预测漏水度有助于安全地重复使用建筑和拆除废物.
开发的模型为环境风险评估和废物管理提供了有价值的工具.

关键词:

柱子浸试验柱子浸试验建筑和拆除废弃物的废弃物.液体与固体的比率是什么机器学习机器学习这些PAHs是PAH.

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