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预测废水处理厂的废水质量参数:基于机器学习的方法

João Vitor Rios Fuck¹, Maria Alice Prado Cechinel¹, Juliana Neves¹

¹Hydroinfo - Hydroinformatics Solutions Ltda, Florianópolis, SC, Brazil; Laboratory of Materials and Scientific Computing (LabMAC), Department of Chemical and Food Engineering, Federal University of Santa Catarina (UFSC), Florianópolis, SC, Brazil.

|February 21, 2024

概括

机器学习模型准确地预测了模拟和现实世界废水处理厂 (WWTP) 的废水质量参数. 了解运营变化对于改善预测模型性能和数据质量至关重要.

科学领域:

环境工程环境工程
数据科学数据科学数据科学
生物化学工程生物化学工程

背景情况:

污水处理厂 (WWTP) 呈现出复杂,可变的生化过程,挑战了准确的预测.
预测废水质量参数对于高效的工厂运行和环保合规至关重要.

研究的目的:

开发和评估用于预测废水质量参数的机器学习 (ML) 模型.
评估运营变化和数据质量对模拟和现实世界WWTP预测模型性能的影响.

主要方法:

应用随机森林 (RF),支持矢量机 (SVM) 和多层感知器 (MLP) 模型与来自模拟 (WEST WWTP) 和现实世界 (AMBEV WWTP) 设施的数据集.
在连续数据场景中评估模型性能,并使用部分依赖图 (PDP) 和转换重要性 (PI) 分析特征重要性.

主要成果:

在WEST WWTP中,MLP实现了总 (TN) 预测的R2为0.72.
尽管有观察到的差异,但RF表现出更好的适应现实世界的AMBEV WWTP数据.
影响的参数与模拟场景中的预测结果有很强的相关性.

结论:

高质量的数据收集和运营信息对于WWTP中强大的预测建模至关重要.
机器学习模型为废水质量预测提供了一个有希望的方法,提高了运营效率和环境管理.
进一步的研究应侧重于改进模型,以考虑运营动态和数据变化.

关键词:

功能重要性功能重要性可以解释性解释性机器学习技术机器学习技术预测模型是一个预测模型.在WWTP WWTP WWTP中使用.

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Typical Model Studies

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