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通过时间窗口和变压器增强的基于图像驱动的深度学习,用于减少饮用水处理厂的碳排放

Ziqi Zhou¹, Baichun Wang¹, Zirui Huang¹

¹Hubei Key Laboratory of Multi-media Pollution Cooperative Control in Yangtze Basin, School of Environmental Science & Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, China.

|November 7, 2025

概括

优化深度学习模型在饮用水处理厂 (DWTP) 中精确剂量凝固剂,减少化学品的使用和温室气体排放. 这种方法使用短期数据和先进的策略来有效净化水.

科学领域:

环境工程环境工程
水处理技术水处理技术
环境科学中的人工智能

背景情况:

在饮用水处理厂 (DWTP) 中精确的凝固剂剂量对水安全和减少温室气体 (GHG) 排放至关重要.
机器学习 (ML) 和深度学习 (DL) 算法对此任务的有效性通常受到广泛的长期数据需求的限制.
利用短期数据探索ML和DL的潜力对于实际,快速部署至关重要.

研究的目的:

通过使用短期数据,研究传统ML和DL算法的性能,以准确地定位凝固剂剂量.
开发和评估提高DL模型性能的策略,包括流体形态特征提取,时间窗口选择和变压器架构集成.
为了证明在现实世界DWTP设置中部署和维护优化的DL模型的可行性.

主要方法:

将四个传统的ML算法与四个DL算法进行比较,使用来自DWTP的短期数据.
引入了三个关键策略:创新的群体形态特征提取,优化的时间窗口选择和变压器架构集成.
构建了16个场景和96个模型来分析这些策略对凝血剂剂量准确性的影响.

主要成果:

最初,ML模型的性能优于DL模型,因为它们的简单性而没有战略性增强.
优化的DL模型,特别是具有特定策略的时间卷积网络 (TCN),显著优于ML模型,达到0.99.9的R和R2值.
性能最好的DL模型迅速部署,在一个月的数据上进行训练,并在六个多月内表现出稳定的运行.

结论:

优化的深度学习模型,利用先进特征提取和时间分析等策略,可以有效地执行精确的凝固剂剂剂量,即使数据有限.
开发的DL模型实现了凝固剂剂量 (20%) 和相关的二氧化碳等效 (CO2-eq) 排放量 (每年70) 的大幅降低.
这项研究突出了先进的DL算法的显著潜力,以提高净水效率,并为DWTP中的环境可持续性做出贡献.

关键词:

减少碳排放减少碳排放深度学习是一种深度学习.群体的形态特征群体的形态特征时间窗口的时间窗口.变压器变压器变压器

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