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数据缺陷条件下支持污水处理厂智能管理的数据增强方法

王建辉,  廖万山,  李慧敏,  冯东,  郭智威,  Mohamed S. Mahmoud,  张冰,  高旭,  申渝,  陈猷鹏

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文章导航 > 环境工程  > 2024 >  42(6): 153-159
王建辉, 廖万山, 李慧敏, 冯东, 郭智威, Mohamed S. Mahmoud, 张冰, 高旭, 申渝, 陈猷鹏. 数据缺陷条件下支持污水处理厂智能管理的数据增强方法[J]. 环境工程, 2024, 42(6): 153-159. doi: 10.13205/j.hjgc.202406018
引用本文: 王建辉, 廖万山, 李慧敏, 冯东, 郭智威, Mohamed S. Mahmoud, 张冰, 高旭, 申渝, 陈猷鹏. 数据缺陷条件下支持污水处理厂智能管理的数据增强方法[J]. 环境工程, 2024, 42(6): 153-159. doi: 10.13205/j.hjgc.202406018
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WANG Jianhui, LIAO Wanshan, LI Huimin, FENG Dong, GUO Zhiwei, Mohamed S. Mahmoud, ZHANG Bing, GAO Xu, SHEN Yu, CHEN Youpeng. A DATA ENHANCEMENT METHOD FOR SUPPORTING INTELLIGENT MANAGEMENT OF WWTPs UNDER DATA DEFICIENCY CONDITIONS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 153-159. doi: 10.13205/j.hjgc.202406018
Citation: WANG Jianhui, LIAO Wanshan, LI Huimin, FENG Dong, GUO Zhiwei, Mohamed S. Mahmoud, ZHANG Bing, GAO Xu, SHEN Yu, CHEN Youpeng. A DATA ENHANCEMENT METHOD FOR SUPPORTING INTELLIGENT MANAGEMENT OF WWTPs UNDER DATA DEFICIENCY CONDITIONS[J]. ENVIRONMENTAL ENGINEERING , 2024, 42(6): 153-159. doi: 10.13205/j.hjgc.202406018
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数据缺陷条件下支持污水处理厂智能管理的数据增强方法

doi: 10.13205/j.hjgc.202406018

  • 1. 重庆工商大学 智能制造服务国际科技合作基地, 重庆 400067;

  • 2. 重庆南向泰斯环保技术研究院有限公司, 重庆 400069;

  • 3. 重庆中法环保研发中心有限公司, 重庆 400010;

  • 4. 埃及住房和建筑国家研究中心 卫生与环境工程研究所, 埃及开罗 1770;

  • 5. 重庆大学 环境与生态学院, 重庆 400045

基金项目: 

重庆水务环境控股集团有限公司科技创新项目(2022-15)

国家自然科学基金项目"基于ASM支持的污水处理系统智能优化调控模型及其在线进化学习机制"(52300031)

重庆工商大学科研项目(1853061)

重庆市教委科研项目(CXQT19023)

详细信息
    作者简介:

    王建辉(1989-),男,副教授,主要研究方向为水污染控制。jhwang@ctbu.edu.cn

    通讯作者:

    申渝(1981-),男,研究员,主要研究方向为工业废水处理工艺及装备智能管理、环境大数据管理。shenyu@ctbu.edu.cn

    陈猷鹏。ypchen@cqu.edu.cn

A DATA ENHANCEMENT METHOD FOR SUPPORTING INTELLIGENT MANAGEMENT OF WWTPs UNDER DATA DEFICIENCY CONDITIONS

  • 1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China;

  • 2. Chongqing South-to-Thais Environmental Protection Technology Research Institute Co., Ltd., Chongqing 400069, China;

  • 3. Chongqing Sino French Environmental Excellence R & D Centre, Chongqing 400010, China;

  • 4. Sanitary and Environmental Engineering Institute(SEI), Housing and Building National Research Center(HBRC), Cairo 1770, Egypt;

  • 5. College of Environment and Ecology, Chongqing University, Chongqing 400045, China

    摘要
  • 摘要: 污水处理厂的智能化管理需要高质量、丰富的数据支持。然而,在当前污水处理厂的运维管理中,过度曝气、过量投药及监测问题等导致水厂运维数据的数量和质量存在缺陷,基于此类缺陷数据支持的各种数据驱动模型性能不高。如何提高数据质量和数量对于各类人工智能模型的研究和应用非常关键。提出了一种基于生成对抗网络的污水厂数据增强方法(WP-GAN),以应对数据缺陷问题,并采用一种经典的污水处理厂人工神经网络模型(W-ANN)对所提出的方法进行验证。研究采用的数据集来自某大型城市污水处理厂的厌氧-缺氧-好氧(A2O)工艺,通过数据增强处理将实测数据扩增5倍,以增强前后的数据样本训练W-ANN模型后,模型性能得到显著提升:拟合度从20%提高到65%,最大模拟精度从67.85%提高到75.55%。该方法是一种应对数据缺陷的通用数据增强方法,可为污水厂智能管理的各种数据驱动模型提供更好的数据支持。
    Abstract: The intelligent management of wastewater treatment plants (WWTPs) requires high-quality and rich data support. However, in the current management mode of WWTPs, issues such as excessive aeration, excessive dosing, and monitoring have led to deficiencies in the quantity and quality of operation and maintenance data. The performance of various data-driven models supported by such defective data is not sufficient. How to improve the quality and quantity of data is crucial for the research and application of various artificial intelligence models. This study proposed a wastewater treatment data enhancement method based on generative adversarial networks (WP-GAN) to address the data defects, and validated the proposed method using a classic wastewater treatment artificial neural network model (W-ANN). The dataset used in the study was from a large urban WWTP with an anaerobic-anoxic-aerobic (A2O) process. The measured data was amplified 5-fold through data enhancement treatment, and the W-ANN model was trained with pre- and post-enhancement data samples. It was found that the performance of the W-ANN model was significantly improved: the fitting degree increased from 20% to 65%, and the maximum simulation accuracy increased from 67.85% to 75.55%. The method proposed in this study is a universal data enhancement method to address data deficiencies, which can provide better data support for various data-driven models for the intelligent management of WWTPs.
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  • 收稿日期:  2023-08-26
  • 网络出版日期:  2024-07-11

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