CSCD来源期刊
中国科技核心期刊
RCCSE中国核心学术期刊
JST China 收录期刊

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

downloadPDF
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
shu
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
shu

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.
    • HTML全文
    • 参考文献(33)
  • [1] ZHANG J, SHAO Y, WANG H, et al. Current operation state of wastewater treatment plants in urban China[J]. Environ Res, 2021, 195: 110843.
    [2] 陈治池,何强,蔡然,等.碳中和趋势下数学模拟在污水处理系统中的发展与综合应用[J].中国环境科学, 2022, 42(6): 2587-2602.
    [3] ZHANG Y, THORBURN P J. Handling missing data in near real-time environmental monitoring: a system and a review of selected methods[J]. Future Gener Comp Sy, 2022, 128: 63-72.
    [4] XU R Z, CAO J S, WU Y, et al. An integrated approach based on virtual data augmentation and deep neural networks modeling for VFA production prediction in anaerobic fermentation process[J]. Water Research, 2020, 184, 116103.
    [5] 高嵩,邱勇,孟凡琳,等.污水处理工艺数据分析技术的现状与趋势[J].环境工程, 2022, 40(6): 194-203.
    [6] XU R Z, CAO J S, FANG F, et al. Integrated data-driven strategy to optimize the processes configuration for full-scale wastewater treatment plant predesign[J]. Science of the Total Environment, 2021, 785, 147356.
    [7] WANG K J, WANG P S, NGUYEN H P. A data-driven optimization model for coagulant dosage decision in industrial wastewater treatment[J]. Computers Chemical Engineering, 2021, 152: 107383.
    [8] EL-RAWY M, ABD-ELLAH M K, FATHI H, et al. Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques[J]. Journal of Water Process Engineering, 2021, 44, 102380.
    [9] YANG Y, KIM K R, KOU R, et al. Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling[J]. Process Safety and Environmental Protection, 2022, 158: 515-524.
    [10] KANG H, YANG S, HUANG J, et al. Time series prediction of wastewater flow rate by bidirectional LSTM deep learning[J]. Int J Control Autom, 2020, 18(12): 3023-3030.
    [11] MANSOUR-BAHMANI A, HAGHIABI A H, SHAMSI Z, et al. Predictive modeling the discharge of urban wastewater using artificial intelligent models (case study: Kerman city)[J]. Modeling Earth Systems and Environment, 2021, 7(3): 1917-1925.
    [12] YAN W W, XU R C, WANG K D, et al. Soft sensor modeling method based on semisupervised deep learning and its application to wastewater treatment plant[J]. Industrial Engineering Chemistry Research, 2020, 59(10): 4589-4601.
    [13] HARROU F, CHENG T, SUN Y, et al. A data-driven soft sensor to forecast energy consumption in wastewater treatment plants: a case study[J]. IEEE Sensors Journal, 2021, 21(4): 4908-4917.
    [14] WANG J H, ZHAO X L, GUO Z W, et al. A full-view management method based on artificial neural networks for energy and material-savings in wastewater treatment plants[J]. Environmental Research, 2022, 211: 113054.
    [15] YANG S S, YU X L, DING M Q, et al. Simulating a combined lysis-cryptic and biological nitrogen removal system treating domestic wastewater at low C/N ratios using artificial neural network[J]. Water Research, 2021, 189: 116576.
    [16] NEWHART K B, HOLLOWAY R W, HERING A S, et al. Data-driven performance analyses of wastewater treatment plants: a review[J]. Water Research, 2019, 157: 498-513.
    [17] ZHANG J B, WANG H C, SHAO Y T, et al. Analysis on common problems of the wastewater treatment industry in urban China[J]. Chemosphere, 2022, 291: 132875.
    [18] CHEN Z, XU H, JIANG P, et al. A transfer learning-based LSTM strategy for imputing large-scale consecutive missing data and its application in a water quality prediction system[J]. J Hydrol, 2021, 602: 126573.
    [19] NGOUNA R H, RATOLOJANAHARY R, MEDJAHER K, et al. A data-driven method for detecting and diagnosing causes of water quality contamination in a dataset with a high rate of missing values[J]. Engineering Applications of Artificial Inteligence, 2020, 95: 103822.
    [20] BERTANZA G, BOIOCCHI R, PEDRAZZANI R. Improving the quality of wastewater treatment plant monitoring by adopting proper sampling strategies and data processing criteria[J]. Science of the Total Environment, 2022, 806: 150724.
    [21] FAN Y, GUO Z, WANG J, et al. Online learning-empowered smart management for A2O process in sewage treatment processes[J]. Environmental Research, 2022, 210: 113015.
    [22] BA-ALAWI A H, LOY-BENITEZ J, KIM S, et al. Missing data imputation and sensor self-validation towards a sustainable operation of wastewater treatment plants via deep variational residual autoencoders[J]. Chemosphere, 2022, 288: 132647.
    [23] KIM J, SEO D, JANG M, et al. Augmentation of limited input data using an artificial neural network method to improve the accuracy of water quality modeling in a large lake[J]. J Hydrol, 2021, 602: 126817.
    [24] ZHAN C J, DAI Z X, SAMPER J, et al. An integrated inversion framework for heterogeneous aquifer structure identification with single-sample generative adversarial network[J]. J Hydrol, 2022, 610: 127844.
    [25] ZHANG K, CHEN Q, CHEN J, et al. A multi-module generative adversarial network augmented with adaptive decoupling strategy for intelligent fault diagnosis of machines with small sample[J]. Knowl-Based Syst, 2022, 239: 107980.
    [26] PEI L L, SUN Z Y, XIAO L Y, et al. Virtual generation of pavement crack images based on improved deep convolutional generative adversarial network[J]. Engineering Applications of Artificial Inteligence, 2021, 104: 104376.
    [27] ZHOU R, JIANG C, XU Q Y. A survey on generative adversarial network-based text-to-image synthesis[J]. Neurocomputing, 2021, 451: 316-336.
    [28] WANG W J, WANG H Y, YANG S K, et al. Resolution enhancement in microscopic imaging based on generative adversarial network with unpaired data[J]. Optics Communications, 2022, 503: 127454.
    [29] LV J, DU L, LIN H, et al. Enhancing effluent quality prediction in wastewater treatment plants through the integration of factor analysis and machine learning[J]. Bioresource Technology, 2024,393:130008.
    [30] KOFINAS D T, SPYROPOULOU A, LASPIDOU C S. A methodology for synthetic household water consumption data generation[J]. Environmental Modelling & Software, 2018, 100: 48-66.
    [31] ZHOU S, SONG W. Deep learning-based roadway crack classification using laser-scanned range images: a comparative study on hyperparameter selection[J]. Automation in Construction, 2020,114:103171.
    [32] 马润,蒋全. 改进数据增强方法在轴承故障诊断中的应用[J]. 软件导刊, 2022, 21 (6): 135-140.
    [33] ADDISSON S, LUIS V, GONZALO S. Generative adversarial networks and markov random fields for oversampling very small training sets[J]. Expert Systems with Applications, 2021, 163: 113819.
    • 相关文章
    • 施引文献
  • 资源附件(0)
  • 加载中
WeChat 点击查看大图
计量
  • 文章访问数:  30
  • HTML全文浏览量:  3
  • PDF下载量:  5
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-08-26
  • 网络出版日期:  2024-07-11

目录

    /

    返回文章
    返回

    期刊在线

    作者中心

    友情链接

    版权所有 ©《工业建筑》杂志社有限公司 京ICP备11033253号-1

    本系统由北京仁和汇智信息技术有限公司 设计开发   百度统计