|本期目录/Table of Contents|

[1]陈亚松,邱 勇*,柳蒙蒙,等.污水处理软测量仪表研究进展与应用[J].工业仪表与自动化装置,2024,(03):60-67+88.[doi:DOI:10.19950/j.cnki.CN61-1121/TH.2024.03.011]
 CHEN Yasong,QIU Yong*,LIU Mengmeng,et al.Research progress and application of soft measurement instruments for wastewater treatment[J].Industrial Instrumentation & Automation,2024,(03):60-67+88.[doi:DOI:10.19950/j.cnki.CN61-1121/TH.2024.03.011]
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污水处理软测量仪表研究进展与应用(PDF)

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2024年03期
页码:
60-67+88
栏目:
出版日期:
2024-06-15

文章信息/Info

Title:
Research progress and application of soft measurement instruments for wastewater treatment
文章编号:
1000-0682(2024)03-0060-67
作者:
陈亚松1邱 勇23*柳蒙蒙1刘萌萌3刘雪洁3田宇心2黄 霞2
(1.中国长江三峡集团有限公司长江生态环境工程研究中心,北京 100083; 2. 清华大学环境学院,北京 100084;3. 清华苏州环境创新研究院,江苏 苏州 215000)
Author(s):
CHEN Yasong1 QIU Yong23* LIU Mengmeng1 LIU Mengmeng3 LIU Xuejie3 TIAN Yuxin2 HUANG Xia2
(1. Yangtze River Ecological Environment Engineering Research Center of China Three Gorges Group Co., Ltd., Beijing 100083, China; 2. School of Environment, Tsinghua University, Beijing 100084, China; 3. Research Institute for Environmental Innovation (Suzhou) Tsinghua, Suzhou 215000, Jiangsu, China)
关键词:
软测量仪表污水处理模型性能评价
Keywords:
soft sensing instrument wastewater treatment model performance evaluation
分类号:
X703
DOI:
DOI:10.19950/j.cnki.CN61-1121/TH.2024.03.011
文献标志码:
A
摘要:
软传感器以其检测快速、成本低廉等优点应用于污水处理行业前景极佳,随着人工智能的进步,软测量仪表的精度与可靠度也在逐步提高。该文在总结污水处理软测量研究进展的基础上,研究了软测量仪表开发的关键技术,并分析了某膜曝气生物反应器(MABR)农村污水处理设施出水端的应用案例。
Abstract:
Soft sensors have excellent prospects for application in the wastewater treatment due to their advantages of fast detection and low cost. With the progress of artificial intelligence, the accuracy and reliability of soft measurement instruments are also gradually improving. On the basis of summarizing the research progress of soft measurement in wastewater treatment, this article studies the key technologies of soft measurement instrument development and analyzes the application case of a MABR agricultural wastewater treatment facility’s outlet.

参考文献/References:

[1] 张道广.基于乡村振兴背景下农村污水治理应用现状及发展研究[J].清洗世界,2023,39(09):120-122.

[2] Ching P M L, SO R H Y, MORCK T. Advances in soft sensors for wastewater treatment plants: A systematic review[J]. Journal of Water Process Engineering,2021,44: 102367.
[3] Zhu S, HAN H, GUO M, et al. A data-derived soft-sensor method for monitoring effluent total phosphorus[J]. Chinese Journal of Chemical Engineering,2017,25(12): 1791-1797.
[4] Sapmaz A, Kurban S, Dundar A G, et al. Deep Learning Based Soft Sensor to Predict Total Suspended Solids of Refinery Water Treatment Plant Using Real Process Data[J]. IFAC-PapersOnLine,2022,55(33): 60-65.
[5] Kovacs D J, Li Z, Baetz B W, et al. Membrane fouling prediction and uncertainty analysis using machine learning: A wastewater treatment plant case study[J]. Journal of Membrane Science,2022,660:120817.
[6] Ba-Alawi A H, Al-masni M A, Yoo C. Simultaneous sensor fault diagnosis and reconstruction for intelligent monitoring in wastewater treatment plants: An explainable deep multi-task learning model[J]. Journal of Water Process Engineering,2023,55:104119.
[7] Zhu J, Jiang Z, Feng L. Improved neural network with least square support vector machine for wastewater treatment process[J]. Chemosphere,2022,308:136116.
[8] Wu X, Zheng Z, Wang L, et al. Coupling process-based modeling with machine learning for long-term simulation of wastewater treatment plant operations[J]. Journal of Environmental Management, 2023,341:118116.
[9] Zhang Y, Li C, Duan H, et al. Deep learning based data-driven model for detecting time-delay water quality indicators of wastewater treatment plant influent[J]. Chemical Engineering Journal,2023,467:143483.
[10] Farhi N, Kohen E, Mamane H, et al. Prediction of wastewater treatment quality using LSTM neural network[J]. Environmental Technology & Innovation, 2021,23:101632.
[11] Zaghloul M S, Achari G. Application of machine learning techniques to model a full-scale wastewater treatment plant with biological nutrient removal[J]. Journal of Environmental Chemical Engineering,2022, 10(3):107430.
[12] Jia R, Song Y, Piao D, et al. Exploration of deep learning models for real-time monitoring of state and performance of anaerobic digestion with online sensors[J]. Bioresource Technology,2022,363:127908.
[13] Jang J, Abbas A, Kim M, et al. Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models[J]. Water Research,2021, 196:117001.
[14] 姚亭吉,刘娴,张爱茜.基于机器学习的环境污染物EGFR结合活性的预测研究:中国毒理学会第十次全国毒理学大会[C],中国广东珠海,2023.
[15] Li G, Ji J, Ni J, et al. Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane bioreactors[J]. Science of The Total Environment,2022,813:151920.
[16] 张成彬,邵星,徐燕萍,等.基于物联网与深度学习的污水处理智能监控系统设计[J].软件导刊,2017,16(10):89-91.
[17] Peng C, Zeyu L, Gongming W, et al. An effective deep recurrent network with high-order statistic information for fault monitoring in wastewater treatment process[J]. Expert Systems with Applications,2021,167:114141.
[18] 齐晗兵,李怀治,王秋实,等.光谱预处理-卷积神经网络模型预测油田污水含油量研究[J].当代化工,2023,52(02): 407-410.
[19] Muniappan A, Tirth V, Almujibah H, et al. Deep convolutional neural network with sine cosine algorithm based wastewater treatment systems[J]. Environmental Research,2023,219:114910.
[20] Chen K, Wang H, Valverde-Pérez B, et al. Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning[J]. Chemosphere, 2021,279:130498.
[21] Xu R, Cao J, 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.
[22] 鲁树武,伍小龙,郑江,等.基于动态融合LOF的城市污水处理过程数据清洗方法[J].控制与决策,2022,37(05): 1231-1240.
[23] 姜奕圻.基于多源数据融合的城市排水管网水质预测模型研究[D].哈尔滨.哈尔滨工业大学,2022.
[24] 郭利进,李博仑.基于GNFA-SVR污水出水总氮软测量研究[J].工业水处理,2022,42(10):111-117.
[25] 钱维坤,毕新忠,张蕊,等.数据驱动的油田污水处理水质预测研究[J].工业安全与环保,2023,49(04):77-81.
[26] 陈园,李明河,朱琳.基于BAS优化SVM的出水COD软测量建模[J].广东化工,2020,47(21):107-109.
[27] Wang G, Jia Q, Zhou M, et al. Soft-sensing of Wastewater Treatment Process via Deep Belief Network with Event-triggered Learning[J]. Neurocomputing,2021, 436:103-113.
[28] 王旭,钟炜.一种污水处理厂设备预测及辅助维护智能平台[J].中国给水排水,2023,39(10):121-125.
[29] 赵超,戴坤成,王贵评,等.基于AWLS-SVM的污水处理过程软测量建模[J].仪器仪表学报,2015,36(08):1792-1800.
[30] 黄学平,吴留兴,辛攀,等.基于3种机器学习模型的污水处理厂出水总氮预测分析[J].能源研究与管理, 2023(02): 100-105.
[31] 赵帅,李妍君,熊伟丽.基于KPCA-Bagging的高斯过程回归建模方法及应用[J].控制工程,2019,26(01): 131-136.
[32] Wang K, Zhang H, Shen Y, et al. Impact of salinity on anaerobic ceramic membrane bioreactor for textile wastewater treatment: Process performance, membrane fouling and machine learning models[J]. Journal of Environmental Management,2023,345:118717.
[33] Nguyen X C, Ly Q V, Peng W, et al. Vertical flow constructed wetlands using expanded clay and biochar for wastewater remediation: A comparative study and prediction of effluents using machine learning[J]. Journal of Hazardous Materials,2021,413:125426.
[34] Wang D, Thunéll S, Lindberg U, et al. A machine learning framework to improve effluent quality control in wastewater treatment plants[J].Science of The Total Environment,2021,784:147138.
[35] Qambar A S, Al Khalidy M M. Optimizing dissolved oxygen requirement and energy consumption in wastewater treatment plant aeration tanks using machine learning[J]. Journal of Water Process Engineering, 2022,50: 103237.
[36] Yang B, Xiao Z, Meng Q, et al. Deep learning-based prediction of effluent quality of a constructed wetland[J]. Environmental Science and Ecotechnology,2023,13: 100207.
[37] Jeong K, Abbas A, Shin J, et al. Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning models[J]. Water Research,2021,205: 117697.
[38] 周长亮,苗盛,王明丽,等.基于深度学习和物联网技术的智慧污水管控系统[J].安全与环境工程,2021,28(01): 191-196.
[39] Chang P, Li Z. Over-complete deep recurrent neutral network based on wastewater treatment process soft sensor application[J]. Applied Soft Computing,2021,105: 107227.
[40] 薛同来,赵冬晖,韩菲,等.SVR在城市污水BOD预测中的应用[J].新型工业化,2019,9(04):94-98.
[41] 周铭睿,曲江北,李彭,等.分散式农村污水基于三维荧光光谱和紫外-可见全波段吸收光谱的“聚类-回归”COD预测模型[J].光谱学与光谱分析,2022,42(07):2113-2119.
[42] Niu C, Tan K, Jia X, et al. Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery[J]. Environmental Pollution,2021,286:117534.
[43] Wang X, Kvaal K, Ratnaweera H. Explicit and interpretable nonlinear soft sensor models for influent surveillance at a full-scale wastewater treatment plant[J]. Journal of Process Control,2019,77:1-6.
[44] Wijaya J, Oh S. Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant[J]. Environmental Research,2023,222:115366.
[45] Asadi M, McPhedran K N. Greenhouse gas emission estimation from municipal wastewater using a hybrid approach of generative adversarial network and data-driven modelling[J]. Science of The Total Environment,2021,800:149508.
[46] 韩红桂,张璐,乔俊飞.一种基于自适应回归核函数的污水处理能耗模型[J].化工学报,2016,67(03):947-953.
[47] Szel?g B, Zaborowska E, M?kinia J. An algorithm for selecting a machine learning method for predicting nitrous oxide emissions in municipal wastewater treatment plants[J]. Journal of Water Process Engineering,2023,54:103939.
[48] 李畅,潘丰.基于XGBoost的出水氨氮和总磷浓度的软测量研究[J].计算机与数字工程,2021,49(05):1003-1008.
[49] Bellamoli F, Di Iorio M, Vian M, et al. Machine learning methods for anomaly classification in wastewater treatment plants[J]. Journal of Environmental Management,2023,344:118594.
[50] Liu H, Zhang Y, Zhang H. Prediction of effluent quality in papermaking wastewater treatment processes using dynamic kernel-based extreme learning machine[J]. Process Biochemistry,2020,97:72-79.
[51] Zhao F, Liu M, Wang K, et al. A soft measurement approach of wastewater treatment process by lion swarm optimizer-based extreme learning machine[J]. Measurement, 2021,179:109322.
[52] Safder U, Loy-Benitez J, Nguyen H, et al. A hybrid extreme learning machine and deep belief network framework for sludge bulking monitoring in a dynamic wastewater treatment process[J]. Journal of Water Process Engineering, 2022,46:102580.
[53] 于丽昕,张家昌,韩红桂,等.城市污水处理水质多尺度自适应特征提取[J].控制工程,2023,30(03):552-559.
[54] Wang R, Yu Y, Chen Y, et al. Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration[J]. Journal of Environmental Management,2022,302:114020.
[55] Shen Y, Li H, Zhang B, et al. An artificial neural network-based data filling approach for smart operation of digital wastewater treatment plants[J]. Environmental Research,2023,224:115549.
[56] Chang P, Li Z. Over-complete deep recurrent neutral network based on wastewater treatment process soft sensor application[J]. Applied Soft Computing,2021,105: 107227.
[57] Ching P M L, Zou X, Wu D, et al. Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine[J]. Environmental Research,2022,210:112953.
[58] 裴力锋,陈伟杰,徐敬生,等.基于自注意力机制的污水处理厂精确加药模型预测控制[J].环境工程,2023:1-14.
[59] 刘杰,李佟,李军.基于改进支持向量回归机的污水处理厂出水总氮预测模型[J].环境工程学报,2018,12(01): 119-126.
[60] Zhang H, Yang C, Shi X, et al. Effluent quality prediction in papermaking wastewater treatment processes using dynamic Bayesian networks[J]. Journal of Cleaner Production,2021,282:125396.
[61] Xiong J, Tao Z, Hua L, et al. An evolutionary robust soft measurement technique via enhanced atom search optimization and outlier robust extreme learning machine for wastewater treatment process[J]. Journal of Water Process Engineering,2023,55:104102.[62] 柴伟,池彬彬.基于区间预测模型的污水处理厂传感器故障检测[J].计算技术与自动化,2020,39(01):23-28.
[63] 邱勇,刘雪洁,田宇心,等.一种水质监测物联网传感器的标定装置及方法:CN202210713258.0[P].2023-08-08.

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[2]金秀章,郝兆平,佟纯涛,等.KPCA和LSSVM在SCR脱硝反应器入口NOx含量软测量中的应用[J].工业仪表与自动化装置,2016,(05):124.
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备注/Memo

备注/Memo:
收稿日期:2024-01-29基金项目:中国长江三峡集团有限公司自主科研项目“长三角典型农村污水治理及资源化技术和成套装备研发”(WWKY-2021-0477)第一作者:陈亚松(1982—),男,正高级工程师,主要研究方向为水环境治理技术研究和应用。E-mail:chen_yasong@ctg.com.cn通信作者:邱勇(1977—),男,工学博士,副研究员,研究方向为污水处理过程控制与技术评估。E-mail:qiuyong@tsinghua.edu.cn
更新日期/Last Update: 1900-01-01