机器学习方法在污水处理系统中的应用

芮栋妮; 马燕燕; 叶林

doi:10.13205/j.hjgc.202206019

机器学习方法在污水处理系统中的应用

doi: 10.13205/j.hjgc.202206019

南京大学环境学院污染控制与资源化研究国家重点实验室, 南京 210023

详细信息

作者简介:
芮栋妮(1999-),男,硕士研究生,主要研究方向为污水生物处理。mf21250084@smail.nju.edu.cn

通讯作者:
叶林(1982-),男,副教授,主要研究方向为污水生物处理系统的解析与调控。linye@nju.edu.cn

计量
- 文章访问数: 915
- HTML全文浏览量: 87
- PDF下载量: 43
- 被引次数: 26
出版历程
- 收稿日期: 2021-12-21
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

APPLICATION OF MACHINE LEARNING METHODS IN WASTEWATER TREATMENT SYSTEMS

State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China

摘要

摘要: 由于污水处理系统一般较为复杂且受外界因素影响较多,对其进行精准调控一直是环境领域的难题之一。传统方法无法满足日益复杂的工程项目需求。近年来发展起来的机器学习方法为此类问题提供了一系列有效的解决方案。介绍了人工神经网络、支持向量机、随机森林等机器学习方法的特点,并从水质预测预警、污水处理系统故障诊断和智能控制3个方面阐述了机器学习方法在污水处理领域的应用,分析了机器学习方法相较于传统方法的优势及其应用于污水处理系统中存在的问题,展望了机器学习方法未来在污水处理领域应用的前景和趋势。
- 机器学习 /
- 污水处理 /
- 预测 /
- 故障诊断 /
- 智能控制
Abstract: Wastewater treatment systems are usually very complicated and may be affected by many external factors.Therefore,control and management of these systems are always one of the great challenges in environmental engineering.Traditional controlling and managing approaches could not meet the needs of the increasingly complex wastewater treatment facilities.While the recently-developed machine learning methods provide a series of effective solutions for such problems.This article introduces the characteristics of machine learning methods,including artificial neural networks,support vector machines,random forests,etc.,and explains the application of machine learning methods in the field of wastewater treatment systems from three aspects,i.e.water quality prediction and early warning,wastewater treatment system fault diagnosis and intelligent control.The advantages of machine learning methods and the challenges of their applications in wastewater treatment systems are also presented.In addition,the future development trends of machine learning methods in the field of wastewater treatment are outlined.
- machine learning /
- wastewater treatment /
- prediction /
- fault diagnosis /
- intelligent control

HTML全文

参考文献(68)

[1]	ILOMS E, OLOLADE O O, OGOLA H J O, et al. Investigating industrial effluent impact on municipal wastewater treatment plant in Vaal, South Africa[J]. International Journal of Environmental Research and Public Health, 2020, 17(3):1096.
[2]	NOURANI V, ELKIRAN G, ABBA S. Wastewater treatment plant performance analysis using artificial intelligence-an ensemble approach[J]. Water Science and Technology, 2018, 78(10):2064-2076.
[3]	黄道平,邱禹,刘乙奇,等.面向污水处理的数据驱动故障诊断及预测方法综述[J].华南理工大学学报(自然科学版), 2015, 43(3):111-120.
[4]	DAIGAVANE V V, GAIKWAD M. Water quality monitoring system based on IoT[J]. Advances in Wireless and Mobile Communications, 2017, 10(5):1107-1116.
[5]	VIJAYAKUMAR N, RAMYA R. The real time monitoring of water quality in IoT environment[C]//2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). Coimbatore, India:IEEE, 2015:1-5.
[6]	KODITALA N K, PANDEY P S. Water quality monitoring system using IoT and machine learning[C]//2018 International Conference on Research in Intelligent and Computing in Engineering (RICE). San Salvador:IEEE, 2018:1-5.
[7]	何清,李宁,罗文娟,等.大数据下的机器学习算法综述[J].模式识别与人工智能, 2014, 27(4):327-336.
[8]	MJALLI F S, AL-ASHEH S, ALFADALA H. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance[J]. Journal of Environmental Management, 2007, 83(3):329-338.
[9]	ZHONG S F, ZHANG K, BAGHERI M, et al. Machine learning:new ideas and tools in environmental science and engineering[J]. Environmental Science&Technology, 2021, 55(19):12741-12754.
[10]	JORDAN M I, MITCHELL T M. Machine learning:trends, perspectives, and prospects[J]. Science, 2015, 349(6245):255-260.
[11]	CABITZA F, BANFI G. Machine learning in laboratory medicine:waiting for the flood?[J]. Clinical Chemistry and Laboratory Medicine (CCLM), 2018, 56(4):516-524.
[12]	BERRY M W, MOHAMED A, YAP B W. Supervised and unsupervised learning for data science[M]. Springer, 2019:22-23.
[13]	YOON H, JUN S C, HYUN Y, et al. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer[J]. Journal of Hydrology, 2011, 396(1/2):128-138.
[14]	GOVINDARAJU R S. Artificial neural networks in hydrology. Ⅱ:hydrologic applications[J]. Journal of Hydrologic Engineering, 2000, 5(2):124-137.
[15]	GUO H, JEONG K, LIM J, et al. Prediction of effluent concentration in a wastewater treatment plant using machine learning models[J]. Journal of Environmental Sciences, 2015, 32:90-101.
[16]	TRICHAKIS I C, NIKOLOS I K, KARATZAS G. Artificial neural network (ANN) based modeling for karstic groundwater level simulation[J]. Water Resources Management, 2011, 25(4):1143-1152.
[17]	MUTTIL N, CHAU K W. Neural network and genetic programming for modelling coastal algal blooms[J]. International Journal of Environment and Pollution, 2006, 28(3/4):223-238.
[18]	LEE E, SEONG C, KIM H, et al. Predicting the impacts of climate change on nonpoint source pollutant loads from agricultural small watershed using artificial neural network[J]. Journal of Environmental Sciences, 2010, 22(6):840-845.
[19]	ZARE A, OZDEMIR A, IWEN M A, et al. Extension of PCA to higher order data structures:an introduction to tensors, tensor decompositions, and tensor PCA[J]. Proceedings of the IEEE, 2018, 106(8):1341-1358.
[20]	NA S, LIU X M, YONG G. Research on k-means clustering algorithm:an improved k-means clustering algorithm[C]//2010 Third International Symposium on Intelligent Information Technology and Security Informatics. Jian, China:Ieee, 2010:63-67.
[21]	ZHOU Z H. A brief introduction to weakly supervised learning[J]. National Science Review, 2017, 5(1):44-53.
[22]	PANG J W, YANG S S, HE L, et al. An influent responsive control strategy with machine learning:Q-learning based optimization method for a biological phosphorus removal system[J]. Chemosphere, 2019, 234:893-901.
[23]	GRANATA F, PAPIRIO S, ESPOSITO G, et al. Machine learning algorithms for the forecasting of wastewater quality indicators[J]. Water, 2017, 9(2):105.
[24]	HAMED M M, KHALAFALLAH M G, HASSANIEN E A. Prediction of wastewater treatment plant performance using artificial neural networks[J]. Environmental Modelling&Software, 2004, 19(10):919-928.
[25]	BAGHERI M, MIRBAGHERI S A, BAGHERI Z, et al. Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach[J]. Process Safety and Environmental Protection, 2015, 95:12-25.
[26]	李晓东,曾光明,蒋茹,等.改进支持向量机对污水处理厂运行状况的故障诊断[J].湖南大学学报(自然科学版), 2007,34(12):68-71.
[27]	LEE M W, HONG S H, CHOI H, et al. Real-time remote monitoring of small-scaled biological wastewater treatment plants by a multivariate statistical process control and neural network-based software sensors[J]. Process Biochemistry, 2008, 43(10):1107-1113.
[28]	陆林花.聚类算法及其在污水处理工艺故障诊断中应用的研究[D].重庆:重庆大学, 2007.
[29]	DOVŽAN D, LOGAR V,ŠKRJANC I. Implementation of an evolving fuzzy model (eFuMo) in a monitoring system for a waste-water treatment process[J]. IEEE Transactions on Fuzzy Systems, 2014, 23(5):1761-1776.
[30]	IZQUIERDO J, LÓPEZ P A, MARTÍNEZ F J, et al. Fault detection in water supply systems using hybrid (theory and data-driven) modelling[J]. Mathematical and Computer Modelling, 2007, 46(3/4):341-350.
[31]	岳宇飞,罗健旭.一种改进的SOM神经网络在污水处理故障诊断中的应用[J].华东理工大学学报, 2017,43(3):389-396.
[32]	LIUKKONEN M, LAAKSO I, HILTUNEN Y. Advanced monitoring platform for industrial wastewater treatment:multivariable approach using the self-organizing map[J]. Environmental Modelling&Software, 2013, 48:193-201.
[33]	BERNARDELLI A, MARSILI-Libelli S, MANZINI A, et al. Real-time model predictive control of a wastewater treatment plant based on machine learning[J]. Water Science and Technology, 2020, 81(11):2391-2400.
[34]	ICKE O, VAN ES D M, DE KONING M F, et al. Performance improvement of wastewater treatment processes by application of machine learning[J]. Water Science and Technology, 2020, 82(12):2671-2680.
[35]	SYAFIIE S, TADEO F, MARTINEZ E, et al. Model-free control based on reinforcement learning for a wastewater treatment problem[J]. Applied Soft Computing, 2011, 11(1):73-82.
[36]	de CANETE J F, DEL SAZ-OROZCO P, BARATTI R, et al. Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network[J]. Expert Systems with Applications, 2016, 63:8-19.
[37]	HERNANDEZ-DEL-OLMO F, GAUDIOSO E, DURO N, et al. Machine learning weather soft-sensor for advanced control of wastewater treatment plants[J]. Sensors, 2019, 19(14):3139.
[38]	FERNANDEZ DE CANETA J, DEL SAZ-OROZCO P, GÓMEZ-DE-GABRIEL J, et al. Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach[J]. Computers&Chemical Engineering, 2021, 144:107-146.
[39]	SUNDUI B, CALDERON O A R, ABDELDAYEM O M, et al. Applications of machine learning algorithms for biological wastewater treatment:updates and perspectives[J]. Clean Technologies and Environmental Policy, 2021,23:127-143.
[40]	VUJNOVIĆ G, PERIŠIĆ J, BOŽILOVIĆZ, et al. using scada system for process control in water industry[J]. Acta Technica Corviniensis-Bulletin of Engineering, 2019, 12(2):67-72.
[41]	WANG T, SUN J, ZHANG W, et al. Prediction of product formation in 2-keto-l-gulonic acid fermentation through Bayesian combination of multiple neural networks[J]. Process Biochemistry, 2014, 49(2):188-194.
[42]	HAN H G, QIAO J F. Prediction of activated sludge bulking based on a self-organizing RBF neural network[J]. Journal of Process Control, 2012, 22(6):1103-1112.
[43]	VENKATASUBRAMANIAN V, RENGASWAMY R, YIN K, et al. A review of process fault detection and diagnosis:Part I:quantitative model-based methods[J]. Computers&Chemical Engineering, 2003, 27(3):293-311.
[44]	蒋美迪.污水处理异常诊断智能算法及其应用研究[D].长春:吉林大学, 2015.
[45]	刘俊,梁炎明,尤海生.基于Lasso回归的污水处理设备故障诊断系统及方法:CN110320892A[P]. 2019.
[46]	肖红军,刘乙奇,黄道平.面向污水处理的动态变分贝叶斯混合因子故障诊断[J].控制理论与应用, 2016, 33(11):1519-1526.
[47]	TAO E, SHEN W, LIU T, et al. Fault diagnosis based on PCA for sensors of laboratorial wastewater treatment process[J]. Chemometrics and Intelligent Laboratory Systems, 2013, 128:49-55.
[48]	AHMED M, MAHMOOD A N, HU J. A survey of network anomaly detection techniques[J]. Journal of Network and Computer Applications, 2016, 60:19-31.
[49]	HASAN J, GOLDBLOOM-Helzner D, ICHIDA A, et al. Technologies and techniques for early warning systems to monitor and evaluate drinking water quality:a state-of-the-art review[Z]. United States Environmental Protection Agency, 2005:106-114.
[50]	GUPTA S, AGA D, PRUDEN A, et al. Data analytics for environmental science and engineering research[J]. Environmental Science&Technology, 2021, 55(16):10895-10907.
[51]	PAUL R, KENWAY S, MUKHEIBIR P. How scale and technology influence the energy intensity of water recycling systems:an analytical review[J]. Journal of Cleaner Production, 2019, 215:1457-1480.
[52]	BUTLER D, SCHVTZE M. Integrating simulation models with a view to optimal control of urban wastewater systems[J]. Environmental Modelling&Software, 2005, 20(4):415-426.
[53]	HEO S, NAM K, TARIQ S, et al. A hybrid machine learning-based multi-objective supervisory control strategy of a full-scale wastewater treatment for cost-effective and sustainable operation under varying influent conditions[J]. Journal of Cleaner Production, 2021, 291:125853.
[54]	MERCIER T, DEMBELE A, DENOEUX T, et al. machine learning as a decision support tool for wastewater treatment plant operation[J]. WIT Transactions on Ecology and the Environment, 2019, 229:103-107.
[55]	HERNANDEZ-DEL-OLMO F, GAUDIOSO E, NEVADO A. Autonomous adaptive and active tuning up of the dissolved oxygen setpoint in a wastewater treatment plant using reinforcement learning[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2011, 42(5):768-774.
[56]	HERNÁNDEZ-DEL-OLMO F, GAUDIOSO E, DORMIDO R, et al. Energy and environmental efficiency for the N-ammonia removal process in wastewater treatment plants by means of reinforcement learning[J]. Energies, 2016, 9(9):755.
[57]	CHOI D J, PARK H. A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process[J]. Water Research, 2001, 35(16):3959-3967.
[58]	VANROLLEGHEM P A. Models in advanced wastewater treatment plant control[C]//Proceedings Colloque Automatique et Agronomie, Montpellier, France. 2003:26.
[59]	HOLENDA B, DOMOKOS E, REDEY A, et al. Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control[J]. Computers&Chemical Engineering, 2008, 32(6):1270-1278.
[60]	HAN H G, QIAO J F, CHEN Q L. Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network[J]. Control Engineering Practice, 2012, 20(4):465-476.
[61]	叶洪涛,罗飞,许玉格,等.基于免疫优化PID神经网络的污水处理系统解耦控制[J].计算机应用研究, 2011, 28(11):4097-4099.
[62]	PIOTROWSKI R. Comparison of two nonlinear predictive control algorithms for dissolved oxygen tracking problem at wwtp[J]. Journal of Automation Mobile Robotics and Intelligent Systems, 2016, 10(1):8-16.
[63]	SYAH R, AL-KHOWARIZMI A, ELVENY M, et al. Machine learning based simulation of water treatment using LDH/MOF nanocomposites[J]. Environmental Technology&Innovation, 2021, 23:101805.
[64]	CAI W F, LONG F, WANG Y H, et al. Enhancement of microbiome management by machine learning for biological wastewater treatment[J]. Microbial Biotechnology, 2021, 14(1):59-62.
[65]	TORREGROSSA D, LEOPOLD U, HERNÁNDEZ-SANCHO F, et al. Machine learning for energy cost modelling in wastewater treatment plants[J]. Journal of environmental management, 2018, 223:1061-1067.
[66]	OH S, KIM Y. Machine learning application reveal dynamic interaction of polyphosphate-accumulating organism in full-scale wastewater treatment plant[J]. Journal of Water Process Engineering, 2021, 44:102417.
[67]	BAGHERZADEH F, MEHRANI M J, BASIRIFARD M, et al. Comparative study on total nitrogen prediction in wastewater treatment plant and effect of various feature selection methods on machine learning algorithms performance[J]. Journal of Water Process Engineering, 2021, 41:102033.
[68]	赖春伶.基于不平衡污水处理数据的故障诊断研究及软件开发[D].广州:华南理工大学, 2019.

施引文献

期刊类型引用(12)

1.	张建美，朱永茂，谢健强，吕钰楠，梅江雪，蒋兴超. 一株适用于短程反硝化的细菌筛选及其脱氮特性. 工业水处理. 2024(02): 87-94 . 百度学术
2.	王莎，李斌，孙江华. COD/N对短程内源反硝化过程中NO和N_2O积累的影响. 渭南师范学院学报. 2024(02): 86-94 . 百度学术
3.	马雨晴，王博，李笑迪. 温度与负荷对Anammox菌自富集启动的PDA的影响. 中国环境科学. 2024(03): 1307-1313 . 百度学术
4.	谢健强，张建美，吕钰楠，雷抗. 短程反硝化用于污水脱氮的机制、影响因素和工艺研究进展. 工业水处理. 2024(07): 19-28 . 百度学术
5.	平彩霞，周鑫，聂裕婷. 硫自养反硝化工艺亚硝酸盐积累研究进展. 工业水处理. 2024(08): 21-28 . 百度学术
6.	蒙小俊. 短程反硝化-厌氧氨氧化脱氮工艺研究进展. 工业用水与废水. 2023(01): 6-11+17 . 百度学术
7.	范荣桂，关怀远，张玛格. PN/A工艺应用于主流城市污水处理的研究进展. 应用化工. 2023(03): 880-884+891 . 百度学术
8.	迟冉，李伟英，温朵. 一株脱氮细菌鉴定及其效果研究. 微生物学杂志. 2023(03): 80-85 . 百度学术
9.	李聪，杜睿，彭永臻. 不同聚集形态短程反硝化耦合厌氧氨氧化系统脱氮性能与碳源利用特性. 环境工程. 2023(09): 1-9 . 本站查看
10.	尤俊豪，单洪涛，宗永臣. 低碳氮比城市污水的高效脱氮工艺与新型外加碳源研究现状. 工业用水与废水. 2022(03): 1-6+22 . 百度学术
11.	吴芳磊，贺航运，陈博涵，刘礼祥，李永，杜培坦，陈肖. 低C/N进水城镇污水厂低溶解氧运行效能及微生物变化. 环境工程学报. 2022(08): 2711-2719 . 百度学术
12.	蒙小俊，韩勇，黄志贵，龚晓松. Anammox用于主流污水处理的挑战与对策. 环境工程. 2022(10): 203-214 . 本站查看

其他类型引用(14)

资源附件(0)

访问统计

点击查看大图

计量

文章访问数: 915
HTML全文浏览量: 87
PDF下载量: 43
被引次数: 26

姓名
邮箱
手机号码
标题
留言内容
验证码

留言板

机器学习方法在污水处理系统中的应用

doi: 10.13205/j.hjgc.202206019

作者简介:
芮栋妮(1999-),男,硕士研究生,主要研究方向为污水生物处理。mf21250084@smail.nju.edu.cn

通讯作者:
叶林(1982-),男,副教授,主要研究方向为污水生物处理系统的解析与调控。linye@nju.edu.cn

计量

APPLICATION OF MACHINE LEARNING METHODS IN WASTEWATER TREATMENT SYSTEMS

期刊类型引用(12)

其他类型引用(14)

计量

目录

期刊在线

作者中心

友情链接

留言板

机器学习方法在污水处理系统中的应用

doi: 10.13205/j.hjgc.202206019

作者简介: 芮栋妮(1999-),男,硕士研究生,主要研究方向为污水生物处理。mf21250084@smail.nju.edu.cn

通讯作者: 叶林(1982-),男,副教授,主要研究方向为污水生物处理系统的解析与调控。linye@nju.edu.cn

计量

出版历程

APPLICATION OF MACHINE LEARNING METHODS IN WASTEWATER TREATMENT SYSTEMS

期刊类型引用(12)

其他类型引用(14)

计量

出版历程

目录

期刊在线

作者中心

友情链接

作者简介:
芮栋妮(1999-),男,硕士研究生,主要研究方向为污水生物处理。mf21250084@smail.nju.edu.cn

通讯作者:
叶林(1982-),男,副教授,主要研究方向为污水生物处理系统的解析与调控。linye@nju.edu.cn