Modeling of Wastewater Treatment Processes Using Dynamic Bayesian Networks Based on Fuzzy PLS

被引:24
作者
Liu, Hongbin [1 ,2 ]
Zhang, Hao [1 ]
Zhang, Yuchen [1 ]
Zhang, Fengshan [2 ]
Huang, Mingzhi [3 ,4 ]
机构
[1] Nanjing Forestry Univ, Coinnovat Ctr Efficient Proc & Utilizat Forest Re, Nanjing 210037, Peoples R China
[2] Shandong Huatai Paper Co Ltd, Lab Comprehens Utilizat Paper Waste Shandong Prov, Dongying 257335, Peoples R China
[3] South China Normal Univ, Sch Environm, SCNU Environm Res Inst, Guangdong Prov Key Lab Chem Pollut & Environm Saf, Guangzhou 510006, Peoples R China
[4] South China Normal Univ, Sch Environm, MOE Key Lab Theoret Chem Environm, Guangzhou 510006, Peoples R China
关键词
Bayes methods; Data models; Feature extraction; Wastewater treatment; Monitoring; Uncertainty; Process modeling; Fuzzy partial least squares; latent variables; Bayesian networks; dynamic process modeling; wastewater treatment processes; PARTIAL LEAST-SQUARES; SOFT-SENSORS; TREATMENT PLANTS; FAULT-DIAGNOSIS; PREDICTION; SYSTEM; FRAMEWORK;
D O I
10.1109/ACCESS.2020.2995068
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The complicated characteristics of wastewater treatment plants (WWTPs) significantly hinder the monitoring of industrial processes, and thus much attention has been paid to process modeling and prediction. A fuzzy partial least squares-based dynamic Bayesian networks (FPLS-DBN) is proposed to improve the modeling ability in WWTPs. To adapt the nonlinear process data, fuzzy partial least squares (FPLS) is introduced by using a fuzzy system to extract nonlinear features from process data. In addition, a dynamic extension is included by embedding augmented matrices into Bayesian networks to fit the uncertainty and time-varying characteristics. Regarding the quality indices for effluent suspended solid in the WWTP, the root mean square error of the FPLS-DBN model is decreased by 28.63% and 69.47%, respectively, in comparison with that for partial least squares and Bayesian networks. The results demonstrate the superiority of FPLS-DBN in modeling performance for an actual industrial WWTP application.
引用
收藏
页码:92129 / 92140
页数:12
相关论文
共 50 条
  • [21] A procedure to detect problems of processes in software development projects using Bayesian networks
    Perkusich, Mirko
    Soares, Gustavo
    Almeida, Hyggo
    Perkusich, Angelo
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (01) : 437 - 450
  • [22] Dynamic fault tree analysis based on continuous-time Bayesian networks under fuzzy numbers
    Li, Yan-Feng
    Mi, Jinhua
    Liu, Yu
    Yang, Yuan-Jian
    Huang, Hong-Zhong
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY, 2015, 229 (06) : 530 - 541
  • [23] A framework for the analysis of dynamic processes based on Bayesian networks and case-based reasoning
    Barrientos, MA
    Vargas, JE
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 1998, 15 (3-4) : 287 - 294
  • [24] Using dynamic Bayesian networks as simulation metamodels based on bootstrapping
    Kelleher, Clayton T.
    Hill, Raymond R.
    Bauer, Kenneth W.
    Miller, J. O.
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2018, 115 : 595 - 602
  • [25] Dynamic-static model for monitoring wastewater treatment processes
    Han, Hong-Gui
    Sun, Chen-Xuan
    Wu, Xiao-Long
    Yang, Hong-Yan
    Zhao, Nan
    Li, Jie
    Qiao, Jun-Fei
    [J]. CONTROL ENGINEERING PRACTICE, 2023, 132
  • [26] Gait Type Analysis Using Dynamic Bayesian Networks
    Kozlow, Patrick
    Abid, Noor
    Yanushkevich, Svetlana
    [J]. SENSORS, 2018, 18 (10)
  • [27] Bayesian Neural Networks with Weight Sharing Using Dirichlet Processes
    Roth, Wolfgang
    Pernkopf, Franz
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 42 (01) : 246 - 252
  • [28] A Review of Computational Modeling in Wastewater Treatment Processes
    Duarte, M. Salome
    Martins, Gilberto
    Oliveira, Pedro
    Fernandes, Bruno
    Ferreira, Eugenio C.
    Alves, M. Madalena
    Lopes, Frederico
    Pereira, M. Alcina
    Novais, Paulo
    [J]. ACS ES&T WATER, 2023, 4 (03): : 784 - 804
  • [29] Multiresolution Dynamic Mode Decomposition Based Modeling of Wastewater Treatment Process
    Prakash, Om
    Huang, Biao
    [J]. IFAC PAPERSONLINE, 2024, 58 (28): : 869 - 874
  • [30] Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks
    Medina, Edgardo
    Roberto Fonseca, Carlos
    Gallego-Alarcon, Ivan
    Morales-Napoles, Oswaldo
    Angel Gomez-Albores, Miguel
    Esparza-Soto, Mario
    Alberto Mastachi-Loza, Carlos
    Garcia-Pulido, Daury
    [J]. WATER, 2022, 14 (08)