Soft-sensing method for wastewater treatment based on BP neural network

被引:8
|
作者
Wang, WL [1 ]
Ren, M [1 ]
机构
[1] Zhejiang Univ Technol, Hangzhou 310014, Peoples R China
关键词
D O I
10.1109/WCICA.2002.1021506
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
At present, wastewater treatment quality parameters can't be detected on-line. In this paper, the soft-sensing method based on artificial neural networks is proposed in order to resolve this problem. Wastewater treatment technique is analyzed systematically. ORP, DO, PH and MLSS which can be detected on-line are taken as the secondary variables. BOD, COD, N and P which can not be detected on-line are taken as the primary variables. BP Neural network for soft-sensing is proposed and trained using the testing data of practical treatment process. The simulation results show that the soft-sensing system of wastewater treatment based on BP neural network can correctly estimate the quality parameters on real time. Thus, the system can be accommodated to the changes of environment and implement the real time control of wastewater treatment.
引用
收藏
页码:2330 / 2332
页数:3
相关论文
共 50 条
  • [31] Application of soft measurement based on artificial neural network in wastewater treatment process
    Yue, XP
    Yan, L
    [J]. ISTM/2005: 6TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-9, CONFERENCE PROCEEDINGS, 2005, : 1931 - 1933
  • [32] Soft Sensing Based on Probabilistic Neural Network
    Wang, Qiang
    [J]. PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON CIVIL ENGINEERING AND TRANSPORTATION 2015, 2016, 30 : 1569 - 1572
  • [33] Soft-Sensing Method with Online Correction Based on Semi-Supervised Learning
    汤奇峰
    李德伟
    席裕庚
    [J]. Journal of Shanghai Jiaotong University(Science), 2015, 20 (02) : 171 - 176
  • [34] Soft-sensing method with online correction based on semi-supervised learning
    Tang Q.-F.
    Li D.-W.
    Xi Y.-G.
    [J]. Journal of Shanghai Jiaotong University (Science), 2015, 20 (2) : 171 - 176
  • [35] Soft-Sensing of Oxygen Content in Flue Gas Based on Artificial Intelligence Method
    Chang, Taihua
    Zhang, Jiefu
    Li, Jian
    Li, Qing
    [J]. INTERNATIONAL CONFERENCE ON CONTROL SYSTEM AND AUTOMATION (CSA 2013), 2013, : 246 - 249
  • [36] Soft sensing based on artificial neural network
    Yang, YX
    Chai, TY
    [J]. PROCEEDINGS OF THE 1997 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 1997, : 674 - 678
  • [37] Optimization of wastewater anaerobic digestion treatment based on GA-BP neural network
    Zhao, Hua-Yang
    Huang, Feng-Lan
    Li, Li
    Zhang, Chun-You
    [J]. DESALINATION AND WATER TREATMENT, 2018, 122 : 30 - 35
  • [38] Sliding window neural network based sensing of bacteria in wastewater treatment plants
    Alharbi, Mohammed
    Hong, Pei-Ying
    Laleg-Kirati, Taous-Meriem
    [J]. JOURNAL OF PROCESS CONTROL, 2022, 110 : 35 - 44
  • [39] Design of Soft-Sensing Model for Alumina Concentration Based on Improved Deep Belief Network
    Li, Xiangquan
    Liu, Bo
    Qian, Wei
    Rao, Guoyong
    Chen, Lijuan
    Cui, Jiarui
    [J]. PROCESSES, 2022, 10 (12)
  • [40] Application of multi-modeling neural network soft-sensing technique in the phosphoric acid by wet process
    Kan, Xiao-Xu
    Jin, Xiao-Ming
    [J]. Huagong Zidonghua Ji Yibiao/Control and Instruments in Chemical Industry, 2006, 33 (01): : 64 - 66