Volume 8 Issue 6
Nov.  2018
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LI Chunhua, HU Wen, YE Chun, LI Jinze, WEI Weiwei. Study on prediction of total nitrogen removal effect of a surface water purification device based on BP neural network[J]. Journal of Environmental Engineering Technology, 2018, 8(6): 651-655. doi: 10.3969/j.issn.1674-991X.2018.06.086
Citation: LI Chunhua, HU Wen, YE Chun, LI Jinze, WEI Weiwei. Study on prediction of total nitrogen removal effect of a surface water purification device based on BP neural network[J]. Journal of Environmental Engineering Technology, 2018, 8(6): 651-655. doi: 10.3969/j.issn.1674-991X.2018.06.086

Study on prediction of total nitrogen removal effect of a surface water purification device based on BP neural network

doi: 10.3969/j.issn.1674-991X.2018.06.086
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  • Corresponding author: 叶春 E-mail: yechbj@163.com
  • Received Date: 2018-03-12
  • Publish Date: 2018-11-20
  • A back propagation (BP) artificial neural network model was set up to predict the effect of nitrogen removal using a surface water purification device. The observed data of water quality parameters were used as study sample, and the raw water TN, ammonium nitrogen, nitrate nitrogen, CODMn and operation time of the device selected as projection parameter in this model. Besides, the multivariate linear regression model was introduced to compare with BP neural network. The results showed that the coefficient of determination of BP artificial neural network model was 0.985, which stayed at a high level. And the maximum error was 5.92%. Obviously, BP artificial neural network model was more precise, faster and better than multivariate linear regression model. It could accurately predict the removal effect of TN by purification device.

     

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  • [1]
    焦李成,杨淑媛,刘芳,等.神经网络七十年:回顾与展望[J].计算机学报,2016,39(8):1697-1716.

    JIAO L C,YANG S Y,LIU F,et al.Seventy years beyond neural networks:retrospect and prospect[J].Chinese Journal of Computers,2016,39(8):1697-1716.
    [2]
    MARZOUK M,ELKADI M.Estimating water treatment plants costs using factor analysis and artificial neural networks[J].Journal of Cleaner Production,2016,112:4540-4549.
    [3]
    QADERI F,BABANEJAD E.Prediction of the groundwater remediation costs for drinking use based on quality of water resource,using artificial neural network[J].Journal of Cleaner Production,2017,161:840-849.
    [4]
    黄胜伟,董曼玲.自适应变步长BP神经网络在水质评价中的应用[J].水利学报,2002,33(10):119-123.

    HUANG S W,DONG M L.Application of adaptive variable step size BP network to evaluate water quality[J].Journal of Hydraulic Engineering,2002,33(10):119-123.
    [5]
    WU B,HAN S,XIAO J,et al.Error compensation based on BP neural network for airborne laser ranging[J].Optik-International Journal for Light and Electron Optics,2016,127(8):4083-4088.
    [6]
    阎平凡,张长水.人工神经网络与模拟进化计算[M].北京:清华大学出版社,2005.
    [7]
    张青,王学雷,张婷,等.基于BP神经网络的洪湖水质指标预测研究[J].湿地科学,2016,14(2):212-218.

    ZHANG Q,WANG X L,ZHANG T,et al.Prediction of water quality index of Honghu Lake based on back propagation neural network model[J].Wetland Science,2016,14(2):212-218.
    [8]
    ZHANG S,WANG B,LI X,et al.Research and application of improved gas concentration prediction model based on grey theory and BP neural network in digital mine[J].Procedia Cirp,2016,56:471-475.
    [9]
    HU P,X SONG X Q.On PSO based BP neural network[J].Applied Mechanics and Materials,2014,602/603/604/605:3518-3521.
    [10]
    ZOU H X,ZOU X J,XIONG J T,et al.The correlative positioning error compensation of the vision system and the robot mechanism based on BP neural network[J].Key Engineering Materials,2014,621(34):513-518.
    [11]
    HAN H,LI Y,QIAO J.A fuzzy neural network approach for online fault detection in waste water treatment process[J].Computers & Electrical Engineering,2014,40(7):2216-2226.
    [12]
    WANG Z Q,ZHAO C.Study on the fuzzy neural network control used in wastewater treatment[J].Applied Mechanics & Materials,2006,71/72/73/74/75/76/77/78(Suppl 2):3127-3132.
    [13]
    HE G,HUANG C,GUO L,et al.Identification and adjustment of guide rail geometric errors based on BP neural network[J].Measurement Science Review,2017,17(3):135-144.
    [14]
    SINGH G,KANDASAMY J,SHON H K,et al.Measuring treatment effectiveness of urban wetland using hybrid water quality:artificial neural network (ANN) model[J].Desalination & Water Treatment,2011,32(1/2/3):284-290.
    [15]
    李金泽. 地表水净化装置在水质净化效果上的预测及装置改造后的仿真模拟研究[D].乌鲁木齐:新疆农业大学,2018.
    [16]
    DUDA R O,HART P E,STORK D G.Pattern classification[M].New York:John Wiley & Sons,2003.
    [17]
    朱庆生,周冬冬,黄伟.BP神经网络样本数据预处理应用研究[J].世界科技研究与发展,2012,34(4):624-626.

    ZHU Q S,ZHOU D D,HUANG W.Application research of preprocess in BP neural network sample data[J].World Sci-Tech R&D,2012,34(4):624-626.
    [18]
    陈威,艾婵.基于多元线性回归模型的武汉市水资源承载力研究[J].河南理工大学学报(自然科学版),2017,36(1):75-79.

    CHEN W,AI C.Research on water resources bearing capacity of Wuhan based on multivariate linear regression model[J].Journal of Henan Polytechnic University(Natural Science),2017,36(1):75-79.
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