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BP神经网络和ARIMA模型对污水处理厂出水总氮浓度的模拟预测

林佳敏 陈金良 林晶晶 李宣辑 马聪 张志强 沈亮

林佳敏, 陈金良, 林晶晶, 李宣辑, 马聪, 张志强, 沈亮. BP神经网络和ARIMA模型对污水处理厂出水总氮浓度的模拟预测[J]. 环境工程技术学报, 2019, 9(5): 573-578. doi: 10.12153/j.issn.1674-991X.2019.03.261
引用本文: 林佳敏, 陈金良, 林晶晶, 李宣辑, 马聪, 张志强, 沈亮. BP神经网络和ARIMA模型对污水处理厂出水总氮浓度的模拟预测[J]. 环境工程技术学报, 2019, 9(5): 573-578. doi: 10.12153/j.issn.1674-991X.2019.03.261
LIN Jiamin, CHEN Jinliang, LIN Jingjing, LI Xuanji, MA Cong, ZHANG Zhiqiang, SHEN Liang. The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model[J]. Journal of Environmental Engineering Technology, 2019, 9(5): 573-578. doi: 10.12153/j.issn.1674-991X.2019.03.261
Citation: LIN Jiamin, CHEN Jinliang, LIN Jingjing, LI Xuanji, MA Cong, ZHANG Zhiqiang, SHEN Liang. The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model[J]. Journal of Environmental Engineering Technology, 2019, 9(5): 573-578. doi: 10.12153/j.issn.1674-991X.2019.03.261

BP神经网络和ARIMA模型对污水处理厂出水总氮浓度的模拟预测

doi: 10.12153/j.issn.1674-991X.2019.03.261
详细信息
    作者简介:

    林佳敏(1996—),女,主要从事工业数据处理研究, 13276023278@163.com

    通讯作者:

    马聪 E-mail: mc@xmwaterenv.com

    沈亮 E-mail: shenliang@xmu.edu.cn

  • 中图分类号: X703

The simulation and prediction of TN in wastewater treatment effluent using BP neural network and ARIMA model

More Information
    Corresponding author: MA Cong E-mail: mc@xmwaterenv.com; SHEN Liang E-mail: shenliang@xmu.edu.cn
  • 摘要: 污水处理厂出水总氮(TN)浓度是评价水处理效果的关键指标之一。建立BP神经网络模型对污水处理厂脱氮工艺进行模拟,引入自回归整合移动平均模型(ARIMA模型)对污水处理厂未来短期出水TN浓度进行预测。结果表明:BP神经网络模型在训练集和测试集模拟结果的平均相对误差分别为15.9%和16.5%,模型预测结果的平稳性较差;ARIMA模型对未来7 d出水TN浓度的时序预测平均误差为4.41%,预测精度较高;2个模型相结合有助于实现污水处理厂快捷和高效的在线检测。

     

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出版历程
  • 收稿日期:  2018-11-16
  • 刊出日期:  2019-09-20

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