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制药废水UASB-HAR-ICEAS组合处理系统效能建模及优化研究

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姜涛, 赵延华, 王鸿程. 制药废水UASB-HAR-ICEAS组合处理系统效能建模及优化研究[J]. 环境工程学报, 2014, 8(10): 4326-4332.
引用本文: 姜涛, 赵延华, 王鸿程. 制药废水UASB-HAR-ICEAS组合处理系统效能建模及优化研究[J]. 环境工程学报, 2014, 8(10): 4326-4332.
shu
Jiang Tao, Zhao Yanhua, Wang Hongcheng. Research on modeling and optimization of UASB-HAR-ICEAS combined treatment system[J]. Chinese Journal of Environmental Engineering, 2014, 8(10): 4326-4332.
Citation: Jiang Tao, Zhao Yanhua, Wang Hongcheng. Research on modeling and optimization of UASB-HAR-ICEAS combined treatment system[J]. Chinese Journal of Environmental Engineering, 2014, 8(10): 4326-4332.
shu

制药废水UASB-HAR-ICEAS组合处理系统效能建模及优化研究

  • 1.

    工业和信息化部电子第五研究所分析中心环保技术部, 广州 510610

  • 2.

    广州市环境保护工程设计院有限公司, 广州 510000

  • 基金项目:

  • 中图分类号: X52

Research on modeling and optimization of UASB-HAR-ICEAS combined treatment system

  • 1.

    The Fifth Electronics Research Institute of MIIT, P.R.China, Analysis Center Environmental Technology Department, Guangzhou 510610, China

  • 2.

    Guangzhou EP Environmental Engineering LTD., GuangZhou 510000, China

  • Fund Project:

  • 摘要: 以某制药废水的升流式厌氧污泥床（UASB）-水解酸化池（HAR）-间歇式循环延时曝气活性污泥法（ICEAS）新型组合处理系统为背景，分析该系统效能，并建立遗传算法优化神经网络（GA-BPNN）模型对系统出水水质进行仿真预测，并利用建立的GA-BPNN模型对系统的运行条件进行优化研究。研究表明，在稳态运行的120 d，系统对废水COD和NH3-N去除率分别为98.6%和86.6%；GA-BPNN模型对出水COD和NH3-N的预测结果和实际监测值之间的平均绝对百分误差为5.55%和6.99%，能很好地应用于组合系统的出水水质预测管理中；GA-BPNN模型还可求解出系统的最优化运行条件，为工程实际操作提供了坚实的理论基础。
    Abstract: Based on the background of a novel combination processing system(UASB-HAR-ICEAS) which was used to treat pharmaceutical wastewater,the system performance was analyzed,and a genetic algorithm-back propagation neural network(GA-BPNN) model was established to forecast the effluent quality,then operating conditions of the system were optimized by using this model.The results illustrated that during steady-state operation for 120 days,the removal rates of COD and NH3-N were 98.6% and 86.6%, respectively; the mean absolute percentage error of COD and NH3-N between the predicted results by GA-BPNN and the actual monitored value were 5.55% and 6.99%.So GA-BPNN can be well used in management of water quality prediction.Meanwhile,the model can optimize operation conditions of the system,which provides a solid theoretical foundation for engineering practice.
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出版历程
  • 收稿日期:  2013-11-04
  • 刊出日期:  2014-09-28
姜涛, 赵延华, 王鸿程. 制药废水UASB-HAR-ICEAS组合处理系统效能建模及优化研究[J]. 环境工程学报, 2014, 8(10): 4326-4332.
引用本文: 姜涛, 赵延华, 王鸿程. 制药废水UASB-HAR-ICEAS组合处理系统效能建模及优化研究[J]. 环境工程学报, 2014, 8(10): 4326-4332.
shu
Jiang Tao, Zhao Yanhua, Wang Hongcheng. Research on modeling and optimization of UASB-HAR-ICEAS combined treatment system[J]. Chinese Journal of Environmental Engineering, 2014, 8(10): 4326-4332.
Citation: Jiang Tao, Zhao Yanhua, Wang Hongcheng. Research on modeling and optimization of UASB-HAR-ICEAS combined treatment system[J]. Chinese Journal of Environmental Engineering, 2014, 8(10): 4326-4332.
shu

制药废水UASB-HAR-ICEAS组合处理系统效能建模及优化研究

  • 1. 工业和信息化部电子第五研究所分析中心环保技术部, 广州 510610
  • 2. 广州市环境保护工程设计院有限公司, 广州 510000
  • 收稿日期:  2013-11-04
基金项目:

摘要: 以某制药废水的升流式厌氧污泥床（UASB）-水解酸化池（HAR）-间歇式循环延时曝气活性污泥法（ICEAS）新型组合处理系统为背景，分析该系统效能，并建立遗传算法优化神经网络（GA-BPNN）模型对系统出水水质进行仿真预测，并利用建立的GA-BPNN模型对系统的运行条件进行优化研究。研究表明，在稳态运行的120 d，系统对废水COD和NH3-N去除率分别为98.6%和86.6%；GA-BPNN模型对出水COD和NH3-N的预测结果和实际监测值之间的平均绝对百分误差为5.55%和6.99%，能很好地应用于组合系统的出水水质预测管理中；GA-BPNN模型还可求解出系统的最优化运行条件，为工程实际操作提供了坚实的理论基础。

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