【作者】 杨马英； 周芳芹； 李军；

【Author】 Yang Maying Zhou Fangqin Li Jun (Information Engineering College, Zhejiang University of Technology, Hangzhou 310032, China) (Department of Mathematics and Physics, Quzhou College, Quzhou 324006, China) (Architectural and Civil Engineering College, Zhejiang University of Technology, Hangzhou 310032, China)

【机构】 浙江工业大学信息学院； 浙江工业大学建工学院；

【摘要】 在介绍序批式活性污泥法(SBR)的城市生活污水处理工艺的基础上,针对反应过程所具有的多变量、非线性和动态复杂反应的特点,利用水质参数与多个过程可测参数间的关联关系,提出了基于Elman递归人工神经网络的水质参数软测量模型．以ORP,DO浓度和pH值作为输入参数,可实现对COD,NH3,TP水质参数的软测量．基于污水处理实验数据建立软测量模型,结果表明,上述软测量模型对污水处理水质指标COD,NH3,TP具有理想的预测效果．更多还原

【Abstract】 An Elman recursive neural network based water quality parameters’ soft sensing model is proposed for the sequential wastewater treatment processes, regarding the characteristics of multivariable, nonlinear, dynamic and complicated reactions in the treatment process, and utilizing the relationship between water quality parameters and the measurable process parameters. DO (dissolved oxygen), ORP (oxidation reduction potential) and pH are selected as input parameters, which can realize the soft sensing of the water quality parameters of COD (chemical oxygen demand) , NH3 and TP( total phosphorus) . The wastewater treatment process experiments and the training and simulation of the soft sensing model based on the experimental data were conducted, and the results indicate that the soft sensing model proposed has good predictive effect for the water quality indices COD, NH3 and TP in the wastewater treatment process.更多还原