It is difficult to measure the online values of biochemical oxygen demand(BOD) due to the characteristics of nonlinear dynamics, large lag and uncertainty in wastewater treatment process. In this paper, based on the k...It is difficult to measure the online values of biochemical oxygen demand(BOD) due to the characteristics of nonlinear dynamics, large lag and uncertainty in wastewater treatment process. In this paper, based on the knowledge representation ability and learning capability, an improved T–S fuzzy neural network(TSFNN) is introduced to predict BOD values by the soft computing method. In this improved TSFNN, a K-means clustering is used to initialize the structure of TSFNN, including the number of fuzzy rules and parameters of membership function. For training TSFNN, a gradient descent method with the momentum item is used to adjust antecedent parameters and consequent parameters. This improved TSFNN is applied to predict the BOD values in effluent of the wastewater treatment process. The simulation results show that the TSFNN with K-means clustering algorithm can measure the BOD values accurately. The algorithm presents better approximation performance than some other methods.展开更多
Based on the monitoring data of chemical oxygen demand(COD),permanganate index(I Mn)and five-day biochemical oxygen demand(BOD 5)of surface water in Tongling section of Yangtze River,the linear relationship among the ...Based on the monitoring data of chemical oxygen demand(COD),permanganate index(I Mn)and five-day biochemical oxygen demand(BOD 5)of surface water in Tongling section of Yangtze River,the linear relationship among the three indexes in the annual data analysis and the internal reasons,as well as the linear relationship and changes among the three indexes in different seasons were analyzed.The results reveal that in terms of the whole year,COD,I Mn and BOD 5 had a significant correlation and good linear relationship.The fitting slopes of the three indexes were 3.89 of COD/I Mn,4.39 of COD/BOD 5 and 1.16 of I Mn/BOD 5,respectively,which corresponded to the proportional relationship among the three indexes.From the perspective of seasonal changes,there was a very significant correlation between the three indexes in spring and summer.In autumn and winter,only COD and I Mn had a good correlation,but they had a poor correlation with BOD 5.展开更多
针对污水处理过程具有非线性的特点,建立基于PSO-ESN神经网络的污水处理软测量模型,来对于污水处理关键水质参数BOD(Biochemical Oxygen Demand)进行预测。由于回声状态网络(Echo State Network,ESN)学习算法无法有效解决高维矩阵训练...针对污水处理过程具有非线性的特点,建立基于PSO-ESN神经网络的污水处理软测量模型,来对于污水处理关键水质参数BOD(Biochemical Oxygen Demand)进行预测。由于回声状态网络(Echo State Network,ESN)学习算法无法有效解决高维矩阵训练不可逆,采用基于粒子群优化算法对于回声状态神经网络输出权重进行训练,进而有效解决回声状态网络病态解的问题。仿真结果证明,所设计的基于关键水质参数生化需氧量(BOD)软测量模型,其应用在污水处理关键水质参数预测的有效性,且该软测量模型具有较高测量精度。展开更多
基金Supported by the National Natural Science Foundation of China(61203099,61034008,61225016)Beijing Science and Technology Project(Z141100001414005)+3 种基金Beijing Science and Technology Special Project(Z141101004414058)Ph.D.Program Foundation from Ministry of Chinese Education(20121103120020)Beijing Nova Program(Z131104000413007)Hong Kong Scholar Program(XJ2013018)
文摘It is difficult to measure the online values of biochemical oxygen demand(BOD) due to the characteristics of nonlinear dynamics, large lag and uncertainty in wastewater treatment process. In this paper, based on the knowledge representation ability and learning capability, an improved T–S fuzzy neural network(TSFNN) is introduced to predict BOD values by the soft computing method. In this improved TSFNN, a K-means clustering is used to initialize the structure of TSFNN, including the number of fuzzy rules and parameters of membership function. For training TSFNN, a gradient descent method with the momentum item is used to adjust antecedent parameters and consequent parameters. This improved TSFNN is applied to predict the BOD values in effluent of the wastewater treatment process. The simulation results show that the TSFNN with K-means clustering algorithm can measure the BOD values accurately. The algorithm presents better approximation performance than some other methods.
文摘Based on the monitoring data of chemical oxygen demand(COD),permanganate index(I Mn)and five-day biochemical oxygen demand(BOD 5)of surface water in Tongling section of Yangtze River,the linear relationship among the three indexes in the annual data analysis and the internal reasons,as well as the linear relationship and changes among the three indexes in different seasons were analyzed.The results reveal that in terms of the whole year,COD,I Mn and BOD 5 had a significant correlation and good linear relationship.The fitting slopes of the three indexes were 3.89 of COD/I Mn,4.39 of COD/BOD 5 and 1.16 of I Mn/BOD 5,respectively,which corresponded to the proportional relationship among the three indexes.From the perspective of seasonal changes,there was a very significant correlation between the three indexes in spring and summer.In autumn and winter,only COD and I Mn had a good correlation,but they had a poor correlation with BOD 5.
文摘针对污水处理复杂系统中关键水质参数生化需氧量(biochemical oxygen demand,BOD)难以准确实时预测的问题,在分析污水处理过程相关影响因素的基础上,提出一种基于敏感度分析法的自组织随机权神经网络(selforganizing neural network with random weights,SONNRW)软测量方法.该方法首先通过机理分析选取原始辅助变量,经过数据预处理,之后采用主元分析法对辅助变量进行精选,作为SONNRW的输入变量进行污水处理关键水质参数BOD的预测.SONNRW算法利用隐含层节点输出及其权值向量计算该隐含层节点对于残差的敏感度,根据敏感度大小对网络隐含层节点进行排序,删除敏感度较低的隐含层节点即冗余点.仿真结果表明:该软测量方法对水质参数BOD的预测精度高、实时性好、模型结构稳定,能够用于污水水质的在线预测.
文摘针对污水处理过程具有非线性的特点,建立基于PSO-ESN神经网络的污水处理软测量模型,来对于污水处理关键水质参数BOD(Biochemical Oxygen Demand)进行预测。由于回声状态网络(Echo State Network,ESN)学习算法无法有效解决高维矩阵训练不可逆,采用基于粒子群优化算法对于回声状态神经网络输出权重进行训练,进而有效解决回声状态网络病态解的问题。仿真结果证明,所设计的基于关键水质参数生化需氧量(BOD)软测量模型,其应用在污水处理关键水质参数预测的有效性,且该软测量模型具有较高测量精度。
