关键词: 污水处理过程, 化学需氧量（COD）指标, 分布式改进BP神经网络, 灰色预测, 集成软测量模型, 动态加权方法

Abstract: Aiming at the problem of online information of COD index which can’t be measured directly in the wastewater treatment process, an integrated soft-sensing model of COD index based on distributed improved BP neural network and grey prediction is proposed. In order to reflect the different status in wastewater treatment process, the satisfactory clustering algorithm is utilized to cluster the data samples and form several subsets. The improved BP neural network method is used to establish prediction model for each subset. The Euclidean distance between the current input data and each clustering center is calculated and the COD index prediction value of distributed neural network is obtained through combining the output of some prediction model whose Euclidean distance is small. According to the historical data of COD index, the improved grey prediction method is used to predict the current value of COD index, which can reflect the time dependence of COD index. The dynamic weighted method is adopted to integrate the two obtained COD index prediction value. Simulation results show that the proposed integrated soft-sensing model has good prediction performance and can satisfy the precision requirements of COD index measuring in wastewater treatment process.

Key words: wastewater treatment process, Chemical Oxygen Demand（COD） index, distributed improved BP neural network, grey prediction, integrated soft-sensing model, dynamic weighted method