Soft measurement of effluent index in sewage treatment process based on overcomplete broad learning system

Abstract

To measure whether the sewage treatment meets the standards, biochemical oxygen demand (BOD5) is often used to determine, but the measurement of this indicator often has a long time lag and difficult to observe the real-time changes of BOD5, which brings inconvenience to the industrial process. The soft measurement technology based on neural network can realize BOD5 prediction at every moment by means of auxiliary variables, which has attracted people’s attention. However, there are still two problems with soft measurement technology, neural network-based soft measurement technology has high computational complexity and a certain time delay in measurement; and it cannot handle non-Gaussian data well. To solve them, this paper introduces an over-complete broad learning system (OBLS) based on feature fusion to deal with the problems of real-time measurement of BOD5 in sewage treatment industrial process. In view of the data characteristics, the feature extraction ability of the BLS is improved, the non-Gaussian characteristic of sewage data is captured by the method of Overcomplete Independent Component Analysis (OICA), and the OBLS is used to deal with the real-time soft measurement. Compared with state-of-the-art methods on the sewage standard test platform, the measurement accuracy of the proposed algorithm is found to be higher and the performance is more stable.