A Broad Learning System With Feature Augmentation for Soft Sensing of Urban Wastewater Process
Chang Peng, ShiRao Zhang · IEEE Transactions on Instrumentation and Measurement · 2024
Soft sensing techniques have been extensively employed to monitor water quality in the Urban Wastewater Treatment Process. Wastewater data often exhibit complex features, including nonlinearity, time correlation, and non-Gaussianity. Therefore, to establish an accurate soft sensing model, extracting complex features from wastewater data during the modeling process is essential. Considering the characteristics of wastewater data, a feature-augmented extraction broad learning system (FAE-BLS) is proposed for soft sensing applications. Inspired by the structure of recurrent networks, a recurrent cascading improvement of the feature window in the broad learning system (BLS) is implemented by FAE-BLS to extract the time correlation features of wastewater data. Furthermore, a feature window based on overcomplete independent component analysis (OICA) is proposed to extract the inherent non-Gaussian features of wastewater data. Finally, a method for fast online model updating is developed to address the decline in model accuracy under the harsh conditions of wastewater treatment processes. Experimental results on the wastewater simulation platform validate the effectiveness and superiority of the proposed approach.