Fault Detection in Wastewater Treatment Process Using Broad Slow Feature Neural Network With Incremental Learning Ability

Developing a fault detection model for the wastewater treatment process that combines satisfactory accuracy with comparatively low time overhead remains an exceedingly formidable endeavor. Fortunately, the broad slow feature neural network (BSFNN) perfectly embodies the dual advantages mentioned above. The BSFNN utilizes both slow feature windows and enhancement windows to extract significant and slowly varying information characterized by different velocities, which facilitates the learning of nonlinear and dynamic features related to superior monitoring accuracy. Another benefit of the BSFNN model is that it continues to retain the efficiency of the broad learning system with regard to time overhead, which is considerably decreased through employing the pseudoinverse strategy to determine network parameters. The operational environment often undergoes nonstationary dynamic changes in actual wastewater treatment processes. Especially in scenarios where higher monitoring accuracy is demanded or the network structure needs online adjustments to real-time update, and yet network adjustments can be time-consuming, the number of node parameters within incremental windows can be flexibly determined by dynamically adding enhancement nodes, which better obviates the necessity of retraining the entire BSFNN system from scratch, thereby allowing for online real-time adjustments to the structure and fault detection accuracy to achieve the desired performance. A case study using benchmark wastewater treatment platforms demonstrates that the suggested method outperforms advanced fault detection methods.