Study on clustering multi-model modeling method for activated sludge process

For a complex process like wastewater treatment, a single model suffers from heavy burden calculation and poor accuracy. A multi-model modeling method based on an improved supervised k-means clustering algorithm is proposed. The method introduced the cluster center initialization idea of CCIA algorithm into classical k-means clustering algorithm applied to group the data into clusters, and the least squares method is used to construct ARX sub-models. The system model is constructed by weighing all ARX sub-models. The proposed method is used to identify the ammonia concentration model for Benchmark wastewater treatment system and the actual plant process data. Simulation results show that the proposed method can be used to fit nonlinear characteristics of the system with high precision.