Dissolved Oxygen Model Predictive Control for Activated Sludge Process Model Based on the Fuzzy C-means Cluster Algorithm

In this work, the problem of predictive control of dissolved oxygen for the activated sludge process model with high nonlinearity and strong coupling is addressed. Firstly, the determination of the structure of fuzzy rules is displayed established upon Activated sludge model 1 (ASM1). Besides, the fuzzy space is divided through the clustering algorithm of fuzzy C-means. The corresponding parameters are estimated by means of the well-known least squares method. Subsequently, a fuzzy predictive model of dissolved oxygen is established by using the historical data. The aim is to design a predictive controller that is capable of performing the online track of dissolved oxygen attributed to better dynamic response and steadier output in different weather. Ultimately, the availability and validity of the developed technique are verified by a comparison example.