Abstract

One of the most important steps and the main bottleneck of the activated sludge wastewater treatment process (WWTP) is the secondary clarification, where sludge bulking is still a widespread problem. In this paper, an intelligent method, based on a knowledge-leverage-based fuzzy neural network (KL-FNN), is developed to predict sludge bulking online. This proposed KL-FNN can make full use of the data and the existing knowledge from the operation of WWTP. Meanwhile, a transfer learning mechanism is applied to adjust the parameters of the proposed method to improve the predicting accuracy. Finally, this proposed method is applied to a real wastewater treatment plant for predicting the sludge bulking risk, and then for predicting the sludge bulking. The experimental results indicate that the proposed prediction method can be used as a tool to achieve better performance and adaptability than the existing methods in terms of predicting accuracy for sludge bulking.

You do not currently have access to this content.