Soft Computing of Biochemical Oxygen Demand Using an Improved T–S Fuzzy Neural Network

It is difficult to measure the online values of biochemical oxygen demand (BOD) due to the characteristics of nonlinear dynamics, large lag and uncertainty in wastewater treatment process. In this paper, based on the knowledge representation ability and learning capability, an improved T–S fuzzy neural network (TSFNN) is introduced to predict BOD values by the soft computing method. In this improved TSFNN, a K-means clustering is used to initialize the structure of TSFNN, including the number of fuzzy rules and parameters of membership function. For training TSFNN, a gradient descent method with the momentum item is used to adjust antecedent parameters and consequent parameters. This improved TSFNN is applied to predict the BOD values in effluent of the wastewater treatment process. The simulation results show that the TSFNN with K-means clustering algorithm can measure the BOD values accurately. The algorithm presents better approximation performance than some other methods.

Graphical abstractBOD predictions by two methods in the testing process. Biochemical oxygen demand (BOD) is one of the most important effluent qualities. Soft computing method is proposed as a useful tool to model the complex systems for process industries. An improved T–S fuzzy neural network (TSFNN) is introduced to predict BOD values by the soft computing method. A K-means clustering algorithm and a gradient descent method with the momentum item are used for structure identification and parameter learning of this improved TSFNN, respectively. The simulation results show that the TSFNN with K-means clustering algorithm can measure BOD values accurately. The algorithm presents better approximation performance than some other methods.Figure optionsDownload full-size imageDownload as PowerPoint slide