An intelligent detection method for bulking sludge of wastewater treatment process. (August 2018)

• Record Type: Journal Article
• Authors: Han, Hong-Gui; Liu, Zheng; Guo, Ya-Nan; Qiao, Jun-Fei
• Is Part Of: Journal of process control. Volume 68(2018)
• Page Start: 118
• Page End: 128
• Publication Date: 2018-08
• Subjects: Bulking sludge -- Self-organizing recurrent radial basis function neural network -- Cause variables identification algorithm -- Wastewater treatment process -- Sludge volume index; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.05.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: Predict the SVI values from the water qualities by using SORRBFNN. Detect the bulking sludge and the relation variables via the CVI algorithm. This intelligent detection method has been successfully used in a real wastewater treatment plant. Abstract: Prediction of bulking sludge is a matter of growing importance around the world. In this study, to detect bulking sludge of wastewater treatment process (WWTP), an intelligent detection method, using a self-organizing recurrent radial basis function neural network (SORRBFNN) and a cause variables identification (CVI) algorithm, was developed to detect the fault points and the fault variables of bulking sludge. For this intelligent detection method, first, the structure and parameters of SORRBFNN were updated by an information-oriented algorithm (IOA) and an improved Levenberg-Marquardt (LM) algorithm to improve the prediction accuracy of the sludge volume index (SVI) from the water qualities. Second, the CVI algorithm was designed to allow a quick revealing of the cause variables of bulking sludge with high accuracy. And the intelligent detection method was tested on the measured data from a real WWTP. Experimental results confirmed the attractiveness and effectiveness of the proposed intelligent detection method.