An early warning system for MBR based on multi-step prediction and deep belief network classifier

Abstract

Pollution of Membrane Bioreactor (MBR) remains a serious issue for the development of MBR process. An early warning system for stifling the risks of membrane pollution is of prime importance. Misdiagnosis, incorrectly treatment and over-treatment always exist in traditional early warning system, which cannot satisfy the growing requirements of these wastewater treatment plants (WWTPs). Considering all the factors mentioned above, in this paper, an early warning system based on data and knowledge was successfully developed, composed of forecasting and evaluation. First, a scheme was designed and developed for the early warning system. Second, a data-driven forecasting model was proposed as an important part of this system based on the theory of partial least squares (PLS) and time series multi-step prediction method. Finally, the early warning risk level was evaluated by expert knowledge and deep belief network (DBN) classifier, meanwhile, the pollution warning levels were output accordingly. Experimental results demonstrate that both the accuracy and effectiveness of the early warning system are greatly superior.