Data-Driven Intelligent Warning Method for Membrane Fouling

Membrane fouling has become a serious issue in membrane bioreactor (MBR) and may destroy the operation of the wastewater treatment process (WWTP). The goal of this article is to design a data-driven intelligent warning method for warning the future events of membrane fouling in MBR. The main novelties of the proposed method are threefold. First, a soft-computing model, based on the recurrent fuzzy neural network (RFNN), was proposed to identify the real-time values of membrane permeability. Second, a multistep prediction strategy was designed to predict the multiple outputs of membrane permeability accurately by decreasing the error accumulation over the predictive horizon. Third, a warning detection algorithm, using the state comprehensive evaluation (SCE) method, was developed to evaluate the pollution levels of MBR. Finally, the proposed method was inserted into a warning system to complete the predicting and warning missions and further tested in the real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.

Automated summary

This study proposed a data-driven intelligent warning method for predicting membrane fouling events in MBR. The method utilized an RFNN model, multistep prediction strategy, and SCE method to improve the accuracy of membrane permeability prediction and evaluate pollution levels in MBR for warning purposes. Experimental results confirmed the effectiveness and efficiency of the proposed method.