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Knowledge-Based Fuzzy Broad Learning Algorithm for Warning Membrane Fouling

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Abstract

Membrane fouling is a widespread problem that restricts the stable operation of membrane bioreactor (MBR) in wastewater treatment process (WWTP). However, it is difficult to avoid the occurrence of membrane fouling due to the lack of effective early warning methods. To deal with this problem, an intelligent early warning method, using a knowledge-based fuzzy broad learning (K-FBL) algorithm, is proposed for membrane fouling in this paper. First, the existing knowledge is extracted from the humanistic category of membrane fouling in the form of fuzzy rules. Then, the existing knowledge of membrane fouling can be used to compensate for the shortage of data sets. Second, a K-FBL algorithm is designed to train the fuzzy subsystems with the existing knowledge. Then, the uncertainties of membrane fouling process can be degraded to improve the learning performance. Third, a K-FBL-based early warning method is designed to realize the precise classification and provide the operational suggestions for membrane fouling. Finally, the experiment results of a real plant are given to demonstrate the effectiveness of this proposed K-FBL-based early warning method.

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Abbreviations

ANN:

Artificial neural network

BL:

Broad learning

DO:

Dissolved oxygen

NHE :

Effluent ammonia nitrogen

BODE :

Effluent biochemical oxygen demand

CODE :

Effluent chemical oxygen demand

TNE :

Effluent total nitrogen

TPE :

Effluent total phosphorus

FBL:

Fuzzy broad learning

FNN:

Fuzzy neural network

GWS:

Gas washing size

NHI :

Influent ammonia nitrogen

BODI :

Influent biochemical oxygen demand

CODI :

Influent chemical oxygen demand

SSI :

Influent soluble solid

TPI :

Influent total phosphorus

K-FBL:

Knowledge-based fuzzy broad learning

MBR:

Membrane bioreactor

ORP:

Oxidation–reduction potential

P :

Permeability

PD:

Permeability decay

PR:

Permeability recovery rate

PLC:

Programmable logical controller

RBFNN:

Radial basis function neural network

SC:

Sludge concentration

TMP:

Transmembrane pressure

T :

Turbidity

WF:

Water flow

WP:

Water pressure

WWTP:

Wastewater treatment process

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Acknowledgements

This work was supported by National Key Research and Development Project under Grants 2018YFC1900800-5, National Science Foundation of China under Grants 61890930-5, 61622301 and 61903010, Beijing Outstanding Young Scientist Program under Grant BJJWZYJH01201910005020, and CNPC Research Institute of Safety and Environmental Technology under Grant PPC2019009.

Funding

This study was funded by National Natural Science Foundation of China (61890930-5, 61622301 and 61903010).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Hong-Gui Han, Qian Zhang, Zheng Liu & Jun-Fei Qiao

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Hong-Gui Han, Qian Zhang, Zheng Liu & Jun-Fei Qiao

  3. Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China

    Hong-Gui Han, Qian Zhang, Zheng Liu & Jun-Fei Qiao

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  1. Hong-Gui Han
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  2. Qian Zhang
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  4. Jun-Fei Qiao
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Correspondence to Hong-Gui Han.

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Han, HG., Zhang, Q., Liu, Z. et al. Knowledge-Based Fuzzy Broad Learning Algorithm for Warning Membrane Fouling. Int. J. Fuzzy Syst. 23, 13–26 (2021). https://doi.org/10.1007/s40815-020-00988-6

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  • DOI: https://doi.org/10.1007/s40815-020-00988-6

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