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Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor

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Abstract

Wastewater treatment is a complicated dynamic process affected by microbial, chemical and physical factors. Faults are inevitable during the operation of modified sequencing batch reactors (MSBRs) because of the uncertainty of various factors. Abnormal MSBR results require fault diagnosis to determine the cause of failure and implement appropriate measures to adjust system operations. Bayesian network (BN) is a powerful knowledge representation tool that deals explicitly with uncertainty. A BN-based approach to diagnosing wastewater treatment systems based on MSBR is developed in this study. The network is constructed using the knowledge derived from literature and elicited from experts, and it is parametrized using independent data from a pilot test. A one-year pilot study is conducted to verify the diagnostic analysis. The proposed model is reasonable, and the diagnosis results are accurate. This approach can be applied with minimal modifications to other types of wastewater treatment plants.

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Authors and Affiliations

  1. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Dan Li  (李丹)

  2. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200240, China

    Dan Li  (李丹), Hongdong Wang  (王鸿东) & Xiaofeng Liang  (梁晓锋)

  3. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hongdong Wang  (王鸿东) & Xiaofeng Liang  (梁晓锋)

Authors
  1. Dan Li  (李丹)
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  2. Hongdong Wang  (王鸿东)
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  3. Xiaofeng Liang  (梁晓锋)
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Corresponding author

Correspondence to Xiaofeng Liang  (梁晓锋).

Additional information

Foundation item: the Foundation of State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University (No. GKZD010071)

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Li, D., Wang, H. & Liang, X. Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor. J. Shanghai Jiaotong Univ. (Sci.) 24, 417–429 (2019). https://doi.org/10.1007/s12204-019-2047-9

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  • DOI: https://doi.org/10.1007/s12204-019-2047-9

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