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.
LI Dan (李丹), WANG Hongdong (王鸿东), LIANG Xiaofeng *(梁晓锋)
. Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor[J]. Journal of Shanghai Jiaotong University(Science), 2019
, 24(4)
: 417
-429
.
DOI: 10.1007/s12204-019-2047-9
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