Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor

doi:10.1007/s12204-019-2047-9

Journal of Shanghai Jiao Tong University (Science) ›› 2019, Vol. 24 ›› Issue (4): 417-429.doi: 10.1007/s12204-019-2047-9

Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor

LI Dan (李丹), WANG Hongdong (王鸿东), LIANG Xiaofeng *(梁晓锋)

(a. School of Environmental Science and Engineering; b. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education; c. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

出版日期:2019-08-01 发布日期:2019-07-29
通讯作者: LIANG Xiaofeng *(梁晓锋) E-mail:liang_xiaofeng@sjtu.edu.cn

Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor

LI Dan (李丹), WANG Hongdong (王鸿东), LIANG Xiaofeng *(梁晓锋)

(a. School of Environmental Science and Engineering; b. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education; c. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Online:2019-08-01 Published:2019-07-29
Contact: LIANG Xiaofeng *(梁晓锋) E-mail:liang_xiaofeng@sjtu.edu.cn

摘要/Abstract

摘要： 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.

关键词: diagnostic analysis , modified sequencing batch reactor (MSBR), Bayesian network (BN), inference, probability distribution

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.

Key words: diagnostic analysis , modified sequencing batch reactor (MSBR), Bayesian network (BN), inference, probability distribution

中图分类号:

X 703.1

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 Jiao Tong University (Science), 2019, 24(4): 417-429.

参考文献 21

[1]	YANG H Z. Mechanism and technology process experiment to MSBR process for nitrogen and phosphorus removals of municipal wastewater [D]. Shanghai,China: School of Environmental Science and Engineering,Tongji University, 2001 (in Chinese).
[2]	LI D, YANG H Z, LIANG X F. Prediction analysis of a wastewater treatment system using a Bayesian network[J]. Environmental Modelling & Software, 2013,40: 140-150.
[3]	HE Q P, QIN S J, WANG J. A new fault diagnosis method using fault directions in Fisher discriminant analysis [J]. AIChE Journal, 2005, 51(2): 555-571.
[4]	BAGGIANI F, MARSILI-LIBELLI S. Real-time fault detection and isolation in biological wastewater treatment plants [J]. Water Science & Technology,2009, 60(11): 2949-2961.
[5]	GARCIA-ALVAREZ D, FUENTE M J, VEGA P, et al. Fault detection and diagnosis using multivariate statistical techniques in a wastewater treatment plant[J]. IFAC Proceedings Volumes, 2009, 42(11): 952-957.
[6]	HAN H G, LI Y, QIAO J F. A fuzzy neural network approach for online fault detection in waste water treatment process [J]. Computers & Electrical Engineering,2014, 40(7): 2216-2226.
[7]	SANCHEZ-FERN′ANDEZ A, FUENTE M J, SAINZPALMERO G I. Fault detection in wastewater treatment plants using distributed PCA methods [C]//2015IEEE 20th Conference on Emerging Technologies &Factory Automation (ETFA). Luxembourg: IEEE,2015: 1-7.
[8]	VARIS O. Belief networks for modelling and assessment of environmental change [J]. Environmetrics,2010, 6(5): 439-444.
[9]	NASH D, WATERS D, BULDU A, et al. Using a conceptual Bayesian network to investigate environmental management of vegetable production in the Lake Taihu region of China [J]. Environmental Modelling &Software, 2013, 46: 170-181.
[10]	NASH D, HANNAH M. Using Monte-Carlo simulations and Bayesian networks to quantify and demonstrate the impact of fertiliser best management practices[J]. Environmental Modelling & Software, 2011,26: 1079-1088.
[11]	ZHAO Y, WEN J, WANG S W. Diagnostic Bayesian networks for diagnosing air handling units faults. Part II: Faults in coils and sensors [J]. Applied Thermal Engineering,2015, 90: 145-157.
[12]	SUCH′ANEK P, MARECKI F, BUCKI R. Selflearning Bayesian networks in diagnosis [J]. Procedia Computer Science, 2014, 35: 1426-1435.
[13]	SAHELY B S G E, BAGLEY D M. Diagnosing upsets in anaerobic wastewater treatment using Bayesian belief networks [J]. Journal of Environmental Engineering,2001, 127(4): 302-310.
[14]	FRANCO C, HEPBURN L A, SMITH D J, et al. A Bayesian belief network to assess rate of changes in coral reef ecosystems [J]. Environmental Modelling &Software, 2016, 80: 132-142.
[15]	LANDUYT D, BROEKX S, D’HONDT R, et al. A review of Bayesian belief networks in ecosystem service modelling [J]. Environmental Modelling & Software,2013, 46: 1-11.
[16]	MOLINA J L, BROMLEY J, GARC′IA-AR′OSTEGUI J L, et al. Integrated water resources management of overexploited hydrogeological systems using objectoriented Bayesian networks [J]. Environmental Modelling & Software, 2010, 25(4): 383-397.
[17]	FORIO M A E, LANDUYT D, BENNETSEN E, et al.Bayesian belief network models to analyse and predict ecological water quality in rivers [J]. Ecological Modelling,2015, 312: 222-238.
[18]	AGUILERA P A, FERN′ANDEZ A, FERN′ANDEZ R, et al. Bayesian networks in environmental modelling[J]. Environmental Modelling & Software, 2011,26(12): 1376-1388.
[19]	CHEN S H, POLLINO C A. Good practice in Bayesian network modelling [J]. Environmental Modelling &Software, 2012, 37: 134-145.
[20]	MYLLYM′AKI P, SILANDER T, TIRRI H, et al.B-course: A web service for Bayesian data analysis[C]//International Conference on Tools with Artificial Intelligence. Dallas, TX, USA: IEEE, 2001: 247-256.
[21]	PRADHAN M, HENRION M, PROVAN G, et al. The sensitivity of belief networks to imprecise probabilities:An experimental investigation [J]. Artificial Intelligence,1996, 85(1/2): 363-397.

Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor

Bayesian Network Based Approach for Diagnosis of Modified Sequencing Batch Reactor

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 21

相关文章 4

编辑推荐

Metrics

本文评价

[1]	MA Guohong (马国红), LI Jian (李健), HE Yinshui (何银水), XIAO Wenbo (肖文波). Weld Geometry Monitoring for Metal Inert Gas Welding Process with Galvanized Steel Plates Using Bayesian Network[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(2): 239-244.
[2]	GAO Jianxiong (高建雄), AN Zongwen (安宗文), MA Qiang (马强), ZHAO Shendan (赵申诞). Probability Model of Residual Strength of Materials Under Uncertain Cyclic Load[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(2): 266-272.
[3]	YIN Yichao(殷毅超), HUANG Hongzhong (黄洪钟), LIU Zheng (刘征). Imprecise Probability Method with the Power-Normal Model for Accelerated Life Testing[J]. J Shanghai Jiaotong Univ Sci, 2019, 24(6): 805-810.
[4]	REN Fang-yu (任方宇), SI Shu-bin* (司书宾), CAI Zhi-qiang (蔡志强), ZHANG Shuai (张帅). Transformer Fault Analysis Based on Bayesian Networks and Importance Measures[J]. 上海交通大学学报（英文版）, 2015, 20(3): 353-357.