Equipment failure risk assessment model of wastewater treatment plant based on improved FMEA

doi:10.16265/j.cnki.issn1003-3033.2024.08.1513

Abstract

Abstract:

In order to ensure the normal operation of wastewater treatment plants and prevent equipment failures, an improved FMEA risk assessment model was proposed. Firstly, the FMEA method was used to identify the failure modes of wastewater treatment plant equipment, and it was combined with PFS to portray the uncertainty assessment information. Secondly, the subjective and objective weights were calculated using the stepwise weight assessment ratio analysis (SWARA) method and the maximum deviation method, and the comprehensive weights of the three risk factors were calculated through the game theory combination weights. Thirdly, the multi-objective optimization on basis of ratio analysis (MOORA) method was used for the equipment failure mode risk ranking. Finally, taking Changchun City Z wastewater treatment plant equipment failure risk assessment as an example, the model proposed in this paper was compared with traditional FMEA, Pythagorean fuzzy technique for order preference by similarity to ideal solution (PF-TOPSIS), and Pythagorean fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (PF-VIKOR), and the feasibility and effectiveness of the model were verified. The results show that the top 3 failure modes of wastewater plant equipment are that the grit extracted by the grit remover contains excessive organic matter, the diaphragm is dislodged or broken, and large foreign objects enter the pump.

Key words: wastewater treatment plant, failure mode and effect analysis (FMEA), equipment failure, risk assessment, Pythagorean fuzzy set (PFS)

CLC Number:

X913.4安全系统工程

LIU Xin, WU Junnan, PAN Dianqi, ZHANG Yichen, ZHANG Jiquan, KE Kai. Equipment failure risk assessment model of wastewater treatment plant based on improved FMEA[J]. China Safety Science Journal, 2024, 34(8): 101-107.

Figures/Tables 8

Table 1

Table 2

Table 3

Table 4

Table 5

Fig.1

Table 6

Fig.2

References 19

[1]	吴耀男, 林雷, 任新温, 等. 一种基于逻辑结构数的改进型FMEA方法[J]. 中国安全科学学报, 2021, 31(10):97-104. doi: 10.16265/j.cnki.issn1003-3033.2021.10.014
	WU Yaonan, LIN Lei, REN Xinwen, et al. An improved FMEA method based on logical structure number[J]. China Safety Science Journal, 2021, 31(10): 97-104. doi: 10.16265/j.cnki.issn1003-3033.2021.10.014
[2]	NIE Ru, TIAN Zhangpeng, WANG Xiaokang, et al. Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment[J]. Knowledge-Based Systems, 2018, 162: 185-201.
[3]	ALIZADEH S S, SOLIMANZADEH Y, MOUSAVI S, et al. Risk assessment of physical unit operations of wastewater treatment plant using fuzzy FMEA method: a case study in the northwest of Iran[J]. Environmental Monitoring and Assessment, 2022, 194(9): DOI:10.1007/s10661-022-10248-9.
[4]	SHI Hua, LIU Zheng, LIU Huchen. A new linguistic preference relation-based approach for failure mode and effect analysis with dynamic consensus reaching process[J]. Information Sciences, 2022, 610: 977-993.
[5]	KUMARI S, AHMAD K, KHAN Z A, et al. Failure mode and effects analysis of common effluent treatment plants of humid sub-tropical regions using fuzzy based MCDM methods[J]. Engineering Failure Analysis, 2023, 145: DOI: 10.1016/j.engfailanal.2022.107010.
[6]	BONAB S R, OSGOOEI E. Environment risk assessment of wastewater treatment using FMEA method based on Pythagorean fuzzy multiple-criteria decision-making[J]. Environment, Development and Sustainability, 2022: DOI: 10.1007/s10668-022-02555-5.
[7]	ZHU Jianghong, SHUAI Bin, LI Guofang, et al. Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context[J]. Journal of Loss Prevention in the Process Industries, 2020: 64: DOI: 10.1016/j.jlp.2020.104048.
[8]	YAGER R R. Pythagorean membership grades in multicriteria decision making[J]. IEEE Transactions on Fuzzy Systems, 2013, 22(4): 958-965.
[9]	ILBAHAR E, KARAŞAN A, CEBI S, et al. A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system[J]. Safety Science, 2018, 103: 124-136.
[10]	ZHANG Xiaolu, XU Zeshui. Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets[J]. International Journal of Intelligent Systems, 2014, 29(12): 1 061-1 078.
[11]	PENG Xindong, YANG Yong. Some results for Pythagorean fuzzy sets[J]. International Journal of Intelligent Systems, 2015, 30(11): 1 133-1 160.
[12]	ZHANG Xiaolu. A novel approach based on similarity measure for Pythagorean fuzzy multiple criteria group decision making[J]. International Journal of Intelligent Systems, 2016, 31(6): 593-611.
[13]	KERŠULIENÉ V, TURSKIS Z. Integrated fuzzy multiple criteria decision making model for architect selection[J]. Technological and Economic Development of Economy, 2011, 17(4): 645-666.
[14]	周英豪, 王文杰, 卢西洲, 等. 岩爆灾害博弈论组合赋权预测模型及应用[J]. 中国安全科学学报, 2022, 32(7):105-112. doi: 10.16265/j.cnki.issn1003-3033.2022.07.0620
	ZHOU Yinghao, WANG Wenjie, LU Xizhou, et al. Combination weighting prediction model and application of rock bust disaster based on game theory[J]. China Safety Science Journal, 2022, 32(7): 105-112. doi: 10.16265/j.cnki.issn1003-3033.2022.07.0620
[15]	辛酉阳, 杨德磊, 方前程. 改进GRA-TOPSIS模型在装配式建筑施工风险评估中的应用[J]. 安全与环境学报, 2023, 23(7):2 212-2 222.
	XIN Youyang, YANG Delei, FANG Qiancheng. Application of improved GRA TOPSIS model in risk assessment of prefabricated building construction[J]. Journal of Safety and Environment, 2023, 23(7): 2 212-2 222.
[16]	闫帅平. 基于WSR-FPP和云模型的地铁车站火灾安全评价[J]. 消防科学与技术, 2021, 40(2):279-283.
	YAN Shuaiping. Fire safety evaluation of subway station based on WSR-FPP and cloud model[J]. Fire Science and Technology, 2021, 40(2): 279-283.
[17]	KOKSALMIS E, KABAK Ö. Deriving decision makers' weights in group decision making: an overview of objective methods[J]. Information Fusion, 2019, 49: 146-160.
[18]	GUL M, AK M F, GUNERI A F. Pythagorean fuzzy VIKOR based approach for safety risk assessment in mine industry[J]. Journal of Safety Research, 2019, 69: 135-153.
[19]	ATTRI S D, SINGH S, DHAR A, et al. Multi-attribute sustainability assessment of wastewater treatment technologies using combined fuzzy multi-criteria decision-making techniques[J]. Journal of Cleaner Production, 2022, 357: DOI: 10.1016/j.jclepro.2022.131849.

故障模式语言变量	风险因子权重语言变量	PFN
极其低(EL)	极其不重要(EUI)	(0.10,0.99)
非常低(VL)	非常不重要(VUI)	(0.10,0.97)
低(L)	不重要(UI)	(0.25,0.92)
稍微低(ML)	稍微不重要(MUI)	(0.40,0.87)
一般(F)	一般(F)	(0.50,0.80)
稍微高(MH)	稍微重要(MI)	(0.60,0.71)
高(H)	重要(I)	(0.70,0.60)
非常高(VH)	非常重要(VI)	(0.80,0.44)
极其高(EH)	极其重要(EI)	(1.00,0.00)

序号	故障模式	可能导致的后果
FM₁	粗格栅杂物缠绕	过滤功能不佳
FM₂	限位开关故障或结冰	机器设备停机不工作
FM₃	大块异物进入水泵	损坏水泵
FM₄	集水井泥沙或淤泥增多	进水泵堵塞
FM₅	除砂机抽取的砂浆所含有机物过多	除砂率降低
FM₆	破碎机损坏	污泥无法及时排除
FM₇	膜片脱落或破裂	曝气不足
FM₈	周边传动刮泥机与积雪接触打滑	无法排泥
FM₉	悬浮固体沉积在紫外线管表面	消毒不彻底

序号	S					O					D
序号	TM₁	TM₂	TM₃	TM₄	TM₅	TM₁	TM₂	TM₃	TM₄	TM₅	TM₁	TM₂	TM₃	TM₄	TM₅
FM₁	VH	H	VH	VH	H	VH	MH	H	VH	H	ML	MH	F	ML	ML
FM₂	MH	MH	F	MH	F	VH	MH	L	VH	MH	ML	F	VH	L	F
FM₃	VH	VH	F	VH	H	MH	F	ML	MH	F	VH	H	VH	F	H
FM₄	MH	MH	H	MH	MH	F	VH	MH	F	MH	H	VH	L	MH	MH
FM₅	VH	VH	H	H	H	VH	VH	VH	VH	VH	F	VH	VH	VH	H
FM₆	MH	MH	H	MH	MH	L	MH	MH	F	F	VH	VH	MH	VH	H
FM₇	H	H	VH	H	H	L	VH	MH	H	MH	VH	VH	VH	VH	VH
FM₈	VH	H	H	VH	H	ML	VH	L	ML	ML	L	L	VH	H	F
FM₉	VH	H	H	H	H	ML	VH	L	ML	ML	L	VL	F	ML	L

风险因子	客观权重	主观权重	综合权重
S	0.226	0.378	0.270
O	0.323	0.330	0.325
D	0.451	0.292	0.405

故障模式序号	经典FMEA		PF-TOPSIS		PF-VIKOR		本文模型
故障模式序号	RPN	排序	贴近度CC_i	排序	综合值CQ_i	排序	My_i	排序
FM₁	248	4	0.592	3	0.128	3	0.102	6
FM₂	157.2	8	0.347	8	0.390	9	-0.020	8
FM₃	261.8	3	0.579	4	0.222	4	0.304	3
FM₄	223.2	6	0.442	6	0.288	6	0.117	5
FM₅	435.2	1	0.913	1	-0.085	1	0.672	1
FM₆	226.8	5	0.435	7	0.338	7	0.189	4
FM₇	345.6	2	0.757	2	0.082	2	0.505	2
FM₈	159.2	7	0.460	5	0.257	5	0.084	7
FM₉	101.8	9	0.226	9	0.352	8	-0.417	9