Quality risk management for microbial control in membrane-based water for injection production using fuzzy-failure mode and effects analysis.

IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE PeerJ Computer Science Pub Date : 2024-12-23 eCollection Date: 2024-01-01 DOI:10.7717/peerj-cs.2565
Luoyin Zhu, Yi Liang
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Abstract

Microbial proliferation presents a significant challenge in membrane-based water for injection (WFI) production, particularly in systems with storage and ambient distribution, commonly refered to as cold WFI production. A comprehensive microbial risk assessment of membrane-based WFI systems was performed by employing Fuzzy-Failure Mode and Effects Analysis (Fuzzy-FMEA) to evaluate the potential microbial risks. Failure modes were identified and prioritized based on the Risk Priority Number (RPN), with appropriate preventive measures recommended to control failure modes that could increase the microbial load and mitigate their impact. Key hazards were identified including fouling of ultrafiltration (UF) membranes, insufficient sealing of heat exchangers, leakage in reverse osmosis (RO) membranes, and ineffective vent filters unable to remove airborn microorganism. Based on Fuzzy-FMEA results, suggestions for optimization were proposed to improve microbial control in membrane-based WFI systems in the pharmaceutical industry.

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基于模糊失效模式和影响分析的膜基注水井微生物控制质量风险管理。
微生物增殖对膜基注入水(WFI)生产提出了重大挑战,特别是在具有储存和环境分布的系统中,通常被称为冷WFI生产。采用模糊失效模式和效应分析(Fuzzy-FMEA)对膜基WFI系统的潜在微生物风险进行了综合评价。根据风险优先级编号(RPN)确定故障模式并对其进行优先级排序,并建议采取适当的预防措施来控制可能增加微生物负荷并减轻其影响的故障模式。确定的主要危害包括超滤(UF)膜的污垢,热交换器的密封不足,反渗透(RO)膜的泄漏以及无效的排气过滤器无法去除空气中的微生物。基于模糊fmea结果,提出了优化建议,以改善制药行业基于膜的WFI系统的微生物控制。
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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