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Path-Sampling and Machine Learning for Rare Abnormal Safety and Reliability Events
Path-Sampling and Machine Learning for Rare Abnormal Safety and Reliability Events
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152639
- ISBN
- 9798384025160
- DDC
- 660
- 서명/저자
- Path-Sampling and Machine Learning for Rare Abnormal Safety and Reliability Events
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 216 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: A.
- 주기사항
- Advisor: Seider, Warren D.;Patel, Amish J.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약It is crucial for chemical and manufacturing industries to ensure safe and reliable operation of their plants and processes, by mitigating safety issues (e.g., extreme operating conditions) and reliability issues (e.g., production losses). But, a significant challenge faced by these industries is that such events are rare and undesirable, with little occurrence data available from process historians. Extensive control and alarm systems, with Safety Instrumented Systems (SIS) and reliability risk assessment methods, are often successful in mitigating postulated abnormal events anticipated in HAZOPs. However, it is very challenging to consider the effects of highly infrequent unpostulated abnormal events (i.e., non-specific, randomly-occurring events), which cannot be anticipated in process design, and lead to severe consequences. Hence, in this dissertation, novel, improved multivariate alarm systems are developed using path-sampling and machine learning, for handling rare unpostulated abnormal events resulting from random perturbations in one or more process variables.As a first application of path-sampling to analyze rare abnormal events for chemical process safety, Moskowitz (2016) introduced transition path-sampling (TPS) to locate rare safety pathways for an exothermic continuous stirred-tank reactor (CSTR) and an air separation unit (ASU). In this dissertation, to circumvent the computational limitations posed by TPS, forward-flux sampling (FFS) is introduced. It simulates rare unpostulated abnormal events more-efficiently in a piecewise manner, moving from desirable to undesirable operating regions, with valuable key process-variable data stored during the simulations, followed by calculations of committer probabilities to reach undesirable regions (i.e., pB).Given the process variable-pB data, accurate predictive models are developed using machine learning (ML) - one of the cornerstones of Industry 4.0's vision for improved automation through digital transformation. Using predictions provided by the ML-based models, initial multivariate alarm systems are developed, which are improved significantly by introducing an alarm rationalization - dynamic risk analyses (DRAn) integrated framework. Such improved systems, when implemented alongside widely-used HAZOP studies, aid operators in handling both postulated and unpostulated abnormal events to improve overall safety and reliability.
- 일반주제명
- Chemical engineering
- 일반주제명
- Engineering
- 일반주제명
- Information science
- 키워드
- Industry 4.0
- 키워드
- Machine learning
- 기타저자
- University of Pennsylvania Chemical and Biomolecular Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-02A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384025160
■035 ▼a(MiAaPQ)AAI31484924
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aSudarshan, Vikram.
■24510▼aPath-Sampling and Machine Learning for Rare Abnormal Safety and Reliability Events
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a216 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: A.
■500 ▼aAdvisor: Seider, Warren D.;Patel, Amish J.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aIt is crucial for chemical and manufacturing industries to ensure safe and reliable operation of their plants and processes, by mitigating safety issues (e.g., extreme operating conditions) and reliability issues (e.g., production losses). But, a significant challenge faced by these industries is that such events are rare and undesirable, with little occurrence data available from process historians. Extensive control and alarm systems, with Safety Instrumented Systems (SIS) and reliability risk assessment methods, are often successful in mitigating postulated abnormal events anticipated in HAZOPs. However, it is very challenging to consider the effects of highly infrequent unpostulated abnormal events (i.e., non-specific, randomly-occurring events), which cannot be anticipated in process design, and lead to severe consequences. Hence, in this dissertation, novel, improved multivariate alarm systems are developed using path-sampling and machine learning, for handling rare unpostulated abnormal events resulting from random perturbations in one or more process variables.As a first application of path-sampling to analyze rare abnormal events for chemical process safety, Moskowitz (2016) introduced transition path-sampling (TPS) to locate rare safety pathways for an exothermic continuous stirred-tank reactor (CSTR) and an air separation unit (ASU). In this dissertation, to circumvent the computational limitations posed by TPS, forward-flux sampling (FFS) is introduced. It simulates rare unpostulated abnormal events more-efficiently in a piecewise manner, moving from desirable to undesirable operating regions, with valuable key process-variable data stored during the simulations, followed by calculations of committer probabilities to reach undesirable regions (i.e., pB).Given the process variable-pB data, accurate predictive models are developed using machine learning (ML) - one of the cornerstones of Industry 4.0's vision for improved automation through digital transformation. Using predictions provided by the ML-based models, initial multivariate alarm systems are developed, which are improved significantly by introducing an alarm rationalization - dynamic risk analyses (DRAn) integrated framework. Such improved systems, when implemented alongside widely-used HAZOP studies, aid operators in handling both postulated and unpostulated abnormal events to improve overall safety and reliability.
■590 ▼aSchool code: 0175.
■650 4▼aChemical engineering
■650 4▼aEngineering
■650 4▼aInformation science
■653 ▼aForward-flux sampling
■653 ▼aIndustry 4.0
■653 ▼aMachine learning
■653 ▼aMultivariate alarm systems
■653 ▼aRare abnormal events
■690 ▼a0542
■690 ▼a0800
■690 ▼a0537
■690 ▼a0723
■71020▼aUniversity of Pennsylvania▼bChemical and Biomolecular Engineering.
■7730 ▼tDissertations Abstracts International▼g86-02A.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163212▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
