Intelligent systems for HAZOP analysis of complex process plants

Venkatasubramanian, Venkat; Zhao, Jinsong; Viswanathan, Shankar

doi:10.1016/s0098-1354(00)00573-1

Cited by 137 publications

(63 citation statements)

References 50 publications

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“…The methodology is composed of ontology correlated-layers, and AND-OR partitioning matrices for fault propagation reasoning and failure mode partitioning [5][6][7][8]. Ontology correlated-layers matrix considers fault propagation as a sequence of actions to achieve a specific failure mode.…”

Section: Methodsmentioning

confidence: 99%

“…The context-free grammars are supported by ISO 14224 [5] and OREDA [6], in which an exhaustive technical terminology and format related to maintenance, safety are explain by relevant experts. To provide syntactic and logical forms, we employ FMEA, FTA, ETA & HAZOP to identify cascade events [4,[8][9][10]. To reach a pure knowledge and interpretation from a causality context (logic form), prepared by semantic interpretation, which can be developed and shared among users, we need a base to define and support a common data model for long-term data integration, access and exchange.…”

Section: Methodsmentioning

confidence: 99%

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Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Ebrahimipour¹

2014

Int J of Swarm Intel Evol Comput

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Integrated Intelligent Equipment Health Management System (IIEHMS) is a systematic engineering design Framework that includes multi-functional disciplines (such as mechanical, electrical, optical and software engineering, quality assurance, and manufacturing) with their partners and suppliers works together to achieve a green topology plant with respect to life cycle cost. The proposed structural framework is defined as fitness for operation at any time during plant life cycle. Plant life cycle includes the entire spectrum of activities; beginning with the identification of need and extending through plant design and development, production and/or construction, operational use and sustaining maintenance and support, and plant retirement and material disposal. The methodology recognizes five main aspects of the framework as follows: a) Plant Topology and Semantic Knowledge Acquisition Modeling, to synchronize different failure mechanisms database, environment and safety incident and accident reports, and properly retrieve recorded data and synchronize them during decision making process b) Equipment Failure Reasoning & Modeling, to better understand, represent and address equipment failure mode and their impacts on process, environment and human health c) Adaptive Intelligent Fault Diagnosis & Prognosis, which is fault diagnosis process modeling and recommendations including fault detection, fault isolation, scenario assessment and mitigation plan according to Environmental/safety/Health/Business requirements, d) Green Assessment of diagnosis scenarios, to particularly evaluate CO 2 emissions, the consequence of each failure scenario from the view point of pollution, risk, and global warming human health and ecological risks due to toxicity of pollutants emitted into the environment and social impact e) Reliability, Maintainability and Availability prediction and lifetime estimation and, to calculate some measure of performance that can be used in the instantaneous actions decision making, and to prevent future accidents through preventive actions f) Optimization of purchasing cost considering lifetime, warranty and environmental consciousness, to choose suppliers that minimize LCC and maximize the reliability of equipment and spare parts. Moreover, in the model, we incorporated failure mode, effects, and criticality analysis (FMECA) through which an enterprise is able to evaluate the external failure costs of purchased equipment/ components in order to optimize their choice of suppliers/vendors who provide better components for operation. Those aspects are included in an Equipment Health Management System that aims at assuring the best actions and decisions during plant life cycle and lifetime. They are linked to the result-type information and knowledge flows coming from internal/external parties, experts and stake holders and finally from the dynamic and continuous fault recognition process.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Ebrahimipour¹

2014

Int J of Swarm Intel Evol Comput

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“…However, 70% of the time they require is devoted to routine deviations (Venkatasubramanian et al, 2000). The automation of the procedure saves time and hence money.…”

Section: Comparison With Other Methodologiesmentioning

confidence: 99%

“…The proposed assistant only needs a model (a D-higraph) of the process and not a model for each node of the HAZOP analysis, like the MFM HAZOP assistant (Rossing et al, 2010). The rule database is also common for all of the process because it is related to the relations between the elements of the model and not to the process itself, like HAZOPExpert (Venkatasubramanian et al, 2000), however, HAZOPExpert is able to deal with batch processes. Another advantage of this approach, when compared with the MFM methodology, is that the conclusions of the study can be directly mapped to the devices and equipment of the process.…”

Section: Comparison With Other Methodologiesmentioning

confidence: 99%

Functional modeling applied to HAZOP automation

Mata

Rodríguez

2012

Computer Aided Chemical Engineering

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In this paper we present a new tool to perform guided HAZOP analyses. This tool uses a functional model of the process that merges its functional and its structural information in a natural way. The functional modeling technique used is called D-higraphs. This tool solves some of the problems and drawbacks of other existing methodologies for the automation of HAZOPs. The applicability and easy understanding of the proposed methodology is shown in an industrial case.

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“…Many techniques for identifying hazards under a more general framework of quantitative risk assessment (QRA) have been used in process plants. Some of them are the Hazard and Operability (HAZOP) (Venkatasubramanian et al, 2000), Structured What-If Technique (SWIFT), Hazardous Scenario Analysis (HAZSCAN) (Lauridsen et al, 2001) etc. The systems risk modelling methodology, as proposed in this paper, can be extended to incorporate these effects.…”

Section: Introductionmentioning

confidence: 99%

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

Pan

Korre

Durucan

2016

Computers & Chemical Engineering

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Large scale engineering process systems are subject to a variety of risks which affect the productivity and profitability of the industry in the long run. This paper outlines the short comings of the current methods of risk quantification and proposes a systems engineering framework to overcome these issues. The functionality of the developed model is illustrated for the case of mineral processing and metal production industries using a copper ore processing and refined metal production case study. The methodology provides a quantitative assessment of the risk factors and allows the opportunity to minimise financial losses, which would help investors, insurers and plant operators in these sectors to make appropriate risk hedging policies. The models developed can also be coupled with evolutionary or swarm based algorithms for optimising the systems. A numerical example is illustrated to demonstrate the validity of the proposition.

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Intelligent systems for HAZOP analysis of complex process plants

Cited by 137 publications

References 50 publications

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Integrated Intelligent Equipment Health Management System to Support Life Cycle Decisions Considering Environmentally Conscious Production

Functional modeling applied to HAZOP automation

A systems based approach for financial risk modelling and optimisation of the mineral processing and metal production industry

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