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    Inherently safer design tool (i-SDT): A property-based risk quantification metric for inherently safer design during the early stage of process synthesis

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    Date
    2019
    Author
    Eljack, Fadwa
    Kazi, Monzure-Khoda
    Kazantzi, Vasiliki
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    Abstract
    In this work, an Inherently Safer Design Tool (i-SDT) is presented for early stage process synthesis to characterize and track the risk associated with different life-cycle phases of industrial processes. It also helps to develop characteristic equations for different safety parameters (i.e., flammability, explosiveness, toxicity, etc.) under various operating conditions. This property-based inherent safety quantification metric is a tailor made semi-quantitative safety analysis tool which provides safety assessment in a continuous manner to overcome the subjective nature of the existing available safety metrics. The core of this design and safety assessment tool is probabilistic risk quantification using accident and incident investigation (with over 600 incidents and within 27 years of time span), a property integration model and an exponential curve fitting method. The proposed safety metric has the flexibility to operate by identifying the major accident-prone units/sections of a process, as well as the major safety and operating parameters. The final output of this i-SDT tool is a cluster safety parameter score (CSP) which provides insights regarding the investigated unit/section or process for carrying out inherent safer design using a very limited amount of process information. The developed i-SDT tool was applied to compare different technologies of Ammonia processes in order to assess the safer option in terms of risks associated with the accident-prone unit/section and to highlight the areas of safety improvement in any existing process using the inherent safer design principles. In the future, this metric can can be embedded into a techno-economic framework to perform the cost and safety analysis simultaneously using available materials, design and accident information. - 2018 Elsevier Ltd
    DOI/handle
    http://dx.doi.org/10.1016/j.jlp.2018.12.004
    http://hdl.handle.net/10576/15117
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    • Chemical Engineering [‎1196‎ items ]

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