کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6974272 | 1453326 | 2018 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
The application of Bayesian - Layer of Protection Analysis method for risk assessment of critical subsea gas compression systems
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بهداشت و امنیت شیمی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Subsea gas compression system (SGCS) is a new critical subsea-to-shore field development solution that could reduce costs and environmental footprint. However, this system is not without inherent and operational risks. It is therefore, vital to evaluate the possible risks associated with SGCS to ensure the safe operation of the system. To this end, Layer of Protection Analysis (LOPA) is a suitable method for the estimation of possible risks. However, the failure rate data from SGCS required for LOPA is sparse and mostly developed from experimental testing. Bayesian (BL) logic is an effective tool that could be used to resolve this shortfall. In this paper, generic data from a secondary database was updated with SGCS specific data using BL logic to give a better risk frequency value. The key findings show that the posterior values derived from the BL-LOPA methodology are safer and more reliable to implement for an event scenario when compared to literature, expert judgement and generic data; therefore recommending an improved judgement in the application of safety instrumented systems for a required safety integrity level. The case studies used demonstrated that the BL-LOPA risk assessment method is sufficiently robust for quantifying uncertainties in new process facilities with sparse data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Process Safety and Environmental Protection - Volume 113, January 2018, Pages 305-318
Journal: Process Safety and Environmental Protection - Volume 113, January 2018, Pages 305-318
نویسندگان
Augustine O. Ifelebuegu, Esiwo O. Awotu-Ukiri, Stephen C. Theophilus, Andrew O. Arewa, Enobong Bassey,