Knowledge-inspired operational reliability for optimal LNG production at the offshore site. (5th March 2019)

• Record Type: Journal Article
• Authors: Ali, Wahid; Qyyum, Muhammad Abdul; Khan, Mohd Shariq; Duong, Pham Luu Trung; Lee, Moonyong
• Is Part Of: Applied thermal engineering. Volume 150(2019)
• Page Start: 19
• Page End: 29
• Publication Date: 2019-03-05
• Subjects: Reliability enhancement -- Uncertainty quantification -- Natural gas liquefaction -- LNG -- SMR process; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2018.12.165
• Languages: English
• ISSNs: 1359-4311

Abstract

Graphical abstract: Highlights: Reliability of the SMR liquefaction process is effectively measured. A gPC based surrogate modeling approach is applied for uncertainty quantification. Sobol sensitivity indices are obtained directly from the surrogate model. Computational time is significantly reduced compared to MC/qMC approaches. Abstract: To develop a safe and profitable process, uncertainty quantification is necessary for a reliability, availability, and maintainability (RAM) analysis. The uncertainties of 3% in each key decision variables are propagated which could bring the system into an unreliable/risk region. Hence, in this study, uncertainty quantification (UQ) with simultaneous determination of sensitivity indices (SI) is proposed using generalized polynomial chaos (gPC) modeling approach. This approach reduces about 90% of the total computational time when compared with the conventional simulation approaches required for a complex first principle based model. Subsequently, a knowledge inspired reliability analysis is carried out using the uncertainty analysis (UA). By using the statistical properties of the process, for example, mean/optimal value at 50% failure give the bound between [0.7174, 0.9496] for LNG product stream. Further, it was found that LNG with 10% end flash gas (or 90% liquefaction rate) can be obtained with a failure probability of 14.43%. This value of reliability is promising for a given specified deviation; hence, the process could be assumed to be near to its reliable optimal operational region.