Knowledge-inspired operational reliability for optimal LNG production at the offshore site. (5th March 2019) - Ali, Wahid; Qyyum, Muhammad Abdul; Khan, Mohd Shariq; Duong, Pham Luu Trung; Lee, Moonyong
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.