Normalized sensitivity analysis of LNG processes - Case studies: Cascade and single mixed refrigerant systems

The sensitivities of two liquefied natural gas (LNG) cycles, single mixed refrigerant (SMR) and propane pre-cooled mixed refrigerant (C3MR) to input variables are investigated and compared using normalized sensitivity analysis. The normalized sensitivity coefficients indicate the output of the cycle...

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Veröffentlicht in:Energy (Oxford) 2019-12, Vol.188, p.116068, Article 116068
Hauptverfasser: Sanavandi, Hamid, Mafi, Mostafa, Ziabasharhagh, Masoud
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Sprache:eng
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Zusammenfassung:The sensitivities of two liquefied natural gas (LNG) cycles, single mixed refrigerant (SMR) and propane pre-cooled mixed refrigerant (C3MR) to input variables are investigated and compared using normalized sensitivity analysis. The normalized sensitivity coefficients indicate the output of the cycles, specific energy consumption (SEC) is more sensitive to the inlet temperature and pressure of the mixed refrigerant compressors, the mixed refrigerant composition and the discharge temperature of the aftercoolers. The sensitivity analysis shows even perturbations below the accuracy level of ordinary measuring instruments are still large enough to break the 0.5–1.5 K minimum approach temperature (MAT) in cryogenic heat exchangers, commonly used in many earlier studies. Very few studies have addressed the optimality of the liquefaction process in an operation stability perspective. In this paper, sensitivity analysis is used to investigate the effect of disturbances and uncertainty on the stability of the cycles’ operation. 3 K MAT is suggested and tested by sensitivity analysis and verified by the economic assessment of the cycles. It is also shown that such perturbations cannot break 3 K MAT in cryogenic heat exchangers, hence the recent results are more reliable. •Identification of input parameters having largest effects on SMR and C3MR cycles.•Minuscule disturbances can cause error in 0.5–1.5 K pinch heat exchangers.•3 K minimum approach is an economically optimal value of case study cycles.
ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2019.116068