Ji-Hyun Hwang, Myung-Il Roh, Kyu-Yeul Lee, "Determination of the Optimal Operating Condition of the Dual Mixed Refrigerant Cycle for the LNG FPSO Topside Liquefaction Process", Computers and Chemical Engineering, Vol. 49, pp. 25-36, 2013.02.01
International Journal
2013.12.02 17:42
Ji-Hyun Hwang, Myung-Il Roh, Kyu-Yeul Lee, "Determination of the Optimal Operating Condition of the Dual Mixed Refrigerant Cycle for the LNG FPSO Topside Liquefaction Process", Computers and Chemical Engineering, Vol. 49, pp. 25-36, 2013.02.01
조회 수 7951
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Abstract | With the increased demand for natural gas, there has been an increase in the research on and development of liquefied-natural-gas floating, production, storage, and offloading unit (LNG FPSO) technologies for LNG service in place of onshore LNG plants. The dual mixed refrigerant (DMR) cycle, which precools natural gas with the mixed refrigerants of ethane, propane, butane, and methane and then liquefies the natural gas with another set of mixed refrigerants (nitrogen, methane, ethane, and propane), is well known for having the highest efficiency among the liquefaction cycles, and is being examined for possible application to LNG FPSO. In this study, the optimal operating conditions for the DMR cycle are determined by considering the power efficiency. For this, a mathematical model of the DMR cycle was formulated in this study by referring to the results of a past study that formulated a mathematical model of the single mixed refrigerant (SMR) cycle. Finally, the optimal operating conditions from the formulated mathematical model were obtained using a hybrid optimization method that consists of the genetic algorithm (GA) and sequential quadratic programming (SQP). As a result, the required power at the determined optimal operating conditions was decreased by 34.5% compared with the patent (Roberts & Agrawal, 2001), and by 1.2% compared with the corresponding value from the past relevant study (Venkatarathnam, 2008). |
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Publication Date | 2013-02-01 |
Role | Corresponding Author |
Category | SCI |
Impact Factor | 2.404 |
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