Liquefied natural gas (LNG) plants require large amounts of energy to liquefy natural gas (NG). Therefore, many optimization studies have been conducted to minimize this energy consumption. Such studies have usually focused on optimization approaches to overcome the high nonlinearity of NG liquefaction process models. By contrast, decision variables and design bases have barely been investigated. In this study, an NG liquefaction process is modeled to perform sensitivity analysis of the design parameters and decision variables to determine their effects on the optimal operating conditions and process efficiency. A base case optimization is performed to investigate the convergence rate. Among 120 optimization runs, 57.5% are converged and 15.4% of the converged results show less than 0.1% difference in specific work compared to the best result. The effects of 11 decision variables and four design parameters are studied to obtain sensitivities. Among the decision variables, methane fraction and outlet temperature of a hot stream in an LNG heat exchanger strongly influence process efficiency. When changing the values of the design parameters within the ranges mentioned in the literature, specific work can vary from 724 kJ/kg LNG to 1509 kJ/kg LNG. Irreversibility in the coolers are the major reason to this variation.
Bibliographical noteFunding Information:
This work was supported by the ?Program of Fostering Innovative Global Leaders? of the Korea Institute for Advancement of Technology (KIAT) granted financial resources from the Ministry of Trade, Industry & Energy (MOTIE), Republic of Korea (P0008747).
This work was supported by the “Program of Fostering Innovative Global Leaders” of the Korea Institute for Advancement of Technology (KIAT) granted financial resources from the Ministry of Trade, Industry & Energy ( MOTIE ), Republic of Korea ( P0008747 ).
© 2020 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Building and Construction
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering