The correlation between mixed refrigerant composition and ambient conditions in the PRICO LNG process

Integration of CO 2 cryogenic removal with a natural gas pressurized liquefaction process using gas expansion refrigeration

“…In general, optimization methods can be categorized into classical or intelligent type, which include the genetic algorithm [17,19,21,22,25,26], evolutionary search [20], Tabu search [18], and simulated annealing algorithm [38]. Because intelligent optimization methods are usually complex to understand and difficult to operate, a simple classical optimization method named sequential search algorithm is used in this study.…”

“…Gao et al [15], Yuan et al [16] and He and Ju [17] adopted optimization to minimize the energy consumption of expansionbased processes. Aspelund et al [18], Mokarizadeh Haghighi Shirazi and Mowal [19], Morin et al [20], and Xu et al [21] employed optimization to minimize the power required by single mixed refrigerant processes. Alabdulkarem et al [22], Wang et al [23], Hatcher et al [24], and Mortazavi et al [25] applied optimization to propane precooled mixed refrigerant processes to attain minimum energy consumption.…”

Knowledge inspired investigation of selected parameters on energy consumption in nitrogen single and dual expander processes of natural gas liquefaction

“…Hence, the nitrogen-based expander technology was proposed and attracted continued interest in meeting the growing demand of LNG from offshore fields. On the other hand, the high energy demand compared to the mixed refrigerant system (Chang et al, 2012;Xu et al, 2013) is a usual concern of expander technologies (Barclay & Denton, Oct, 2005). Optimization can be an effective solution for making nitrogen-based expander technologies competitive with traditional technologies (Li et al, 2012).…”

Optimization of modified single mixed refrigerant process of natural gas liquefaction using multivariate Coggin’s algorithm combined with process knowledge

“…A few of them applied mixed-integer non-linear programming (MINLP) model development and optimization because a large number of complicated thermodynamic equations causes major difficulties in finding a meaningful convergence solution (Duvedi and Achenie, 1997;Hasan et al, 2009;Nogal et al, 2008;Wang et al, 2012). An optimization methodology applied to MR systems should account for the fact that the performance of these cycles is difficult to control over a gradient of the optimized parameters; thus, the search methods are preferably gradient-free (Xu et al, 2013). Lee et al (2002) employed non-linear programming (NLP) to minimize the temperature difference between the real cold composite curve and its "pseudo-cold" curve; nevertheless, nonsimultaneous optimization of the decision variables renders the process ineffective for higher dimensional problems.…”

“…Aspelund et al (2010) combined Tabu Search (TS) and Nelder-Mead Downhill Simplex (NMDS) methods connected to Aspen Hysys to optimize PRICO process. Among the stochastic search methods applied for optimizing LNG systems, genetic algorithm (GA) becomes the most popular because they do not depend on starting point and many free source open codes are available (Austbø et al, 2014;Cammarata et al, 2001;Li et al, 2012;Xu et al, 2013). Alabdulkarem et al (2011) applied GA for the optimization of C3MR process which is conducted with different pinch temperatures.…”