Design optimization of single mixed refrigerant natural gas liquefaction process using the particle swarm paradigm with nonlinear constraints

Sequential coordinate random search for optimal operation of LNG (liquefied natural gas) plant

“…Nevertheless, faced with the abundance of local optima, the deterministic approach lands at local optimum [14]. In stochastic approaches, several tuning parameters make the success of the evolutionary approach dependent on the optimal tuning parameters [5].…”

Optimal synthesis of expansion liquefaction cycle for distributed-scale LNG (liquefied natural gas) plant

“…Yuan et al [21] designed a novel small-scale liquefaction process adopting single nitrogen expansion with carbon dioxide pre-cooling to liquefy stranded gas. Khan and Lee [22] applied particle swarm paradigm algorithm to optimize single mixed refrigerant natural gas liquefaction process. Lee et al [23] designed three different liquefaction processes for FLNG (floating liquefied natural gas) based on SMR (single mixed refrigerant) liquefaction process.…”

Evaluation of prediction models for the physical parameters in natural gas liquefaction processes

“…A comparison of the natural gas liquefaction processes with different precooling cycles was performed by Castillo et al (2013) using Aspen HYSYS with the Peng-Robinson thermodynamic fluid package. Using the PR equation to calculate the thermodynamic properties, Khan and Lee (2013) optimized a single mixed refrigerant natural gas liquefaction process using Honeywell UniSim Design and the particle swarm paradigm. With the PR equation of state calculating the thermodynamic properties, a typical single mixed refrigerant with low energy consumption was analyzed to determine the optimum operating conditions (Moein et al, 2015).…”