Roadmap toward energy‐positive upfront nitrogen removal process in baseload LNG plant

Abstract

Summary The use of liquefied natural gas (LNG) has grown over the last decade owing to an increase in the global energy demand. This work presents a pioneering approach for rigorous simulation of an actual cold section of an LNG plant and provides a potential process optimization to achieve a higher production rate (PR) while reducing the specific power consumption (SPC). The concept of upfront nitrogen removal (UNrem) from the natural gas (NG) feed was introduced, and the impact on the PR and SPC was presented. We considered seven scenarios with varying percentages of UNrem and compared them with the conventional plant design (base case) while maintaining the product specifications (LNG high heating value [HHV] of 1105 Btu/Scf and NGL reid vapor pressure [RVP] of 2042 psia). The results show that UNrem of 87.5% from the NG feed can reduce the total power requirements by 0.24%, increase the production flow rate by 4.4%, and decrease the exergy losses by 0.23% compared with the base case. The UNrem can also have a significant improvement on the plant capacity without the need for structural modifications in the cold section. Future research should focus on the efficiency and feasibility of different UNrem processes as well as examine the use of a large‐scale system.

A critical review on the practical use of lithium cycle as an upfront nitrogen removal technology from natural gas

“…Mkacher et al 85 conducted a thorough experiment in which LNG optimization for cost PSO was performed. In the seven experimental scenarios there was a corresponding UNR of 12.5%, 25%, 37.5%, 50%, 62.5%, 75%, and 87.5%.…”

Freezing-Based Acid Gas Removal and Its Integration with the Cold Section in an LNG Plant

“…65,67−71 Several studies have shown that the PSO approach is particularly effective. 47,59,70,72 It keeps track of a growing swarm (population) of viable candidate solutions that improve with each iteration as more particles are added. PSO, unlike other heuristic approaches, does not require variable coding or decoding, as it has fewer user-defined tuning parameters, uses fewer variables, and produces faster and better results.…”

“…Simulation-based optimization (SBO) frameworks using metaheuristic solvers such as simulated annealing (SA), particle swarm optimization (PSO), or genetic algorithm (GA) are commonly used. ,− Several studies have shown that the PSO approach is particularly effective. ,,, It keeps track of a growing swarm (population) of viable candidate solutions that improve with each iteration as more particles are added. PSO, unlike other heuristic approaches, does not require variable coding or decoding, as it has fewer user-defined tuning parameters, uses fewer variables, and produces faster and better results. − …”