Abstract
We show how a multiobjective bare-bones particle swarm optimization can be used for a process parameter tuning and performance enhancement of a natural gas sweetening unit. This has been made through maximization of hydrocarbon recovery and minimization of the total energy of the process as the two objectives of the optimization. A trade-off exists between these two objectives as illustrated by the Pareto front. This algorithm has been applied to a sweetening unit that uses the Benfield HiPure process. Detailed models of the natural gas unit are developed in ProMax process simulator and integrated to the multi-objective optimization developed in visual basic environment (VBA). In this study, the solvent circulation rates, stripper pressure and reboiler duties are considered as the decision variables while hydrogen sulfide and carbon dioxide concentrations in the sweetened gas are considered as process constraints. The upper and lower bounds of the decision variables are obtained through a parametric sensitivity analysis of the models. The Pareto sets show a significant improvement in hydrocarbon recovery and a decent reduction in the heat consumption of the process.
Highlights
As global energy demand rises, natural gas plays an important strategic role in world energy supply
We show how a multiobjective bare-bones particle swarm optimization can be used for a process parameter tuning and performance enhancement of a natural gas sweetening unit
In LNG plants, large amounts of carbon dioxide and sulfur compounds may affect the quality of LNG products or pose serious operational problems in the cryogenic columns [1, 2]
Summary
As global energy demand rises, natural gas plays an important strategic role in world energy supply. Gas sweetening processes use complex facilities whose design and operation basically depend on many parameters including gas composition, flow rate, circulation rates, absorber temperatures and stripper pressures Accurate modeling of such processes involving multicriteria decision making has inspired process engineers to acquire the best practices for process automation in the way to realize competitive returns on investment. The decreased computational time has contributed to routinely solving real-world problems involving large, realistic nonlinear models often encountered in process engineering Researchers such as Aroonwilas et al [7] and Park and Kang [8] simulated CO2 removal with aqueous amine solutions such as DEA, MDEA, and DGA in a gas sweetening process. Conclusions on this study including possible future work are discussed in Section 7 of this article
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