Stochastic Optimization of a Natural Gas Liquefaction Process considering Seawater Temperature Variation based on Particle Swarm Optimization

Authors: Hye Jin Yang, Kyusuk Hwang, Chang Jun Lee

Journal: Industrial and Engineering Chemistry Research (I&EC)

DOI: https://doi.org/10.1021/acs.iecr.7b04546

Abstract:  This paper presents a systematic stochastic optimization method for a Dual Mixed Refrigerant (DMR) process modeled with the simulator Aspen HYSYS. First, a base case design and an objective function are developed based on the simulator and an equation. Next, decision variables among many process variables are determined by a sensitivity analysis. Among the process variables, seawater temperature variation, which has a large impact on operation cost, is considered as a random variable. Since it is not possible to use a deterministic optimization solver for the simulator, a Particle Swarm Optimization (PSO) technique, which employs a gradient-free optimization tool, is employed to solve a stochastic optimization problem. A case study shows the efficacy of the proposed algorithm. This method is general and can be applied to various processes modeled with commercial simulators.

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