Stochastic Optimization of a Natural Gas Liquefaction Process Considering Seawater Temperature Variation Based on Particle Swarm Optimization
2018
Yang, Hye Jin | Hwang, Kyu Suk | Lee, Chang Jun
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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