Industrial & Engineering Chemistry Research, Vol.57, No.6, 2200-2207, 2018
Stochastic Optimization of a Natural Gas Liquefaction Process Considering Seawater Temperature Variation Based on Particle Swarm Optimization
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.