Study on Particle Swarm Optimization Variant and Simulated Annealing in Vapor Liquid Equilibrium Calculation

R. Oktavian, A. Wibowo, Z. Fitriah
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引用次数: 0

Abstract

Phase equilibrium calculation plays a major rule in optimization of separation process in chemical processing. Phase equilibrium calculation is still very challenging due to highly nonlinear and non-convex of mathematical models. Recently, stochastic optimization method has been widely used to solve those problems. One of the promising stochastic methods is Particle Swarm Optimization (PSO) due to its simplicity and robustness. This study presents the capability of particle swarm optimization for correlating isothermal vapor liquid equilibrium data of water with methanol and ethanol system by optimizing Wilson, Non-Random Two Liquids (NRTL), and Universal Quasi Chemical (UNIQUAC) activity coefficient model and also presents the comparison with bare-bones PSO (BBPSO) and simulated annealing (SA). Those three optimization methods were successfully tested and validated to model vapor liquid equilibrium calculation and were successfully applied to correlate vapor liquid equilibrium data for those types of systems with deviation less than 2%. In addition, BBPSO shows a consistency result and faster convergence among those three optimization methods. Keywords: Phase equilibrium, stochastic method, particle swarm optimization, simulated annealing and activity coefficient model
汽液平衡计算中粒子群优化变量和模拟退火的研究
在化工过程中,相平衡计算对分离过程的优化起着重要的作用。由于数学模型的高度非线性和非凸性,相平衡计算仍然非常具有挑战性。近年来,随机优化方法被广泛应用于解决这些问题。粒子群优化算法(PSO)具有简单、鲁棒性好等优点,是一种很有前途的随机方法。本研究通过优化Wilson、Non-Random Two liquid (NRTL)和Universal Quasi Chemical (UNIQUAC)活度系数模型,展示了粒子群优化在水与甲醇和乙醇体系等温汽液平衡数据关联中的能力,并与裸机粒子群优化(BBPSO)和模拟退火(SA)进行了比较。通过对这三种优化方法的测试和验证,成功地建立了汽液平衡计算模型,并成功地应用于偏差小于2%的系统汽液平衡数据的关联。此外,BBPSO在这三种优化方法之间表现出一致性和更快的收敛速度。关键词:相平衡,随机方法,粒子群优化,模拟退火,活度系数模型
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15
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2 weeks
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