基于花授粉算法的食物相关热力学系统液-液平衡建模参数辨识

A. Merzougui, N. Labed, A. Hasseine, A. Bonilla-Petriciolet, D. Laiadi, O. Bacha
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引用次数: 13

摘要

本文采用NRTL方程和UNIQUAC方程以及花授粉算法(Flower Pollination Algorithm, FPA)对22个与食品工业相关的三元和四元体系的液液平衡进行了建模。FPA是一种新兴的受自然启发的随机全局优化方法,它已被用于多组分混合物局部成分模型的LLE参数辨识。对FPA及其改进版本(MFPA)进行了评价,用于解决食品工业相关系统的LLE参数估计问题。分析了这些随机方法在不同数值情况下的数值性能。结果表明,对于局部成分模型的LLE参数识别,MFPA优于FPA和其他元启发式方法(如模拟退火、遗传算法和和声搜索)。在食品相关热力学系统LLE数据处理中,具有闭包方程的MFPA是确定NRTL和UNIQUAC模型最佳相互作用参数的可靠方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parameter Identification in Liquid-Liquid Equilibrium Modeling of Food-Related Thermodynamic Systems Using Flower Pollination Algorithms
In this paper, the liquid–liquid equilibrium of twenty two ternary and quaternary systems relevant for food industry was modeled using the NRTL and UNIQUAC equations and the Flower Pollination Algorithm (FPA). FPA is an emerging natureinspired stochastic global optimization method and it has been used for LLE parameter identification of local composition models in multicomponent mixtures. FPA and its modified version (MFPA) were assessed for solving LLE parameter estimation problems in several systems relevant for food industry. Thenumerical performance of these stochastic methods has been analyzed at different numerical scenarios with and without the application of closure equations. Results showed that MFPA outperformed FPA and other metaheuristics (e.g., Simulated Annealing, Genetic Algorithm and Harmony Search) for LLE parameter identification in local compositions models. MFPA with closure equations is a reliable approach for determining the best interaction parameter of NRTL and UNIQUAC models in the LLE data processing of food-related thermodynamic systems.
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