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Dynamic multi-objective optimization of industrial radial-flow fixed-bed reactor of heavy paraffin dehydrogenation in LAB plant using NSGA-II method

机译:基于NSGA-II方法的LAB厂重质石蜡脱氢工业径向流固定床反应器动态多目标优化

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摘要

Due to the increasing global demand for olefin production, the development of catalytic dehydrogenation of paraffins to olefins has attracted considerable attention in recent years. Thus, a dynamic multiobjective optimization of heavy paraffin dehydrogenation over Pt-Sn-Al_2O_3 catalyst for production of olefin in an industrial radial-flow fixed-bed reactor (RF-FBR) using non-dominated sorting genetic algorithm-II (NSGA-II) is the subject of this study. The optimization problem involves two objective functions namely, maximization of olefin production rate and selectivity. Two points, A' and B', are considered on the obtained Pareto optimal fronts. The result reveals that the midpoints of A' B' have the approximate optimal values of both objective functions. Moreover, the 3D distributions of the optimized decision variables chromosomes are studied during 29 operating days. Concentrations of the chromosomes are increasing toward the lower limit of temperature and pressure, as well as higher limit of total molar flow rate. Optimization results give the optimum values of operating conditions, under which the maximum olefin production rate and selectivity can be obtained.
机译:由于全球对烯烃生产的需求不断增长,近年来链烷烃催化脱氢为烯烃的发展引起了相当大的关注。因此,使用非支配排序遗传算法-II(NSGA-II),对工业径向流固定床反应器(RF-FBR)中用于生产烯烃的Pt-Sn-Al_2O_3催化剂上的重链烷烃脱氢进行动态多目标优化。是本研究的主题。最优化问题涉及两个目标函数,即最大化烯烃生产速率和选择性。在获得的帕累托最优前沿上考虑了两个点A'和B'。结果表明,A'B'的中点具有两个目标函数的近似最佳值。此外,在29个工作日内研究了优化决策变量染色体的3D分布。染色体的浓度朝着温度和压力的下限以及总摩尔流速的上限增加。优化结果给出了最佳的操作条件值,在此条件下可以获得最大的烯烃生产速率和选择性。

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