Optimal sequencing of conventional distillation column train for multicomponent separation system by evolutionary algorithm

IF 1 Q4 ENGINEERING, CHEMICAL
P. Giri, Y. Mahajan
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引用次数: 0

Abstract

Abstract The best structure of multicomponent separation techniques can be obtained using optimal distillation sequencing. Because distillation sequences contribute significantly to the fixed and operational cost of the entire chemical process, developing a systematic approach for choosing the most appropriate and economic distillation sequences becomes an important field of study. Due to its high dimensional space and combinatorial nature, synthesis of the optimal conventional distillation column sequence is a tough problem in the field of process plant development and optimization. A novel method for the synthesis of an optimal conventional distillation column sequence is suggested in this study. Genetic algorithm, an evolutionary algorithm is at the heart of the proposed method. The Total Annual Cost (TAC) is the main basis used to evaluate alternative configurations. To estimate the total cost of each sequence, rigorous methods are used to design all columns in the sequence. The proposed method’s performance and that of the conventional quantitative approach are compared using the results of a five component benchmark test problem used by researchers in this field. According to the comparison results, the suggested algorithm outclasses the other methods and is more adaptable than other existing approaches.
基于进化算法的多组分分离系统常规精馏塔列优化排序
摘要多组分分离技术的最佳结构可以通过优化蒸馏序列来获得。由于蒸馏序列对整个化学过程的固定成本和操作成本有很大贡献,因此开发一种系统的方法来选择最合适和最经济的蒸馏序列成为一个重要的研究领域。由于其高维空间和组合性质,合成最佳常规蒸馏塔序列是工艺装置开发和优化领域的一个难题。本研究提出了一种合成最佳常规蒸馏柱序列的新方法。遗传算法是一种进化算法,是所提出方法的核心。年度总成本(TAC)是用于评估替代配置的主要依据。为了估计每个序列的总成本,使用严格的方法来设计序列中的所有列。使用该领域研究人员使用的五分量基准测试问题的结果,比较了所提出的方法和传统定量方法的性能。根据比较结果,所提出的算法优于其他方法,并且比现有的其他方法更具适应性。
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来源期刊
Chemical Product and Process Modeling
Chemical Product and Process Modeling ENGINEERING, CHEMICAL-
CiteScore
2.10
自引率
11.10%
发文量
27
期刊介绍: Chemical Product and Process Modeling (CPPM) is a quarterly journal that publishes theoretical and applied research on product and process design modeling, simulation and optimization. Thanks to its international editorial board, the journal assembles the best papers from around the world on to cover the gap between product and process. The journal brings together chemical and process engineering researchers, practitioners, and software developers in a new forum for the international modeling and simulation community. Topics: equation oriented and modular simulation optimization technology for process and materials design, new modeling techniques shortcut modeling and design approaches performance of commercial and in-house simulation and optimization tools challenges faced in industrial product and process simulation and optimization computational fluid dynamics environmental process, food and pharmaceutical modeling topics drawn from the substantial areas of overlap between modeling and mathematics applied to chemical products and processes.
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