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首页> 外文期刊>Chemical Engineering Research & Design: Transactions of the Institution of Chemical Engineers >Optimal grade transition of a non-isothermal continuous reactor with multi-objective dynamic optimization approach
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Optimal grade transition of a non-isothermal continuous reactor with multi-objective dynamic optimization approach

机译:多目标动态优化方法的非等温连续反应器的最优等级转变

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Dynamic optimization (DO) is a useful tool for carrying out grade transitions in polymer industry. Most open literature studies on DO emphasize such grade transitions using single objective optimization. However, there are multiple criteria which must be met simultaneously for economic benefits. In this work, we solve a multi-objective DO problem for free-radical polymerization of methyl methacrylate in a non-isothermal continuous stirred tank reactor. The process objectives considered in the DO activity include minimization of off-spec, minimization of grade transition time, and minimization of the averaged feed flowrate. The manipulated variables considered for this problem are the initiator and coolant flowrates. The DO problem is solved using control vector parameterization (CVP) approach with first order interpolation. The solution of the aforementioned multi-objective DO problem is obtained in terms of a trade-off curve, pareto curve, using non-dominated sorting genetic algorithm (NSGA II). The three-dimensional pareto front is then projected to each of the three pairs of the objectives for better visualization and analysis. Furthermore, three representative pareto solution points, namely the two end points and a utopia point are further analysed for of each of the bi-objective pareto solution curves. (C) 2019 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
机译:动态优化(DO)是用于在聚合物工业中进行等级转换的有用工具。最开放的文献研究可以使用单个客观优化强调这些级转换。然而,有多种标准必须同时满足经济效益。在这项工作中,我们解决了非等温连续搅拌釜反应器中甲基丙烯酸甲酯的自由基聚合的多目标DO问题。在DO活性中考虑的过程目标包括最小化异常,最小化等级转变时间,并最小化平均进料流量。考虑该问题的操纵变量是发起者和冷却液流量。使用控制矢量参数化(CVP)方法来解决DO问题,使用一阶插值。在使用非主导的分类遗传算法(NSGA II)的权衡曲线,帕累托曲线(NSGA II)方面获得上述多目标确实问题的解决方案。然后将三维帕累托前线投射到三对象中的每一个以获得更好的可视化和分析。此外,对于每个双目标帕累托溶液曲线进一步分析了三个代表性帕累托溶液点,即两个终点和乌托邦点。 (c)2019化学工程师机构。 elsevier b.v出版。保留所有权利。

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