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Surrogate model generation using self-optimizing variables
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2018-08-25 , DOI: 10.1016/j.compchemeng.2018.08.031
Julian Straus , Sigurd Skogestad

This paper presents the application of self-optimizing concepts for more efficient generation of steady-state surrogate models. Surrogate model generation generally has problems with a large number of independent variables resulting in a large sampling space. If the surrogate model is to be used for optimization, utilizing self-optimizing variables allows to map a close-to-optimal response surface, which reduces the model complexity. In particular, the mapped surface becomes much “flatter”, allowing for a simpler representation, for example, a linear map or neglecting the dependency of certain variables completely. The proposed method is studied using an ammonia reactor which for some disturbances shows limit-cycle behaviour and/or reactor extinction. Using self-optimizing variables, it is possible to reduce the number of manipulated variables by three and map a response surface close to the optimal response surface. With the original variables, the response surface would include also regions in which the reactor is extinct.



中文翻译:

使用自优化变量替代模型生成

本文介绍了自优化概念在更有效地生成稳态替代模型中的应用。替代模型的生成通常存在大量自变量的问题,从而导致较大的采样空间。如果要使用替代模型进行优化,则利用自优化变量可以映射接近最佳的响应面,从而降低了模型的复杂性。尤其是,映射的曲面变得更加“平坦”,从而允许更简单的表示,例如线性映射或完全忽略某些变量的依存关系。使用氨反应器对提出的方法进行了研究,该反应器对某些干扰显示出极限循环行为和/或反应器熄灭。使用自我优化的变量,可以将操纵变量的数量减少三倍,并绘制接近最佳响应面的响应面。在原始变量的情况下,响应表面还将包括反应堆已灭绝的区域。

更新日期:2018-08-25
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