当前位置: X-MOL 学术Comput. Chem. Eng. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
On the estimation of high-dimensional surrogate models of steady-state of plant-wide processes characteristics
Computers & Chemical Engineering ( IF 4.3 ) Pub Date : 2018-02-21 , DOI: 10.1016/j.compchemeng.2018.02.014
Anh Phong Tran , Christos Georgakis

This work generalizes a preliminary investigation (Georgakis and Li, 2010) in which we examined the use of Response Surface Methodology (RSM) for the estimation of surrogate models as accurate approximations of high-dimensional knowledge-driven models. Three processes are examined with higher complexity than before, accounting for a much larger number of input and output variables. The surrogate models obtained are used to analyze several steady-state plant-wide characteristics. In all processes, the knowledge-driven model is a dynamic simulation with a plant-wide control structure of multiple SISO controllers. This type of controller proves to not be robust enough in its stability characteristics to enable substantial changes in the set-points. The net-elastic regularization is successfully used for the estimation of the metamodel parameters, avoiding overfitting and eliminating insignificant terms. Cross validation is used to compare and evaluate the relative accuracy of the quadratic and cubic models.



中文翻译:

全厂过程特性稳态高维替代模型的估计

这项工作概括了一项初步调查(Georgakis和Li,2010年),在该调查中,我们研究了使用响应面方法(RSM)来估计替代模型(作为高维知识驱动模型的精确近似)。与以前相比,以更高的复杂度检查了三个过程,这说明了输入和输出变量的数量要大得多。获得的替代模型用于分析工厂范围内的几种稳态特性。在所有过程中,知识驱动模型都是具有多个SISO控制器的全厂控制结构的动态仿真。事实证明,这种类型的控制器在稳定性方面不够鲁棒,无法实现设置点的实质性变化。净弹性正则化已成功用于估计元模型参数,避免过度拟合并消除无关紧要的条款。交叉验证用于比较和评估二次模型和三次模型的相对精度。

更新日期:2018-02-21
down
wechat
bug