基于自适应采样算法的芳烃异构化代理模型

谢雨珩; 李智; 杨明磊; 杜文莉

doi:10.11949/0438-1157.20190359

您当前的位置：

首页 >

文章列表页 >

基于自适应采样算法的芳烃异构化代理模型

过程系统工程 | 更新时间：2026-07-24

- 基于自适应采样算法的芳烃异构化代理模型
- Surrogate model of aromatic isomerization process based on adaptive sampling algorithm
- 化工学报 2020年71卷第2期页码：688-697
- 作者机构：
  
  化学工程联合国家重点实验室，华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237
- 作者简介：
  
  谢雨珩(1994—)，男，硕士研究生，751619916@qq.com
  杜文莉(1974—)，女，博士，教授，wldu@ecust.edu.cn
- 基金信息：
  
  国家自然科学基金重大项目(61590923);国家自然科学基金重大项目(61890933);重点国际(地区)合作研究项目(61720106008);国家杰出青年科学基金项目(61725301);国家自然科学基金青年科学基金项目(61803158);国家自然科学基金面上项目(61873093)
- DOI：10.11949/0438-1157.20190359
  中图分类号： TQ 241.1
- 收稿：2019-04-08，
  
  修回：2019-06-19，
  
  网络首发：2020-08-14，
  
  纸质出版：2020-02-05
- 稿件说明：
移动端阅览
谢雨珩, 李智, 杨明磊, 杜文莉. 基于自适应采样算法的芳烃异构化代理模型[J]. 化工学报, 2020, 71(2): 688-697

XIE Yuheng, LI Zhi, YANG Minglei, DU Wenli. Surrogate model of aromatic isomerization process based on adaptive sampling algorithm[J]. CIESC Journal, 2020, 71(2): 688-697
谢雨珩, 李智, 杨明磊, 杜文莉. 基于自适应采样算法的芳烃异构化代理模型[J]. 化工学报, 2020, 71(2): 688-697 DOI： 10.11949/0438-1157.20190359.

XIE Yuheng, LI Zhi, YANG Minglei, DU Wenli. Surrogate model of aromatic isomerization process based on adaptive sampling algorithm[J]. CIESC Journal, 2020, 71(2): 688-697 DOI： 10.11949/0438-1157.20190359.

摘要

异构化是芳烃生产中的重要环节，提高异构化环节的建模和优化效率对工业生产有着重要意义。但是，直接使用机理模型的优化过程耗时较长，优化效率低。代理模型可以有效地对机理模型进行近似，而代理模型采样方法对模型精度有很大影响。提出了一种新的基于稀疏度和最邻近期望的自适应采样算法，该方法可以平衡全局搜索和局部搜索，通过求解优化问题找到反映函数关键信息的新采样点，再加入原始样本集中，使得代理模型精度不断提高。多个测试函数结果表明，相比于其他自适应采样算法，该算法能有效提升代理模型精度和建模效率。该算法在芳烃异构化环节代理模型中也得到了有效验证，与本文中其他算法对比，该算法模型误差减少5%以上，建模时间缩短30%以上。

Abstract

Isomerization is an important part in the production of aromatics. Improving the modeling and optimization efficiency of isomerization is of great significance to industrial production. However

the optimization process using the mechanism model directly takes a long time and the optimization efficiency is low. The surrogate model can effectively approximate the mechanism model and greatly reduce the computation time. The surrogate model sampling method has a great influence on the accuracy of the model. A new adaptive sampling algorithm based on sparsity and nearest neighbor expectation is proposed in this paper

which can balance global search and local search. By solving the optimization problem

we can find new sampling points that reflect the key information of the object function

and then add the new point to the sample set

so that the accuracy of the surrogate model is continuously improved. The results of multiple test functions show that compared with other adaptive sampling algorithms

the algorithm can effectively improve the accuracy and modeling efficiency of the surrogate model. The algorithm is also validated in the aromatic isomerization surrogate model. Compared with other algorithms

our algorithm model error is reduced by more than 5%

and the modeling time is reduced by more than 30%.

关键词

Keywords

references

徐欧官 , 傅永峰 , 陈祥华 . 工业异构化反应器建模及仿真 [J ] . 化工学报 , 2011 , 62 ( 8 ): 2298 - 2302 .

Xu O G , Fu Y F , Chen X H . Modeling and simulation for an industrial reactor on hydroisomerization of C8-aromatics [J ] . CIESC Journal , 2011 , 62 ( 8 ): 2298 - 2302 .

Lim D , Jin Y , Ong Y S , et al . Generalizing surrogate-assisted evolutionary computation [J ] . IEEE Transactions on Evolutionary Computation , 2010 , 14 ( 3 ): 329 - 355 .

Jin Y . A comprehensive survey of fitness approximation in evolutionary computation [J ] . Soft Computing , 2005 , 9 ( 1 ): 3 - 12 .

Box G E P , Wilson K B . On the experimental attainment of optimum conditions [J ] . Journal of the Royal Statistical Society , 1951 , 13 ( 1 ): 1 - 45 .

Lian Y S , Liou M S . Multiobjective optimization using coupled response surface model and evolutionary algorithm [J ] . AIAA Journal , 2005 , 43 ( 6 ): 1316 - 1325 .

Mullur A , Messac A . Extended radial basis functions: more flexible and effective metamodeling [J ] . AIAA Journal , 2005 , 43 ( 6 ): 1306 - 1315 .

Fang H B , Horstemeyer M F . Global response approximation with radial basis functions [J ] . Engineering Optimization , 2006 , 38 ( 4 ): 407 - 424 .

Krige D G . A statistical approach to some basic mine valuation problems on the witwatersrand [J ] . Journal of the Chemical , Metallurgical and Mining Engineering Society of South Africa , 1951 , 52 ( 6 ): 119 - 139 .

Cressie N . Spatial prediction and ordinary kriging [J ] . Mathematical Geology , 1989 , 21 ( 4 ): 493 - 494 .

Vapnik V , Golowich S E , Smola A . Support vector method for function approximation , regression estimation , and signal processing [C ] // Advances in Neural Information Processing Systems 9 , 1997 , 9 : 281 - 287 .

Friedman J H , Roosen C B . An introduction to multivariate adaptive regression splines [J ] . Statistical Methods in Medical Research , 1995 , 4 ( 3 ): 197 - 217 .

Friedman J H . Multivariate adaptive regression spline [J ] . The Annals of Statistics , 1991 , 19 ( 1 ): 1 - 67 .

Basheer I , Hajmeer M . Artificial neural networks: fundamentals, computing, design, and application [J ] . Journal of Microbiological Methods , 2000 , 43 ( 1 ): 3 - 31 .

Jin Y , Li J , Du W , et al . Adaptive sampling for surrogate modelling with artificial neural network and its application in an industrial cracking furnace [J ] . The Canadian Journal of Chemical Engineering , 2016 , 94 ( 2 ): 262 - 272 .

Zhou B D , Yao H L , Shi M H , et al . An new immune genetic algorithm based on uniform design sampling [J ] . Knowledge and Information Systems , 2012 , 31 ( 2 ): 389 - 403 .

Ye K Q . Orthogonal column Latin hypercubes and their application in computer experiments [J ] . Publications of the American Statistical Association , 1998 , 93 ( 444 ): 1430 - 1439 .

Wang G . Adaptive response surface method using inherited Latin hypercube design points [J ] . Journal of Mechanical Design , 2003 , 125 ( 2 ): 210 - 220 .

Jin Y . A framework for evolutionary optimization with approximate fitness functions [J ] . IEEE Transactions on Evolutionary Computation , 2002 , 6 ( 5 ): 481 - 494 .

Wang H , Jin Y , Doherty J . Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems [J ] . IEEE Trans . Cybern ., 2017 , 47 ( 9 ): 2664 - 2677 .

Eason J , Cremaschi S . Adaptive sequential sampling for surrogate model generation with artificial neural networks [J ] . Computers & Chemical Engineering , 2014 , 68 : 220 - 232 .

Wei X , Wu Y Z , Chen L P . A new sequential optimal sampling method for radial basis functions [J ] . Applied Mathematics and Computation , 2012 , 218 ( 19 ): 9635 - 9646 .

Garud S S , Karimi I A , Kraft M . Smart sampling algorithm for surrogate model development [J ] . Computers & Chemical Engineering , 2017 , 96 : 103 - 114 .

Müller J , Shoemaker C A . Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems [J ] . Journal of Global Optimization , 2014 , 60 ( 2 ): 123 - 144 .

Sacks J , Welch W J , Toby J , et al . Design and analysis of computer experiments [J ] . Statistical Science , 1989 , 4 ( 4 ): 409 - 423 .

王晓强 , 罗娜 , 叶贞成 , 等 . 基于Kriging的差分进化算法及其在苯乙烯流程优化中的应用 [J ] . 化工学报 , 2013 , 64 ( 12 ): 4563 - 4570 .

Wang X Q , Luo N , Ye Z C , et al . Differential evolution algorithm based on Kriging and its application in styrene plant optimization [J ] . CIESC Journal , 2013 , 64 ( 12 ): 4563 - 4570 .

段星辰 , 杜文莉 . 基于bootstrap GEI算法的碳二加氢反应器代理模型 [J ] . 化工学报 , 2015 , 66 ( 12 ): 4904 - 4909 .

Duan X C , Du W L . Surrogate model of acetylene hydrogenation reactor based on bootstrap GEI algorithm [J ] . CIESC Journal , 2015 , 66 ( 12 ): 4904 - 4909 .

Xiao N C , Zuo M J , Guo W . Efficient reliability analysis based on adaptive sequential sampling design and cross-validation [J ] . Applied Mathematical Modelling , 2018 , 58 : 404 - 420 .

Gong W , Duan Q . An Adaptive Surrogate modeling-based sampling strategy for parameter optimization and distribution estimation (ASMO-PODE) [J ] . Environmental Modelling & Software , 2017 , 95 : 61 - 75 .

韩忠华 . Kriging模型及代理优化算法研究进展 [J ] . 航空学报 , 2016 , 37 ( 11 ): 3197 - 3225 .

Han Z H . Kriging surrogate model and its application to design optimization: a review of recent progress [J ] . Acta Aeronautica Et Astronautica Sinica , 2016 , 37 ( 11 ): 3197 - 3225 .

Jin Y . Surrogate-assisted evolutionary computation: recent advances and future challenges [J ] . Swarm & Evolutionary Computation , 2011 , 1 ( 2 ): 61 - 70 .

Hooke R , Jeeves T A . “Direct search” solution of numerical and statistical problems [J ] . Journal of the ACM , 1961 , 8 ( 2 ): 212 - 229 .

张剑超 , 杜文莉 , 覃水 . 基于新型自适应采样算法的催化重整过程代理模型 [J ] . 华东理工大学学报(自然科学版) ， 2018 ， doi: 10.14135/j.cnki.1006-3080.20180915001 http://dx.doi.org/10.14135/j.cnki.1006-3080.20180915001 .

Zhang J C , Du W L , Qin S . Surrogate model of catalytic reforming process based on a new adaptive sampling algorithm [J ] . Journal of East China University of Science and Technology (Natural Science Edition) ， 2018 ， doi: 10.14135/j.cnki.1006-3080.20180915001 http://dx.doi.org/10.14135/j.cnki.1006-3080.20180915001 .

刘祥荣 . 二甲苯异构化反应器的模拟研究 [D ] . 北京 : 北京化工大学 , 2010 .

Liu X R . The modeling and dynamic simulation of the xylene isomerization reactor [D ] . Beijing : Beijing University of Chemical Technology , 2010 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于CNN-LSTM的换热器污垢因子预测研究

考虑贫流股旁路的热-质交换网络同步优化

基于人工智能的微分散基础研究

基于改进深度强化学习的乙烯裂解炉操作优化

原油作业过程优化的研究进展