基于Ms-LWPLS的化工过程网络化性能分级评估方法

曹晨鑫; 杜玉鹏; 王昕; 王振雷

doi:10.11949/j.issn.0438-1157.20181369

您当前的位置：

首页 >

文章列表页 >

基于Ms-LWPLS的化工过程网络化性能分级评估方法

过程系统工程 | 更新时间：2020-06-24

- 基于Ms-LWPLS的化工过程网络化性能分级评估方法
- Networked grading performance assessment method of chemical process based on Ms-LWPLS
- 化工学报 2019年第S1期页码：141-149
- 作者机构：
  
  1.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
  2.上海交通大学电工电子实验教学中心，上海 200240
- 作者简介：
  
  曹晨鑫（1994—），男，硕士研究生，296883892@qq.com
  王昕（1972—），男，博士，副教授，wangxin26@sjtu.edu.cn
  王振雷（1975—），男，博士，教授，wangzhen_1@ecust.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61673268);国家自然科学基金重点项目(61533003);国家自然科学基金重大项目(61590922);中央高校基本科研业务费专项资金(222201814043)
- DOI：10.11949/j.issn.0438-1157.20181369
  中图分类号： TP 273
- 收稿：2018-11-18，
  
  修回：2018-12-25，
- 稿件说明：
移动端阅览
曹晨鑫, 杜玉鹏, 王昕, 王振雷. 基于Ms-LWPLS的化工过程网络化性能分级评估方法[J]. 化工学报, 2019, 70(S1): 141-149

CAO Chenxin, DU Yupeng, WANG Xin, WANG Zhenlei. Networked grading performance assessment method of chemical process based on Ms-LWPLS[J]. CIESC Journal, 2019, 70(S1): 141-149
曹晨鑫, 杜玉鹏, 王昕, 王振雷. 基于Ms-LWPLS的化工过程网络化性能分级评估方法[J]. 化工学报, 2019, 70(S1): 141-149 DOI： 10.11949/j.issn.0438-1157.20181369.

CAO Chenxin, DU Yupeng, WANG Xin, WANG Zhenlei. Networked grading performance assessment method of chemical process based on Ms-LWPLS[J]. CIESC Journal, 2019, 70(S1): 141-149 DOI： 10.11949/j.issn.0438-1157.20181369.

摘要

针对化工过程输入输出数据间非线性关系问题，提出一种基于多数据空间局部加权潜结构映射(multi-space locally weighted projection to latent structures，Ms-LWPLS)的网络化性能分级评估方法。该方法将历史数据分成不同性能等级的集合，利用Ms-LWPLS方法提取不同性能等级训练数据的过程变化，获得训练数据与性能等级标签之间的非线性映射结构，实现输入数据与性能等级之间的网络化“离线建模”。得到模型后，以数据滑动时间窗为评估单元，将滑动窗口数据输入到训练好的神经网络模型中，根据网络输出划分过程当前性能等级，并构造过渡性能系数，将稳态性能等级和过渡性能等级进行识别和区分。最后，将该方法应用到乙烯裂解过程在线性能评估中，说明此性能评估方法的有效性和准确性。

Abstract

A networked performance assessment method based on multi-space locally weighted projection to latent structures (Ms-LWPLS) is proposed to solve the nonlinear relationship between input and output data of chemical process. This method divides the historical training datasets into different sets of performance grades

extracting the process changes of different performance grade of training datasets by Ms-LWPLS method. This method obtains the latent structures accurately by matching the training datasets and performance grade labels through the non-linear networked structure. The“off-line modeling”is achieved by the trained neural network. With the model obtained

the sliding window is used as the assessment unit

working as the input data into the trained neural network model. The current performance grade is identified according to the network output and the transition performance coefficient is constructed. The steady-state performance grades and the transition performance grades are recognized and distinguished. Finally

the method is applied to the online performance assessment of ethylene cracking process

which shows the effectiveness and accuracy of the performance assessment method proposed.

关键词

Keywords

references

Harris T J . Assessment of control loop performance [J]. Canadian Journal of Chemical Engineering , 1989 , 67 ( 5 ): 856 - 861 .

Harris T J , Boudreau F , Macgregor J F . Performance assessment of multivariable feedback controllers [J]. Automatica , 1996 , 32 ( 11 ): 1505 - 1518 .

Huang B , Kwok E K , Shah S L . Good, bad or optimal? Performance assessment of multivariable processes [J]. Automatica , 1997 , 33 ( 6 ): 1175 - 1183 .

Huang B , Shah S L . Practical issues in multivariable feedback control performance assessment [J]. Journal of Process Control , 1997 , 8 ( 5 ): 421 - 430 .

Huang B , Ding S X , Thornhill N . Practical solutions to multivariate feedback control performance assessment problem: reduced a-priori knowledge of interactor matrix [J]. Journal of Process Control , 2005 , 15 ( 5 ): 573 - 583 .

Huang B , Ding S X , Thornhill N . Alternative solutions to multi-variate control performance assessment problems [J]. Journal of Process Control , 2006 , 16 ( 5 ): 457 - 471 .

Yu J , Qin S J . Statistical MIMO controller performance monitoring(Ⅰ): Data-driven covariance benchmark [J]. Journal of Process Control , 2008 , 18 ( 3 ): 277 - 296 .

Yu J , Qin S J . Statistical MIMO controller performance monitoring(Ⅱ): Performance diagnosis [J]. Journal of Process Control , 2008 , 18 ( 3 ): 297 - 319 .

Qin S J . Survey on data-driven industrial process monitoring and diagnosis [J]. Annual Reviews in Control , 2012 , 36 ( 2 ): 220 - 234 .

赵春晖 , 王福利 , 姚远 , 等 . 基于时段的间歇过程统计建模、在线监测及质量预报 [J]. 自动化学报 , 2010 , 36 ( 3 ): 366 - 374 .

Zhao C H , Wang F L , Yao Y , et al . Phase-based statistical modeling, online monitoring and quality prediction for batch processes [J]. Acta Automatica Sinica , 2010 , 36 ( 3 ): 366 - 374 .

Yan L , Wang F , Chang Y . Online fuzzy assessment of operating performance and cause identification of nonoptimal grades for industrial processes [J]. Industrial & Engineering Chemistry Research , 2013 , 52 ( 50 ): 18022 - 18030 .

Liu Y , Chang Y , Wang F . Online process operating performance assessment and nonoptimal cause identification for industrial processes [J]. Journal of Process Control , 2014 , 24 ( 10 ): 1548 - 1555 .

Liu Y , Wang F , Chang Y . Operating optimality assessment based on optimality related variations and nonoptimal cause identification for industrial processes [J]. Journal of Process Control , 2016 , 39 : 11 - 20 .

Zhao C , Gao F , Niu D , et al . A two-step basis vector extraction strategy for multiset variable correlation analysis [J]. Chemometrics & Intelligent Laboratory Systems , 2011 , 107 ( 1 ): 147 - 154 .

Li G , Qin S J , Zhou D . Geometric properties of partial least squares for process monitoring [J]. Automatica , 2010 , 46 ( 1 ): 204 - 210 .

Zhou D H , Li G , Qin S J . Total projection to latent structures for process monitoring [J]. AIChE Journal , 2010 , 56 ( 1 ): 168 - 178 .

赵小强 , 薛永飞 . 基于核 T-PLS 的化工过程故障检测算法 [J]. 化工学报 , 2013 , 64 ( 12 ): 4608 - 4614 .

Zhao X Q , Xue Y F . Fault detection algorithm for chemical process based on kernel T-PLS [J]. CIESC Journal , 2013 , 64 ( 12 ): 4608 - 4614 .

Liu Y , Chang Y , Wang F . Nonlinear dynamic quality-related process monitoring based on dynamic total kernel PLS [C] //Intelligent Control and Automation . IEEE , 2015 : 1360 - 1365 .

Kaneko H , Funatsu K . Ensemble locally weighted partial least squares as a just-in-time modeling method [J]. AIChE Journal , 2016 , 62 ( 3 ): 717 - 725 .

Hazama K , Kano M . Covariance-based locally weighted partial least squares for high-performance adaptive modeling [J]. Chemometrics & Intelligent Laboratory Systems , 2015 , 146 : 55 - 62 .

袁小锋 , 葛志强 , 宋执环 . 基于时间差分和局部加权偏最小二乘算法的过程自适应软测量建模 [J]. 化工学报 , 2016 , 67 ( 3 ): 724 - 728 .

Yuan X F , Ge Z Q , Song Z H . Adaptive soft sensor based on time difference model and locally weighted partial least squares regression [J]. CIESC Journal , 2016 , 67 ( 3 ): 724 - 728 .

Wang G , Yin S , Kaynak O . An LWPR-based data-driven fault detection approach for nonlinear process monitoring [J]. IEEE Transactions on Industrial Informatics , 2014 , 10 ( 4 ): 2016 - 2023 .

Yin S , Xie X , Sun W . A nonlinear process monitoring approach with locally weighted learning of available data [J]. IEEE Transactions on Industrial Electronics , 2017 , 64 ( 2 ): 1507 - 1516 .

Uchimaru T , Kano M . Sparse sample regression based just-in-time modeling (SSR-JIT): beyond locally weighted approach [J]. IFAC Papers OnLine , 2016 , 49 ( 7 ): 502 - 507 .

Liu J , Tao L , Jie Z . Window-based step-wise sequential phase partition for nonlinear batch process monitoring [J]. Industrial & Engineering Chemistry Research , 2016 , 55 ( 34 ): 9229 - 9243 .

刘学彦 , 王昕 , 王振雷 . 带遗忘因子的线性回归性能评估算法及应用 [J]. 控制工程 , 2014 , 21 ( 6 ): 867 - 872 .

Liu X Y , Wang X , Wang Z L . Performance assessment algorithm of improved linear regression with forgetting factor (ILR) and its application [J]. Control Engineering of China , 2014 , 21 ( 6 ): 867 - 872 .

张壤文 , 田学民 . 带变遗忘因子的自适应子空间预测控制器设计 [J]. 化工学报 , 2016 , 67 ( 3 ): 858 - 864 .

Zhang R W , Tian X M . Design of adaptive subspace predictive controller with variable forgetting factor [J]. CIESC Journal , 2016 , 67 ( 3 ): 858 - 864 .

Kerezsi J . Computer simulation of an industrial ethane-cracking furnace operation [C] //International Youth Conference on Energy . IEEE , 2013 : 1 - 5 .

李平 , 李奇安 . 乙烯裂解炉先进控制系统开发与应用 [J]. 化工学报 , 2011 , 62 ( 8 ): 2216 - 2220 .

Li P , Li Q A . Development and application of advanced process control system for ethylene cracking heaters [J]. CIESC Journal , 2011 , 62 ( 8 ): 2216 - 2220 .

江伟 , 王昕 , 王振雷 . 基于 LTSA 和 MICA 与 PCA 联合指标的过程监控方法及应用 [J]. 化工学报 , 2015 , 66 ( 12 ): 4895 - 4903 .

Jiang W , Wang X , Wang Z L . LTSA and combined index based MICA and PCA process monitoring and application [J]. CIESC Journal , 2015 , 66 ( 12 ): 4895 - 4903 .

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Ms-NIPLS-GPR的化工过程性能等级评估方法

基于相似度网格模型的在线经济性能分级评估方法

基于时序-空间双流卷积神经网络的炼化污水厂进水COD预测模型

基于SlowFast-Transformer的化工过程故障检测与风险预警

炼化变工况下多变量过程的智能PID协调整定