A novel MDFA-MKECA method with application to industrial batch process monitoring

For the complex batch process with characteristics of unequal batch data length, a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis ( MDFA-MKECA ) in this paper. Combining the mechanistic knowledge, different mixed data features of each batch including statistical and thermodynamics entropy features, are extracted to finish data pre-processing. After that, MKECA is applied to reduce data dimensionality and finally establish a monitoring model. The proposed method is applied to a reheating furnace industry process, and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.

[1]  Kaixiang Peng,et al.  A Common and Individual Feature Extraction-Based Multimode Process Monitoring Method With Application to the Finishing Mill Process , 2018, IEEE Transactions on Industrial Informatics.

[2]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[3]  Nazatul Aini Abd Majid,et al.  Aluminium process fault detection by Multiway Principal Component Analysis , 2011 .

[4]  Alberto Ferrer,et al.  Multivariate SPC of a sequencing batch reactor for wastewater treatment , 2007 .

[5]  Jie Zhang,et al.  Multiway Interval Partial Least Squares for Batch Process Performance Monitoring , 2013 .

[6]  John F. MacGregor,et al.  Multivariate SPC charts for monitoring batch processes , 1995 .

[7]  Jie Yu,et al.  Quality relevant nonlinear batch process performance monitoring using a kernel based multiway non-Gaussian latent subspace projection approach , 2014 .

[8]  Chen Xiaobo,et al.  Kernel entropy component analysis based process monitoring method with process subsystem division , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[9]  Bin Wu,et al.  Total heat exchange factor based on non-gray radiation properties of gas in reheating furnace , 2009 .

[10]  Liang Zhao,et al.  External analysis and moving window LoOP based monitoring for a class of multi-mode timevarying process , 2017 .

[11]  H. Abdi,et al.  Principal component analysis , 2010 .

[12]  Hui Guo,et al.  On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis , 2013 .

[13]  Sang Woo Kim,et al.  An Estimation of a Billet Temperature during Reheating Furnace Operation , 2007 .

[14]  Zhihua Xiong,et al.  Nonlinear process modeling and optimization based on Multiway Kernel Partial Least Squares model , 2008, 2008 Winter Simulation Conference.

[15]  Theodora Kourti,et al.  Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start‐ups and grade transitions , 2003 .

[16]  Kaixiang Peng,et al.  A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches ☆ , 2015 .

[17]  Jin Wang,et al.  Statistics pattern analysis: A new process monitoring framework and its application to semiconductor batch processes , 2011 .

[18]  Svante Wold,et al.  Modelling and diagnostics of batch processes and analogous kinetic experiments , 1998 .

[19]  ChangKyoo Yoo,et al.  Fault detection of batch processes using multiway kernel principal component analysis , 2004, Comput. Chem. Eng..