Multisynchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms

Batch synchronization has been widely misunderstood as being only needed when variable trajectories have uneven length. Batch data are actually considered not synchronized when the key process events do not occur at the same point of process evolution, irrespective of whether the batch duration is the same for all batches or not. Additionally, a single synchronization procedure is usually applied to all batches without taking into account the nature of asynchronism of each batch, and the presence of abnormalities. This strategy may distort the original trajectories and decrease the signal‐to‐noise ratio, affecting the subsequent multivariate analyses. The approach proposed in this paper, named multisynchro, overcomes these pitfalls in scenarios of multiple asynchronisms. The different types of asynchronisms are effectively detected by using the warping information derived from synchronization. Each set of batch trajectories is synchronized by appropriate synchronization procedures, which are automatically selected based on the nature of asynchronisms present in data. The novel approach also includes a procedure that performs abnormality detection and batch synchronization in an iterative manner. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisiae cultivation are used to illustrate the performance of the proposed approach in a context of multiple asynchronisms.Copyright © 2014 John Wiley & Sons, Ltd.

[1]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[2]  Claus A. Andersson,et al.  Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data , 2004 .

[3]  Junghui Chen,et al.  Post analysis on different operating time processes using orthonormal function approximation and multiway principal component analysis , 2000 .

[4]  A. Smilde,et al.  Multivariate statistical process control of batch processes based on three-way models , 2000 .

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

[6]  Theodora Kourti,et al.  Troubleshooting of an Industrial Batch Process Using Multivariate Methods , 2003 .

[7]  A. Çinar,et al.  Online batch/fed-batch process performance monitoring, quality prediction, and variable-contribution analysis for diagnosis , 2003 .

[8]  Nitin Kaistha,et al.  Extraction of Event Times in Batch Profiles for Time Synchronization and Quality Predictions , 2001 .

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

[10]  Theodora Kourti,et al.  Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications , 2003, Annu. Rev. Control..

[11]  A. Smilde,et al.  Dynamic time warping of spectroscopic BATCH data , 2003 .

[12]  Alberto Ferrer,et al.  Batch process diagnosis: PLS with variable selection versus block-wise PCR , 2004 .

[13]  José Camacho,et al.  Cross-validation in PCA models with the element-wise k-fold (ekf) algorithm: Practical aspects , 2014 .

[14]  C. Posten,et al.  Supervision of bioprocesses using a dynamic time warping algorithm , 1996 .

[15]  S. Jørgensen,et al.  A biochemically structured model for Saccharomyces cerevisiae. , 2001, Journal of biotechnology.

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

[17]  José Camacho,et al.  Bilinear modeling of batch processes. Part III: parameter stability , 2014 .

[18]  C. E. Schlags,et al.  Multivariate statistical analysis of an emulsion batch process , 1998 .

[19]  J. Macgregor,et al.  Monitoring batch processes using multiway principal component analysis , 1994 .

[20]  George C. Runger,et al.  Automated feature extraction from profiles with application to a batch fermentation process , 2012 .

[21]  Staffan Folestad,et al.  Real-time alignment of batch process data using COW for on-line process monitoring , 2006 .

[22]  J. Carstensen,et al.  Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping , 1998 .

[23]  Johan A. Westerhuis,et al.  Using warping information for batch process monitoring and fault classification , 2013 .

[24]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[25]  A. J. Morris,et al.  Comparison of Methods for Handling Unequal Length Batches , 1998 .

[26]  Hans-Georg Müller,et al.  Functional Data Analysis , 2016 .

[27]  Ali Cinar,et al.  Statistical monitoring of multistage, multiphase batch processes , 2002 .

[28]  J. Macgregor,et al.  Experiences with industrial applications of projection methods for multivariate statistical process control , 1996 .

[29]  T. Kourti,et al.  Multivariate Statistical Process Control and Process Control, Using Latent Variables , 2020, Comprehensive Chemometrics.

[30]  Sirish L. Shah,et al.  Monitoring Batch Processes Using Multivariate Statistical Tools: Extensions and Practical Issues , 1996 .

[32]  Rajagopalan Srinivasan,et al.  Off-line Temporal Signal Comparison Using Singular Points Augmented Time Warping , 2005 .

[33]  D. Massart,et al.  A comparison of two algorithms for warping of analytical signals , 2002 .

[34]  Alberto Ferrer,et al.  Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping , 2011 .

[35]  A. Ferrer,et al.  Effect of synchronization on bilinear batch process modeling , 2014 .

[36]  Svante Wold,et al.  Batch Process Modeling and MSPC , 2009 .

[37]  Theodora Kourti,et al.  Comparing alternative approaches for multivariate statistical analysis of batch process data , 1999 .

[38]  P. A. Taylor,et al.  Synchronization of batch trajectories using dynamic time warping , 1998 .

[39]  Yale Zhang,et al.  Integrated monitoring solution to start-up and run-time operations for continuous casting , 2003, Annu. Rev. Control..

[40]  Rajagopalan Srinivasan,et al.  Online fault diagnosis and state identification during process transitions using dynamic locus analysis , 2006 .

[41]  Geert Gins,et al.  Hybrid derivative dynamic time warping for online industrial batch-end quality estimation , 2012 .

[42]  Yang Zhang,et al.  A robust Dynamic Time Warping algorithm for batch trajectory synchronization , 2008, 2008 American Control Conference.

[43]  Theodora Kourti,et al.  Multivariate SPC for startups and grade transitions , 2002 .

[44]  A. Ferrer,et al.  Dealing with missing data in MSPC: several methods, different interpretations, some examples , 2002 .