Bilinear modeling of batch processes. Part III: Parameter Stability

A paramount aspect in the development of a model for a monitoring system is the so-called parameter stability. This is inversely related to the uncertainty, i.e., the variance in the parameters estimates. Noise affects the performance of the monitoring system, reducing its fault detection capability. Low parameters uncertainty is desired to ensure a reduced amount of noise in the model. Nonetheless, there is no sound study on the parameter stability in batch multivariate statistical process control (BMSPC). The aim of this paper is to investigate the parameter stability associated to the most used synchronization and principal component analysis-based BMSPC methods. The synchronization methods included in this study are the following: indicator variable, dynamic time warping, relaxed greedy time warping, and time linear expanding/compressing-based. In addition, different arrangements of the three-way batch data into two-way matrices are considered, namely single-model, K-models, and hierarchicalmodel approaches. Results are discussed in connection with previous conclusions in the first two papers of the series.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. Authors also acknowledge the anonymous reviewers for their comments to improve the article.González Martínez, JM.; Camacho Páez, J.; Ferrer, A. (2014). Bilinear modeling of batch processes. Part III: Parameter Stability. Journal of Chemometrics. 28(1):10-27. https://doi.org/10.1002/cem.2562S1027281Process analysis and abnormal situation detection: from theory to practice. (2002). IEEE Control Systems, 22(5), 10-25. doi:10.1109/mcs.2002.1035214Statistical monitoring of multistage, multiphase batch processes. (2002). IEEE Control Systems, 22(5), 40-52. doi:10.1109/mcs.2002.1035216Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3Camacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part I: theoretical discussion. Journal of Chemometrics, 22(5), 299-308. doi:10.1002/cem.1113Camacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22(10), 533-547. doi:10.1002/cem.1179González-Martínez J Vitale R de Noord O Ferrer A Does synchronization matter in bilinear batch process monitoring?García-Muñoz, S., Kourti, T., MacGregor, J. F., Mateos, A. G., & Murphy, G. (2003). Troubleshooting of an Industrial Batch Process Using Multivariate Methods. Industrial & Engineering Chemistry Research, 42(15), 3592-3601. doi:10.1021/ie0300023Zarzo, M., & Ferrer, A. (2004). Batch process diagnosis: PLS with variable selection versus block-wise PCR. Chemometrics and Intelligent Laboratory Systems, 73(1), 15-27. doi:10.1016/j.chemolab.2003.11.009Wallace D Prosensus multivariate v12. 02 2010Louwerse, D. J., Tates, A. A., Smilde, A. K., Koot, G. L. M., & Berndt, H. (1999). PLS discriminant analysis with contribution plots to determine differences between parallel batch reactors in the process industry. Chemometrics and Intelligent Laboratory Systems, 46(2), 197-206. doi:10.1016/s0169-7439(98)00185-3Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375. doi:10.1002/aic.690400809Kaistha, N., & Moore, C. F. (2001). Extraction of Event Times in Batch Profiles for Time Synchronization and Quality Predictions. Industrial & Engineering Chemistry Research, 40(1), 252-260. doi:10.1021/ie990937cRamsay, J. O., & Silverman, B. W. (1997). Functional Data Analysis. Springer Series in Statistics. doi:10.1007/978-1-4757-7107-7Andersen, S. W., & Runger, G. C. (2012). Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(2), 327-344. doi:10.1111/j.1467-9876.2011.01032.xKassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412González-Martínez, J. M., Ferrer, A., & Westerhuis, J. A. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems, 105(2), 195-206. doi:10.1016/j.chemolab.2011.01.003Zhang Y Edgar TF A robust dynamic time warping algorithm for batch trajectory synchronization 2008 2864 2860Gins, G., Van den Kerkhof, P., & Van Impe, J. F. M. (2012). Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation. Industrial & Engineering Chemistry Research, 51(17), 6071-6084. doi:10.1021/ie2019068Gurden, S. P., Westerhuis, J. A., Bijlsma, S., & Smilde, A. K. (2000). Modelling of spectroscopic batch process data using grey models to incorporate external information. Journal of Chemometrics, 15(2), 101-121. doi:10.1002/1099-128x(200102)15:23.0.co;2-vKourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778Westerhuis, J. A., Kourti, T., & MacGregor, J. F. (1999). Comparing alternative approaches for multivariate statistical analysis of batch process data. Journal of Chemometrics, 13(3-4), 397-413. doi:10.1002/(sici)1099-128x(199905/08)13:3/43.0.co;2-iNomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888Wold, S., Kettaneh, N., Fridén, H., & Holmberg, A. (1998). Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 331-340. doi:10.1016/s0169-7439(98)00162-2Chen, J., & Liu, K.-C. (2002). On-line batch process monitoring using dynamic PCA and dynamic PLS models. Chemical Engineering Science, 57(1), 63-75. doi:10.1016/s0009-2509(01)00366-9Ramaker, H.-J., van Sprang, E. N. M., Westerhuis, J. A., & Smilde, A. K. (2005). Fault detection properties of global, local and time evolving models for batch process monitoring. Journal of Process Control, 15(7), 799-805. doi:10.1016/j.jprocont.2005.02.001Lennox, B., Montague, G. A., Hiden, H. G., Kornfeld, G., & Goulding, P. R. (2001). Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering, 74(2), 125-135. doi:10.1002/bit.1102Ündey, C., Ertunç, S., & Çınar, A. (2003). Online Batch/Fed-Batch Process Performance Monitoring, Quality Prediction, and Variable-Contribution Analysis for Diagnosis. Industrial & Engineering Chemistry Research, 42(20), 4645-4658. doi:10.1021/ie0208218Camacho, J., & Picó, J. (2006). Multi-phase principal component analysis for batch processes modelling. Chemometrics and Intelligent Laboratory Systems, 81(2), 127-136. doi:10.1016/j.chemolab.2005.11.003Rännar, S., MacGregor, J. F., & Wold, S. (1998). Adaptive batch monitoring using hierarchical PCA. Chemometrics and Intelligent Laboratory Systems, 41(1), 73-81. doi:10.1016/s0169-7439(98)00024-0Camacho, J., Picó, J., & Ferrer, A. (2009). The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter? Analytica Chimica Acta, 642(1-2), 59-68. doi:10.1016/j.aca.2009.02.001Van Sprang, E. N. ., Ramaker, H.-J., Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2002). Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science, 57(18), 3979-3991. doi:10.1016/s0009-2509(02)00338-xLei, F., Rotbøll, M., & Jørgensen, S. B. (2001). A biochemically structured model for Saccharomyces cerevisiae. Journal of Biotechnology, 88(3), 205-221. doi:10.1016/s0168-1656(01)00269-3Camacho J González-Martínez J Ferrer A Multi-phase (MP) toolbox 2013 http://mseg.webs.upv.es/Software.htmlCamacho, J., Picó, J., & Ferrer, A. (2008). Multi-phase analysis framework for handling batch process data. Journal of Chemometrics, 22(11-12), 632-643. doi:10.1002/cem.115

Multi-synchro: 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.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02. Part of this research work was carried out during an internship of the corresponding author at Shell Global Solutions International B.V. (Amsterdam, The Netherlands). The authors also thank the anonymous referees for their comments, which greatly helped to improve the text.González Martínez, JM.; De Noord, O.; Ferrer, A. (2014). 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Industrial & Engineering Chemistry Research, 42(20), 4645-4658. doi:10.1021/ie0208218Neogi, D., & Schlags, C. E. (1998). Multivariate Statistical Analysis of an Emulsion Batch Process. Industrial & Engineering Chemistry Research, 37(10), 3971-3979. doi:10.1021/ie980243oKourti, T., Lee, J., & Macgregor, J. F. (1996). Experiences with industrial applications of projection methods for multivariate statistical process control. Computers & Chemical Engineering, 20, S745-S750. doi:10.1016/0098-1354(96)00132-9Duchesne, C., Kourti, T., & MacGregor, J. F. (2002). Multivariate SPC for startups and grade transitions. AIChE Journal, 48(12), 2890-2901. doi:10.1002/aic.690481216Zhang, Y., Dudzic, M., & Vaculik, V. (2003). Integrated monitoring solution to start-up and run-time operations for continuous casting. 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Springer Series in Statistics. doi:10.1007/978-1-4757-7107-7Statistical monitoring of multistage, multiphase batch processes. (2002). IEEE Control Systems, 22(5), 40-52. doi:10.1109/mcs.2002.1035216Andersen, S. W., & Runger, G. C. (2012). Automated feature extraction from profiles with application to a batch fermentation process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(2), 327-344. doi:10.1111/j.1467-9876.2011.01032.xSrinivasan, R., & Qian, M. S. (2005). Off-line Temporal Signal Comparison Using Singular Points Augmented Time Warping. Industrial & Engineering Chemistry Research, 44(13), 4697-4716. doi:10.1021/ie049528tSrinivasan, R., & Sheng Qian, M. (2006). Online fault diagnosis and state identification during process transitions using dynamic locus analysis. Chemical Engineering Science, 61(18), 6109-6132. doi:10.1016/j.ces.2006.05.037Srinivasan, R., & Qian, M. (2007). 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A comparison of two algorithms for warping of analytical signals. Analytica Chimica Acta, 456(1), 77-92. doi:10.1016/s0003-2670(02)00008-9Tomasi, G., van den Berg, F., & Andersson, C. (2004). Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. Journal of Chemometrics, 18(5), 231-241. doi:10.1002/cem.859Kassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412Gollmer, K., & Posten, C. (1996). Supervision of bioprocesses using a dynamic time warping algorithm. Control Engineering Practice, 4(9), 1287-1295. doi:10.1016/0967-0661(96)00136-0Ramaker, H.-J., van Sprang, E. N. M., Westerhuis, J. A., & Smilde, A. K. (2003). Dynamic time warping of spectroscopic BATCH data. Analytica Chimica Acta, 498(1-2), 133-153. doi:10.1016/j.aca.2003.08.045Fransson, M., & Folestad, S. (2006). Real-time alignment of batch process data using COW for on-line process monitoring. Chemometrics and Intelligent Laboratory Systems, 84(1-2), 56-61. doi:10.1016/j.chemolab.2006.04.020González-Martínez, J. M., Ferrer, A., & Westerhuis, J. A. (2011). Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping. Chemometrics and Intelligent Laboratory Systems, 105(2), 195-206. doi:10.1016/j.chemolab.2011.01.003Gins, G., Van den Kerkhof, P., & Van Impe, J. F. M. (2012). Hybrid Derivative Dynamic Time Warping for Online Industrial Batch-End Quality Estimation. Industrial & Engineering Chemistry Research, 51(17), 6071-6084. doi:10.1021/ie2019068Zhang Y Edgar TF A robust dynamic time warping algorithm for batch trajectory synchronization 2008 2864 2869González-Martínez, J. M., Westerhuis, J. A., & Ferrer, A. (2013). Using warping information for batch process monitoring and fault classification. 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Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

MVBatch: A matlab toolbox for batch process modeling and monitoring

[EN] A novel user-friendly graphical interface for process understanding, monitoring and troubleshooting has been developed as a freely available MATLAB toolbox, called the MultiVariate Batch (MVBatch) Toolbox. The main contribution of this software package is the integration of recent developments in Principal Component Analysis (PCA) based Batch Multivariate Statistical Process Monitoring (BMSPM) that overcome modeling problems such as missing data, different speed of process evolution and length of batch trajectories, and multiple stages. An interactive user interface is provided, which aims to guide users in handling batch data through the main BMSPM steps: data alignment, data modeling, and the development of monitoring schemes. In addition, a small-scale non-linear dynamic simulator of the fermentation process of the Saccharomyces cerevisiae cultivation is available to generate realistic batch data under normal and abnormal operating conditions. This generator of synthetic data can be used for teaching purposes or as a benchmark to illustrate and compare the performance of new methods with sound techniques published in the field of BMSPM.This work is partially supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds through the projects DPI2017-82896-C2-1-R and TIN2017-83494-R. Authors also acknowledge the volunteers to test MVBatch and report their impressions for this software tutorial.González Martínez, JM.; Camacho Paez, J.; Ferrer, A. (2018). MVBatch: A matlab toolbox for batch process modeling and monitoring. Chemometrics and Intelligent Laboratory Systems. 183:122-133. https://doi.org/10.1016/j.chemolab.2018.11.001S12213318

ADVANCES ON BILINEAR MODELING OF BIOCHEMICAL BATCH PROCESSES

[EN] This thesis is aimed to study the implications of the statistical modeling approaches proposed for the bilinear modeling of batch processes, develop new techniques to overcome some of the problems that have not been yet solved and apply them to data of biochemical processes. The study, discussion and development of the new methods revolve around the four steps of the modeling cycle, from the alignment, preprocessing and calibration of batch data to the monitoring of batches trajectories. Special attention is given to the problem of the batch synchronization, and its effect on the modeling from different angles. The manuscript has been divided into four blocks. First, a state-of- the-art of the latent structures based-models in continuous and batch processes and traditional univariate and multivariate statistical process control systems is carried out. The second block of the thesis is devoted to the preprocessing of batch data, in particular, to the equalization and synchronization of batch trajectories. The first section addresses the problem of the lack of equalization in the variable trajectories. The different types of unequalization scenarios that practitioners might finnd in batch processes are discussed and the solutions to equalize batch data are introduced. In the second section, a theoretical study of the nature of batch processes and of the synchronization of batch trajectories as a prior step to bilinear modeling is carried out. The topics under discussion are i) whether the same synchronization approach must be applied to batch data in presence of different types of asynchronisms, and ii) whether synchronization is always required even though the length of the variable trajectories are constant across batches. To answer these questions, a thorough study of the most common types of asynchronisms that may be found in batch data is done. Furthermore, two new synchronization techniques are proposed to solve the current problems in post-batch and real-time synchronization. To improve fault detection and classification, new unsupervised control charts and supervised fault classifiers based on the information generated by the batch synchronization are also proposed. In the third block of the manuscript, a research work is performed on the parameter stability associated with the most used synchronization methods and principal component analysis (PCA)-based Batch Multivariate Statistical Process Control methods. The results of this study have revealed that accuracy in batch synchronization has a profound impact on the PCA model parameters stability. Also, the parameter stability is closely related to the type of preprocessing performed in batch data, and the type of model and unfolding used to transform the three-way data structure to two-way. The setting of the parameter stability, the source of variability remaining after preprocessing and the process dynamics should be balanced in such a way that multivariate statistical models are accurate in fault detection and diagnosis and/or in online prediction. Finally, the fourth block introduces a graphical user-friendly interface developed in Matlab code for batch process understanding and monitoring. To perform multivariate analysis, the last developments in process chemometrics, including the methods proposed in this thesis, are implemented.[ES] La presente tesis doctoral tiene como objetivo estudiar las implicaciones de los métodos estadísticos propuestos para la modelización bilineal de procesos por lotes, el desarrollo de nuevas técnicas para solucionar algunos de los problemas más complejos aún por resolver en esta línea de investigación y aplicar los nuevos métodos a datos provenientes de procesos bioquímicos para su evaluación estadística. El estudio, la discusión y el desarrollo de los nuevos métodos giran en torno a las cuatro fases del ciclo de modelización: desde la sincronización, ecualización, preprocesamiento y calibración de los datos, a la monitorización de las trayectorias de las variables del proceso. Se presta especial atención al problema de la sincronización y su efecto en la modelización estadística desde distintas perspectivas. El manuscrito se ha dividido en cuatro grandes bloques. En primer lugar, se realiza una revisión bibliográfica de las técnicas de proyección sobre estructuras latentes para su aplicación en procesos continuos y por lotes, y del diseño de sistemas de control basados en modelos estadísticos multivariantes. El segundo bloque del documento versa sobre el preprocesamiento de los datos, en concreto, sobre la ecualización y la sincronización. La primera parte aborda el problema de la falta de ecualización en las trayectorias de las variables. Se discuten las diferentes políticas de muestreo que se pueden encontrar en procesos por lotes y las soluciones para ecualizar las variables. En la segunda parte de esta sección, se realiza un estudio teórico sobre la naturaleza de los procesos por lotes y de la sincronización de las trayectorias como paso previo a la modelización bilineal. Los temas bajo discusión son: i) si se debe utilizar el mismo enfoque de sincronización en lotes afectados por diferentes tipos de asincronismos, y ii) si la sincronización es siempre necesaria aún y cuando las trayectorias de las variables tienen la misma duración en todos los lotes. Para responder a estas preguntas, se lleva a cabo un estudio exhaustivo de los tipos más comunes de asincronismos que se pueden encontrar en este tipo de datos. Además, se proponen dos nuevas técnicas de sincronización para resolver los problemas existentes en aplicaciones post-morten y en tiempo real. Para mejorar la detección de fallos y la clasificación, también se proponen nuevos gráficos de control no supervisados y clasificadores de fallos supervisados en base a la información generada por la sincronización de los lotes. En el tercer bloque del manuscrito se realiza un estudio de la estabilidad de los parámetros asociados a los métodos de sincronización y a los métodos estadístico multivariante basados en el Análisis de Componentes Principales (PCA) más utilizados para el control de procesos. Los resultados de este estudio revelan que la precisión de la sincronización de las trayectorias tiene un impacto significativo en la estabilidad de los parámetros de los modelos PCA. Además, la estabilidad paramétrica está estrechamente relacionada con el tipo de preprocesamiento realizado en los datos de los lotes, el tipo de modelo a justado y el despliegue utilizado para transformar la estructura de datos de tres a dos dimensiones. El ajuste de la estabilidad de los parámetros, la fuente de variabilidad que queda después del preprocesamiento de los datos y la captura de las dinámicas del proceso deben ser a justados de forma equilibrada de tal manera que los modelos estadísticos multivariantes sean precisos en la detección y diagnóstico de fallos y/o en la predicción en tiempo real. Por último, el cuarto bloque del documento describe una interfaz gráfica de usuario que se ha desarrollado en código Matlab para la comprensión y la supervisión de procesos por lotes. Para llevar a cabo los análisis multivariantes, se han implementado los últimos desarrollos en la quimiometría de proc[CA] Aquesta tesi doctoral te com a objectiu estudiar les implicacions dels mètodes de modelització estadística proposats per a la modelització bilineal de processos per lots, el desenvolupament de noves tècniques per resoldre els problemes encara no resolts en aquesta línia de recerca i aplicar els nous mètodes a les dades dels processos bioquímics. L'estudi, la discussió i el desenvolupament dels nous mètodes giren entorn a les quatre fases del cicle de modelització, des de l'alineació, preprocessament i el calibratge de les dades provinents de lots, a la monitorització de les trajectòries. Es presta especial atenció al problema de la sincronització per lots, i el seu efecte sobre el modelatge des de diferents angles. El manuscrit s'ha dividit en quatre grans blocs. En primer lloc, es realitza una revisió bibliogràfica dels principals mètodes basats en tècniques de projecció sobre estructures latents en processos continus i per lots, així com dels sistemes de control estadístics multivariats. El segon bloc del document es dedica a la preprocessament de les dades provinents de lots, en particular, l' equalització i la sincronització. La primera part aborda el problema de la manca d'equalització en les trajectòries de les variables. Es discuteixen els diferents tipus d'escenaris en que les variables estan mesurades a distints intervals i les solucions per equalitzar-les en processos per lots. A la segona part d'aquesta secció es porta a terme un estudi teòric de la naturalesa dels processos per lots i de la sincronització de les trajectòries de lots com a pas previ al modelatge bilineal. Els temes en discussió són: i) si el mateix enfocament de sincronització ha de ser aplicat a les dades del lot en presència de diferents tipus de asincronismes, i ii) si la sincronització sempre es requereix tot i que la longitud de les trajectòries de les variables són constants en tots el lots. Per respondre a aquestes preguntes, es du a terme un estudi exhaustiu dels tipus més comuns de asincronismes que es poden trobar en les dades provinents de lots. A més, es proposen dues noves tècniques de sincronització per resoldre els problemes existents la sincronització post-morten i en temps real. Per millorar la detecció i la classificació de anomalies, també es proposen nous gràfics de control no supervisats i classificadors de falla supervisats dissenyats en base a la informació generada per la sincronització de lots. En el tercer bloc del manuscrit es realitza un treball de recerca sobre l'estabilitat dels paràmetres associats als mètodes de sincronització i als mètodes estadístics multivariats basats en l'Anàlisi de Components Principals (PCA) més utilitzats per al control de processos. Els resultats d'aquest estudi revelen que la precisió en la sincronització per lots te un profund impacte en l'estabilitat dels paràmetres dels models PCA. A més, l'estabilitat paramètrica està estretament relacionat amb el tipus de preprocessament realitzat en les dades provinents de lots, el tipus de model i el desplegament utilitzat per transformar l'estructura de dades de tres a dos dimensions. L'ajust de l'estabilitat dels paràmetres, la font de variabilitat que queda després del preprocessament i la captura de la dinàmica de procés ha de ser equilibrada de tal manera que els models estadístics multivariats són precisos en la detecció i diagnòstic de fallades i/o en la predicció en línia. Finalment, el quart bloc del document introdueix una interfície gràfica d'usuari que s'ha dissenyat e implementat en Matlab per a la comprensió i la supervisió de processos per lots. Per dur a terme aquestes anàlisis multivariats, s'han implementat els últims desenvolupaments en la quimiometria de processos, incloent-hi els mètodes proposats en aquesta tesi.González Martínez, JM. (2015). ADVANCES ON BILINEAR MODELING OF BIOCHEMICAL BATCH PROCESSES [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/55684TESISPremios Extraordinarios de tesis doctorale

On-line product quality and process failure monitoring in freeze-drying of pharmaceutical products

This is an Author's Accepted Manuscript of Domenico Colucci, José M. Prats-Montalbán, Alberto Ferrer & Davide Fissore (2021) On-line product quality and process failure monitoring in freeze-drying of pharmaceutical products, Drying Technology, 39:2, 134-147, DOI: 10.1080/07373937.2019.1614949 [copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/07373937.2019.1614949[EN] In this work the information provided by a noninvasive imaging sensor was used to develop two algorithms for real time fault detection and product quality monitoring during the Vacuum Freeze-Drying of single dose pharmaceuticals. Two algorithms based on multivariate statistical techniques, namely Principal Component Analysis and Partial Least Square Regression, were developed and compared. Five batches obtained under Normal Operating Conditions were used to train a reference model of the process; the classification abilities of these algorithms were tested on five more batches simulating different kind of faults. Good classification performances have been obtained with both algorithms. Coupling the information obtained from an infrared camera with that of other variables obtained from the PLC of the equipment, and from the textural analysis performed on the RGB images of the product, strongly improves the performances of the algorithms. The proposed algorithms can account for the heterogeneity of the batch and aim to reduce the off-specification products.This research work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the project DPI2017-82896-C2-1-R.Colucci, D.; Prats-Montalbán, JM.; Ferrer, A.; Fissore, D. (2021). On-line product quality and process failure monitoring in freeze-drying of pharmaceutical products. Drying Technology. 39(2):134-147. https://doi.org/10.1080/07373937.2019.1614949S134147392Jennings, T. A. (1999). Lyophilization. doi:10.1201/b14424PIKAL, M., SHAH, S., ROY, M., & PUTMAN, R. (1990). The secondary drying stage of freeze drying: drying kinetics as a function of temperature and chamber pressure☆. International Journal of Pharmaceutics, 60(3), 203-207. doi:10.1016/0378-5173(90)90074-eU. S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Veterinary, Medicine (CVM), Office of Regulatory Affairs (ORA), Pharmaceutical CGMPs. September 2004. Guidance for Industry, PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance, 2004. https://www.fda.gov/downloads/drugs/guidances/ucm070305.pdf (accessed Jan 2019).Barresi, A. A., Pisano, R., Fissore, D., Rasetto, V., Velardi, S. A., Vallan, A., … Galan, M. (2009). Monitoring of the primary drying of a lyophilization process in vials. Chemical Engineering and Processing: Process Intensification, 48(1), 408-423. doi:10.1016/j.cep.2008.05.004Patel, S. M., & Pikal, M. (2009). Process Analytical Technologies (PAT) in freeze-drying of parenteral products. Pharmaceutical Development and Technology, 14(6), 567-587. doi:10.3109/10837450903295116Fissore, D., Pisano, R., & Barresi, A. A. (2018). Process analytical technology for monitoring pharmaceuticals freeze-drying – A comprehensive review. Drying Technology, 36(15), 1839-1865. doi:10.1080/07373937.2018.1440590Barresi, A. A., Pisano, R., Rasetto, V., Fissore, D., & Marchisio, D. L. (2010). Model-Based Monitoring and Control of Industrial Freeze-Drying Processes: Effect of Batch Nonuniformity. Drying Technology, 28(5), 577-590. doi:10.1080/07373931003787934Pisano, R., Fissore, D., & Barresi, A. A. (2014). Intensification of Freeze-Drying for the Pharmaceutical and Food Industries. Modern Drying Technology, 131-161. doi:10.1002/9783527631704.ch05Fissore, D.; Pisano, R.; Barresi, A. On the Use of Temperature Measurement to Monitor a Freeze-Drying Process for Pharmaceuticals. Proceedings of IEEE International Instrumentation and Measurement Technology Conference “I2MTC 2017”, Torino, Italy, May 22–25, 2017; pp. 1276–1281.Bosca, S., Corbellini, S., Barresi, A. A., & Fissore, D. (2013). Freeze-Drying Monitoring Using a New Process Analytical Technology: Toward a «Zero Defect» Process. Drying Technology, 31(15), 1744-1755. doi:10.1080/07373937.2013.807431Grassini, S., Parvis, M., & Barresi, A. A. (2013). Inert Thermocouple With Nanometric Thickness for Lyophilization Monitoring. IEEE Transactions on Instrumentation and Measurement, 62(5), 1276-1283. doi:10.1109/tim.2012.2223312Emteborg, H., Zeleny, R., Charoud-Got, J., Martos, G., Lüddeke, J., Schellin, H., & Teipel, K. (2014). Infrared Thermography for Monitoring of Freeze-Drying Processes: Instrumental Developments and Preliminary Results. Journal of Pharmaceutical Sciences, 103(7), 2088-2097. doi:10.1002/jps.24017Van Bockstal, P.-J., Corver, J., De Meyer, L., Vervaet, C., & De Beer, T. (2018). Thermal Imaging as a Noncontact Inline Process Analytical Tool for Product Temperature Monitoring during Continuous Freeze-Drying of Unit Doses. Analytical Chemistry, 90(22), 13591-13599. doi:10.1021/acs.analchem.8b03788Lietta, E., Colucci, D., Distefano, G., & Fissore, D. (2019). On the Use of Infrared Thermography for Monitoring a Vial Freeze-Drying Process. Journal of Pharmaceutical Sciences, 108(1), 391-398. doi:10.1016/j.xphs.2018.07.025Velardi, S. A., & Barresi, A. A. (2008). Development of simplified models for the freeze-drying process and investigation of the optimal operating conditions. Chemical Engineering Research and Design, 86(1), 9-22. doi:10.1016/j.cherd.2007.10.007Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572. doi:10.1080/14786440109462720Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(7), 498-520. doi:10.1037/h0070888Nomikos, P., & MacGregor, J. F. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375. doi:10.1002/aic.690400809Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109-130. doi:10.1016/s0169-7439(01)00155-1Nomikos, P., & MacGregor, J. F. (1995). Multi-way partial least squares in monitoring batch processes. Chemometrics and Intelligent Laboratory Systems, 30(1), 97-108. doi:10.1016/0169-7439(95)00043-7Kourti, T. (2006). Process Analytical Technology Beyond Real-Time Analyzers: The Role of Multivariate Analysis. Critical Reviews in Analytical Chemistry, 36(3-4), 257-278. doi:10.1080/10408340600969957Van Sprang, E. N. ., Ramaker, H.-J., Westerhuis, J. A., Gurden, S. P., & Smilde, A. K. (2002). Critical evaluation of approaches for on-line batch process monitoring. Chemical Engineering Science, 57(18), 3979-3991. doi:10.1016/s0009-2509(02)00338-xRato, T. J., Rendall, R., Gomes, V., Chin, S.-T., Chiang, L. H., Saraiva, P. M., & Reis, M. S. (2016). A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part I—Assessing Detection Strength. Industrial & Engineering Chemistry Research, 55(18), 5342-5358. doi:10.1021/acs.iecr.5b04851Rato, T. J., Rendall, R., Gomes, V., Saraiva, P. M., & Reis, M. S. (2018). A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part II—Assessing Detection Speed. Industrial & Engineering Chemistry Research, 57(15), 5338-5350. doi:10.1021/acs.iecr.7b04911Bharati, M. H., & MacGregor, J. F. (1998). Multivariate Image Analysis for Real-Time Process Monitoring and Control. Industrial & Engineering Chemistry Research, 37(12), 4715-4724. doi:10.1021/ie980334lPrats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Duchesne, C., Liu, J. J., & MacGregor, J. F. (2012). Multivariate image analysis in the process industries: A review. Chemometrics and Intelligent Laboratory Systems, 117, 116-128. doi:10.1016/j.chemolab.2012.04.003Colucci, D., Prats-Montalbán, J. M., Fissore, D., & Ferrer, A. (2019). Application of multivariate image analysis for on-line monitoring of a freeze-drying process for pharmaceutical products in vials. Chemometrics and Intelligent Laboratory Systems, 187, 19-27. doi:10.1016/j.chemolab.2019.02.004Kourti, T. (2005). Application of latent variable methods to process control and multivariate statistical process control in industry. International Journal of Adaptive Control and Signal Processing, 19(4), 213-246. doi:10.1002/acs.859Kourti, T. (2003). Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. Journal of Chemometrics, 17(1), 93-109. doi:10.1002/cem.778Camacho, J., Picó, J., & Ferrer, A. (2009). The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter? Analytica Chimica Acta, 642(1-2), 59-68. doi:10.1016/j.aca.2009.02.001Kourti, T., Nomikos, P., & MacGregor, J. F. (1995). Analysis, monitoring and fault diagnosis of batch processes using multiblock and multiway PLS. Journal of Process Control, 5(4), 277-284. doi:10.1016/0959-1524(95)00019-mPatel, S. M., Doen, T., & Pikal, M. J. (2010). Determination of End Point of Primary Drying in Freeze-Drying Process Control. AAPS PharmSciTech, 11(1), 73-84. doi:10.1208/s12249-009-9362-7Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699Nomikos, P. Statistical Process Control of Batch Processes. PhD diss., McMaster University, Hamilton, Ontario, 1995.Arteaga, F., & Ferrer, A. (2002). Dealing with missing data in MSPC: several methods, different interpretations, some examples. Journal of Chemometrics, 16(8-10), 408-418. doi:10.1002/cem.750Arteaga, F., & Ferrer, A. (2005). Framework for regression-based missing data imputation methods in on-line MSPC. Journal of Chemometrics, 19(8), 439-447. doi:10.1002/cem.946García-Muñoz, S., Kourti, T., & MacGregor, J. F. (2004). Model Predictive Monitoring for Batch Processes. Industrial & Engineering Chemistry Research, 43(18), 5929-5941. doi:10.1021/ie034020wCamacho, J., Picó, J., & Ferrer, A. (2008). Bilinear modelling of batch processes. Part II: a comparison of PLS soft-sensors. Journal of Chemometrics, 22(10), 533-547. doi:10.1002/cem.1179Folch-Fortuny, A., Arteaga, F., & Ferrer, A. (2017). PLS model building with missing data: New algorithms and a comparative study. Journal of Chemometrics, 31(7), e2897. doi:10.1002/cem.2897Bharati, M. H.; MacGregor, J. F. Texture Analysis of Images Using Principal Component Analysis. Proceeding of SPIE/Photonics Conference on Process Imaging for Automatic Control, Boston, 2000; pp 27–37.Bellows, R. J., & King, C. J. (1972). Freeze-drying of aqueous solutions: Maximum allowable operating temperature. Cryobiology, 9(6), 559-561. doi:10.1016/0011-2240(72)90179-4Tsourouflis, S., Flink, J. M., & Karel, M. (1976). Loss of structure in freeze-dried carbohydrates solutions: Effect of temperature, moisture content and composition. Journal of the Science of Food and Agriculture, 27(6), 509-519. doi:10.1002/jsfa.2740270604Prats-Montalbán, J. M., & Ferrer, A. (2014). Statistical process control based on Multivariate Image Analysis: A new proposal for monitoring and defect detection. Computers & Chemical Engineering, 71, 501-511. doi:10.1016/j.compchemeng.2014.09.014Camacho, J., Picó, J., & Ferrer, A. (2008). Multi-phase analysis framework for handling batch process data. Journal of Chemometrics, 22(11-12), 632-643. doi:10.1002/cem.115

Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes

A

Industrial Data Science for Batch Manufacturing Processes

Batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant information for process engineers. Two common use cases will be presented: 1) AutoML analysis to quickly find correlations in batch process data, and 2) trajectory analysis to monitor and identify anomalous batches leading to process control improvements

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes