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

Real-time synchronization of batch trajectories for on-line multivariate statistical process control using Dynamic Time Warping

This paper addresses the real-time monitoring of batch processes with multiple different local time trajectories of variables measured during the process run. For Unfold Principal Component Analysis (U-PCA)—or Unfold Partial Least Squares (U-PLS)-based on-line monitoring of batch processes, batch runs need to be synchronized, not only to have the same time length, but also such that key events happen at the same time. An adaptation from Kassidas et al.'s approach [1] will be introduced to achieve the on-line synchronization of batch trajectories using the Dynamic Time Warping (DTW) algorithm. In the proposed adaptation, a new boundaries definition is presented for accurate on-line synchronization of an ongoing batch, together with a way to adapt mapping boundaries to batch length. A relaxed greedy strategy is introduced to avoid assessing the optimal path each time a new sample is available. The key advantages of the proposed strategy are its computational speed and accuracy for the batch process context. Data from realistic simulations of a fermentation process of the Saccharomyces cerevisae cultivation are used to illustrate the performance of the proposed strategy.This research work was supported by the Spanish government under the project (DPI2008-06880-C03-03). We also gratefully acknowledge Jose Camacho PhD. for providing simulated data from a fermentation process of Saccharomyces cerevisae. The authors would also like to acknowledge the valuable suggestions made by Prof. Paul Taylor.González Martínez, JM.; Ferrer Riquelme, AJ.; Westerhuis, JA. (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. https://doi.org/10.1016/j.chemolab.2011.01.003S195206105

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). Multi-synchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms. Journal of Chemometrics. 28(5):462-475. https://doi.org/10.1002/cem.2620S462475285Kourti, T. (2009). Multivariate Statistical Process Control and Process Control, Using Latent Variables. Comprehensive Chemometrics, 21-54. doi:10.1016/b978-044452701-1.00013-2Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3Kourti, T. (2003). Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications. Annual Reviews in Control, 27(2), 131-139. doi:10.1016/j.arcontrol.2003.10.004Lakshminarayanan S Gudi R Shah S Monitoring batch processes using multivariate statistical tools: extensions and practical issues. 1996 241 246Zarzo, 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.2003.11.009Louwerse, D. J., & Smilde, A. K. (2000). Multivariate statistical process control of batch processes based on three-way models. Chemical Engineering Science, 55(7), 1225-1235. doi:10.1016/s0009-2509(99)00408-xWesterhuis, 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. (1994). Monitoring batch processes using multiway principal component analysis. AIChE Journal, 40(8), 1361-1375. doi:10.1002/aic.690400809Ündey, C., Ertunç, S., & Çınar, A. (2003). Online Batch/Fed-Batch Process Performance Monitoring, Quality Prediction, and Variable-Contribution Analysis for Diagnosis. 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Online Temporal Signal Comparison Using Singular Points Augmented Time Warping. Industrial & Engineering Chemistry Research, 46(13), 4531-4548. doi:10.1021/ie060111sChen, J., & Liu, J. (2000). Post analysis on different operating time processes using orthonormal function approximation and multiway principal component analysis. Journal of Process Control, 10(5), 411-418. doi:10.1016/s0959-1524(00)00016-0Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. doi:10.1109/tassp.1978.1163055Nielsen, N.-P. V., Carstensen, J. M., & Smedsgaard, J. (1998). Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. Journal of Chromatography A, 805(1-2), 17-35. doi:10.1016/s0021-9673(98)00021-1Pravdova, V., Walczak, B., & Massart, D. L. (2002). 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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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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

A generic SPC framework for the characterization of batch profiles

This work develops a generic Statistical Process Control (SPC) framework for characterizing the systematic variability seen in a historical database of batch profiles in terms of meaningful scale parameters and studying the correlation with the final product quality. The complete framework, in contrast to existing methods, is geared towards giving meaningful results that can be easily connected to the actual process.The variability in the profiles is partitioned into two parts - consistent and inconsistent. The consistent variability is characterized using scale parameters. The Consistent variability is further partitioned as along the time and measurement axes leading to time and magnitude scale parameters for the same, respectively. Time Scaling refers to the alignment of events in a batch while magnitude scaling refers to the use of projection methods for extracting the systematic variability along the measurement axis. The framework thus integrates the time synchronization of thebatch trajectories and the subsequent application of multivariate techniques for monitoring and quality correlation purposes.Tools for time and magnitude scaling are developed. The use of DynamicTime Warping (DTW) for time scaling is studied. A novel technique based on translation of feature vectors is also developed and is shown to be especially suited for time scaling batch profiles. Magnitude scaling is based on the application of projection methods such as Principal Component Analysis (PGA) for extracting directions of systematic measurement axis variability in the profiles. PGA is modified to an evolving factor type method, for extracting easily interpretable factors.IllMultivariate SPC charts for process monitoring on the scale parameters and the residuals remaining after scaling are developed. The framework presented is an offline analysis and can be readily adapted for online monitoring purposes.A polymethyl methacrylate batch polymerization simulation is used in order to demonstrate the application of the SPC framework. The importance of time scaling in explaining a significant amount of the overall variability in the profiles and also significant correlation with the final batch quality is established. Comparisons with multiway principal component analysis (MPCA), the existing framework, show the better sensitivity of the proposed technique to special cause disturbances. The ease in the engineering interpretation of the results as contrasted to MPCA is also established

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Mining users' significant driving routes with low-power sensors

While there is significant work on sensing and recognition of significant places for users, little attention has been given to users' significant routes. Recognizing these routine journeys, opens doors to the development of novel applications, like personalized travel alerts, and enhancement of user's travel experience. However, the high energy consumption of traditional location sensing technologies, such as GPS or WiFi based localization, is a barrier to passive and ubiquitous route sensing through smartphones. In this paper, we present a passive route sensing framework that continuously monitors a vehicle user solely through a phone's gyroscope and accelerometer. This approach can differentiate and recognize various routes taken by the user by time warping angular speeds experienced by the phone while in transit and is independent of phone orientation and location within the vehicle, small detours and traffic conditions. We compare the route learning and recognition capabilities of this approach with GPS trajectory analysis and show that it achieves similar performance. Moreover, with an embedded co-processor, common to most new generation phones, it achieves energy savings of an order of magnitude over the GPS sensor.This research has been funded by the EPSRC Innovation and Knowledge Centre for Smart Infrastructure and Construction project (EP/K000314).This is the author accepted manuscript. The final version is available from ACM via http://dx.doi.org/10.1145/2668332.266834