Data Science for Batch Processes: Statistical Learning, Monitoring and Understanding - Hardcover

González-Martínez, José M.; Camacho, José; Borràs-Ferrís, Joan; Ferrer, Alberto

Hardcover

ISBN 10: 3527326405 ISBN 13: 9783527326402

Verlag: Wiley-VCH, 2026

Inhaltsangabe

Batch Processes: Monitoring and Process Understanding offers a comprehensive examination of batch process modeling, monitoring, and control, emphasizing PAT and LSB methods such as principal component analysis (PCA) and partial least squares (PLS). Batch Processes: Monitoring and Process Understanding addresses practical challenges in batch data analysis, such as preprocessing, missing data imputation, equalization, synchronization (DTW, RGTW, Multisynchro), and multi-phase modeling. Real-world case studies and hands-on MATLAB examples using the MVBatch toolbox bridge theory and practice, demonstrating how LSB methods improve quality, safety, and economic and ecological outcomes across chemical, biotech, and pharmaceutical industries. Batch Processes: Monitoring and Process Understanding is an essential guide for professionals in chemical, biotech and pharmaceutical industries seeking both foundational knowledge and advanced techniques in batch processes and data analysis.

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Über die Autorin bzw. den Autor

José M. González-Martínez is Manager of the Department of Chemometrics and Digital Chemistry at Shell Global Solutions International B.V., Amsterdam, overseeing worldwide operations and leading key consultancy efforts, new technology developments, and process improvement projects, and R&D business initiatives. He specializes in Process and Analytical Chemometrics across diverse areas such as Chemicals, Refining, Catalysis, Integrated Gas (LNG, GTL, xTL), and Low Carbon Fuel and Gas solutions. He has published extensively and was awarded several industry prizes.
 
José Camacho is a Full Professor at the Department of Signal Theory, Telematics and Communication and leader of the Computational Data Science Laboratory (CoDaS Lab) at the University of Granada, Spain. He specializes in extracting knowledge from data and the design of new algorithms to do so. He has more than 150 publications, half of them in highly cited impact journals (JCR).
 
Joan Borràs-Ferrís is a researcher and specialist in chemical engineering, applied statistics, and process modeling in digitalized industrial environments. He holds a PhD in Statistics and Optimization from the Universitat Politècnica de València (UPV) and has developed his career between UPV (Spain) and Université Laval (Canada). He is currently Co-founder and Chief Technology Officer (CTO) at Kensight. He has received the ENBIS Young Statistician Award for his work in introducing innovative methods that promote the use of statistics in daily practice.
 
Alberto Ferrer is a Full Professor of Statistics at the Universitat Politècnica de València (Spain), head of the Multivariate Statistical Engineering Group (GIEM), co-founder and Chief Scientific Officer at Kenko Imalytics, S.L., and co-founder and Scientific Advisor at Kensight Solutions, S.L. His research focuses on the development and integration of machine learning and multivariate statistics in Data Science, collaborating with national and international researchers and industry partners (chemical, pharma, food) to address the challenges that digitalization is generating in industry, healthcare, and technology. He is a member of ISBIS and ENBIS, and an elected member of the International Statistical Institute (ISI). He is the recipient of the ENBIS Box Medal Award 2025.

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Overview of methods for bilinear modeling of batch data, including theory, methodologies and examples for experienced professionals in the biotech, pharmaceutical and petrochemical industries.

Process Analytical Technologies (PAT) have become increasingly important with the establishment of the quality-by-design paradigm in industrial processes, particularly where batch operation is standard. PAT plays an instrumental role in advancing process understanding and operational efficiency, while strengthening safety and reliability to ensure consistent on-spec product quality and minimize environmental impact. Empirical methods based on latent variables, often referred to as chemometric methods, are a main component of PAT. When used alongside Batch Multivariate Statistical Process Control (BMSPC), these methods enable the timely detection and diagnosis of process upsets. Furthermore, process understanding can be improved by applying Latent Variable Models (LVMs), such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), particularly relevant in batch processes, where the inherent complexity of the model results in a high degree of uncertainty in the operation.

Data Science for Batch Processes: Statistical Learning, Monitoring and Understanding provides a comprehensive and rigorous examination of the bilinear modeling and monitoring of batch processes, comprising data alignment, pre-processing, three-way-to-two-way data transformation, data analysis and design of monitoring systems, including practical challenges and considerations when analyzing multi-dimensional batch data. Case studies and hands-on MATLAB examples using the MVBatch toolbox bridge theory and practice, illustrating how these methods can be applied.

Data Science for Batch Processes: Statistical Learning, Monitoring and Understanding is an essential guide for professionals and academics who seek both foundational knowledge and advanced techniques in batch processes and data analysis.

Details zur Ausgabe

• Verlag: Wiley-VCH
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 3527326405
• ISBN 13: 9783527326402
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 224
• Kontakt zum Hersteller: Wiley-VCH GmbH; product_safety@wiley.com; ; Boschstrasse 12; Weinheim; 69469; Deutschland