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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Sprache
Verlagsort
Zielgruppe
Illustrationen
307
300 s/w Abbildungen, 7 farbige Abbildungen
300 schwarz-weiße und 7 farbige Abbildungen
Maße
Höhe: 240 mm
Breite: 170 mm
Dicke: 170 mm
ISBN-13
978-3-527-32640-2 (9783527326402)
Schweitzer Klassifikation
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.
Autor*in
Universidad Politécnica de Valencia, Valencia, Spain
Universidad Politécnica de Valencia, Spain
Technical University of Valencia, Spain
Technical University of Valencia, Spain
Prologue: Challenges for the third millennium
1 Introduction
1.1 Industrial batch processes
1.2 Types of sensors
1.3 Batch process modeling
1.4 Bilinear modeling cycle for batch process monitoring
2 Data-driven models based on latent variables
2.1 Compression
2.2 Principal components analysis
2.3 Regression
2.4 Regression models based on latent variables
2.5 Multivariate Exploratory Data Analysis
2.6 Missing data
3 Batch data equalization
3.1 Introduction
3.2 Challenges in batch equalization
3.3 Equalization of variables within a batch
3.4 Multi-rate system
4 Batch synchronization
4.1 Introduction
4.2 Synchronization approaches
4.3 Effect of synchronization on the correlation structure
5 Batch data preprocessing
5.1 Batch preprocessing operations
5.2 Mean centering
5.3 Scaling
6 Three-way to two-way transformation
6.1 Introduction
6.2 Single-model approach
6.3 K-models approach
6.4 Multi-phase Approach
6.5 Conclusions
7 Batch Process Data Analysis and Statistical Monitoring
7.1 Introduction
7.2 Historical Batch Data Analysis
7.3 Batch Multivariate Statistical Process Control (BMSPC)
7.4 Practical issues
