González-Martínez / Camacho / Borràs-Ferrís Data Science for Batch Processes

Statistical Learning, Monitoring and Understanding
1. Auflage 2026
ISBN: 978-3-527-65039-2
Verlag: Wiley-VCH
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 227 Seiten, E-Book

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.

Autoren/Hrsg.

González-Martínez, José M.

Camacho, José

Borràs-Ferrís, Joan

Ferrer, Alberto

Inhaltsverzeichnis

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

Über Autor(innen)

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.