Data Science for Batch Processes

José M. González-Martínez is Manager of the Department of Chemometrics and Digital Chemistry at Shell in the Netherlands, overseeing worldwide operations and leading key consultancy efforts, new technology developments and R&D business initiatives. He specializes in Chemometrics and Statistics for Chemicals, Catalysis, Integrated Gas, CO2 Abatement, and Low Carbon Fuel and Gas solutions. He has published multiple scientific articles and patents, and has been awarded several academic and 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 data science algorithms and software in domains like precision medicine, industrial processes, cybersecurity or ecology. He is Scientific Advisor at Datharsis.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 Politcnica de Val?ncia, Spain. He is currently Chief Technology Officer at Kensight Solutions. He has received the ENBIS Young Statistician Award for his work 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, Chief Scientific Officer at Kenko Imalytics, Scientific Advisor at Kensight Solutions, and elected member of the International Statistical Institute. His research focuses on the development and integration of machine learning and multivariate statistics to address the digitalization challenges in industry, healthcare, and technology. He is the recipient of the ENBIS Box Medal Award 2025.

• Foreword Prologue: Challenges for the Third Millennium About the Companion Website 1 Introduction 1.1 Industrial Batch Processes1.2 Types of Sensors1.3 Batch Process Modeling1.3.1 Knowledge-based Models1.3.2 Data-driven Models1.3.3 Hybrid Models1.4 Bilinear Modeling Cycle for Batch Process Monitoring2 Data-driven Models Based on Latent Variables 2.1 Compression2.2 Principal Component Analysis2.2.1 Data Preprocessing2.2.2 Selection of the Number of Principal Components2.2.3 Parameters Stability2.3 Regression2.4 Regression Models based on Latent Variables2.4.1 Principal Component Regression2.4.2 Partial Least Squares2.4.3 Data Preprocessing2.4.4 Selection of the Number of Latent Variables2.4.5 PLS Versus Other Regression Models2.5 Multivariate Exploratory Data Analysis2.6 Missing Data2.6.1 Model Exploitation2.6.2 Model Building2.6.3 Final Reflections about Missing Data Imputation and MSPC3 Batch Data Equalization 3.1 Introduction3.2 Challenges in Batch Equalization3.3 Equalization of Variables within a Batch3.3.1 Discarding Intermediate Values3.3.2 Estimating Missing Values3.3.2.1 Comparison of Equalization Methods Based on Latent Variable Models3.3.3 Rearranging Data3.4 Multirate System4 Batch Synchronization 4.1 Introduction4.2 Synchronization Approaches4.2.1 Indicator Variable4.2.2 Time Linear Expanding/Compressing4.2.2.1 Observation (OWU) Level and TLEC Synchronization Approach4.2.3 Dynamic Time Warping4.2.3.1 Warping Function Constraints4.2.3.2 The DTW Algorithm4.2.3.3 Optimization Problem4.2.3.4 End-of-batch DTW Synchronization for Batch Process Monitoring4.2.3.5 On the Use of Warping Information4.2.4 Relaxed Greedy Time Warping4.2.4.1 Enhanced Global Constraints4.2.4.2 Cross-validation for the Estimation of the RGTW Parameters4.2.5 Multisynchro4.2.5.1 Asynchronism Detection4.2.5.2 Specific Batch Synchronization4.2.5.3 Iterative Batch Synchronization and Anomaly Detection Procedure4.3 Effects of Synchronization on the Correlation Structure5 Batch Data Preprocessing 5.1 Batch Preprocessing Operations5.2 Mean Centering5.3 Scaling6 Three-way to Two-way Transformation6.1 Introduction6.2 Single-model Approach6.2.1 Batch-wise Unfolding6.2.2 Variable-wise Unfolding6.2.3 Batch Dynamic Unfolding6.3 K-models Approach6.3.1 Hierarchical-model Approach6.4 Multiphase Approach6.4.1 Phases in Batch-wise Data6.4.2 Phases in Variable-wise Data6.4.3 Phases in Batch Dynamic Data6.5 Conclusion7 Batch Process Data Analysis and Statistical Monitoring 7.1 Introduction7.2 Historical Batch Data Analysis7.3 Batch Multivariate Statistical Process Control7.3.1 Phase I7.3.2 Phase II7.3.2.1 Post-batch Process Monitoring7.3.2.2 Real-time Process Monitoring7.4 Practical IssuesList of Acronyms Bibliography Index