An overview of the fundamental principles of batch processes
Batch Processes addresses practical challenges in batch data analysis, with real-world case studies and hands-on MATLAB examples using the MVBatch toolbox bridging theory and practice and demonstrating how LSB methods improve quality, safety, and economic and ecological outcomes across chemical, biotech, and pharmaceutical industries. The book is supported by exercises and free software to enable reader learning.
In Batch Processes, readers will find information on:
- Preprocessing, missing data imputation, equalization, synchronization (DTW, RGTW, multisynchro), and multi-phase modeling
- Modeling of batch processes with 2-way models, covering cross-validation algorithms and a multi-phase analysis framework
- Multivariate statistical process control of batch processes, covering statistical process control in continuous processes, analysis of historical data in batch processes (phase I), and on-line monitoring of batch processes (phase II)
- Other applications of LVB methods to batch processes, including soft-sensors and optimization
Batch Processes 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.
Table of Contents:
Foreword Prologue: Challenges for the Third Millennium
About the Companion Website
1 Introduction
1.1 Industrial Batch Processes
1.2 Types of Sensors
1.3 Batch Process Modeling
1.3.1 Knowledge-based Models
1.3.2 Data-driven Models
1.3.3 Hybrid Models
1.4 Bilinear Modeling Cycle for Batch Process Monitoring
2 Data-driven Models Based on Latent Variables
2.1 Compression
2.2 Principal Component Analysis
2.2.1 Data Preprocessing
2.2.2 Selection of the Number of Principal Components
2.2.3 Parameters Stability
2.3 Regression
2.4 Regression Models based on Latent Variables
2.4.1 Principal Component Regression
2.4.2 Partial Least Squares
2.4.3 Data Preprocessing
2.4.4 Selection of the Number of Latent Variables
2.4.5 PLS Versus Other Regression Models
2.5 Multivariate Exploratory Data Analysis
2.6 Missing Data
2.6.1 Model Exploitation
2.6.2 Model Building
2.6.3 Final Reflections about Missing Data Imputation and MSPC
3 Batch Data Equalization
3.1 Introduction
3.2 Challenges in Batch Equalization
3.3 Equalization of Variables within a Batch
3.3.1 Discarding Intermediate Values
3.3.2 Estimating Missing Values
3.3.2.1 Comparison of Equalization Methods Based on Latent Variable Models
3.3.3 Rearranging Data
3.4 Multirate System
4 Batch Synchronization
4.1 Introduction
4.2 Synchronization Approaches
4.2.1 Indicator Variable
4.2.2 Time Linear Expanding/Compressing
4.2.2.1 Observation (OWU) Level and TLEC Synchronization Approach
4.2.3 Dynamic Time Warping
4.2.3.1 Warping Function Constraints
4.2.3.2 The DTW Algorithm
4.2.3.3 Optimization Problem
4.2.3.4 End-of-batch DTW Synchronization for Batch Process Monitoring
4.2.3.5 On the Use of Warping Information
4.2.4 Relaxed Greedy Time Warping
4.2.4.1 Enhanced Global Constraints
4.2.4.2 Cross-validation for the Estimation of the RGTW Parameters
4.2.5 Multisynchro
4.2.5.1 Asynchronism Detection
4.2.5.2 Specific Batch Synchronization
4.2.5.3 Iterative Batch Synchronization and Anomaly Detection Procedure
4.3 Effects 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
6.2.1 Batch-wise Unfolding
6.2.2 Variable-wise Unfolding
6.2.3 Batch Dynamic Unfolding
6.3 K-models Approach
6.3.1 Hierarchical-model Approach
6.4 Multiphase Approach
6.4.1 Phases in Batch-wise Data
6.4.2 Phases in Variable-wise Data
6.4.3 Phases in Batch Dynamic Data
6.5 Conclusion
7 Batch Process Data Analysis and Statistical Monitoring
7.1 Introduction
7.2 Historical Batch Data Analysis
7.3 Batch Multivariate Statistical Process Control
7.3.1 Phase I
7.3.2 Phase II
7.3.2.1 Post-batch Process Monitoring
7.3.2.2 Real-time Process Monitoring
7.4 Practical Issues
List of Acronyms
Bibliography
Index
About the Author :
José M. González-Martínez is Manager of the Department of Chemometrics and Digital Chemistry at Shell Global Solutions International B.V., Amsterdam.
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.
Joan Borràs-Ferrís is a researcher and specialist in chemical engineering, applied statistics, and process modeling in digitalized industrial environments.
Alberto Ferrer is a Full Professor of Statistics at the Universitat Politècnica de València (Spain).
