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Batch Processes

Batch Processes


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About the Book

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:
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


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).


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Product Details
  • ISBN-13: 9783527326402
  • Publisher: Wiley-VCH Verlag GmbH
  • Publisher Imprint: Blackwell Verlag GmbH
  • Height: 240 mm
  • No of Pages: 340
  • ISBN-10: 3527326405
  • Publisher Date: 20 May 2026
  • Binding: Hardback
  • Language: English
  • Width: 170 mm


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