Batch Processes

Nyhet

Monitoring and Process Understanding

Inbunden, Engelska, 2026

AvJosé M. González-Martínez,José Camacho,Joan Borràs-Ferrís,Alberto Ferrer,J Camacho–Paez

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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 modelingModeling of batch processes with 2-way models, covering cross-validation algorithms and a multi-phase analysis frameworkMultivariate 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 optimizationBatch 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.

Produktinformation

Utgivningsdatum2026-06-10
Mått170 x 240 x 170 mm
FormatInbunden
SpråkEngelska
Antal sidor256
FörlagWiley-VCH Verlag GmbH
ISBN9783527326402

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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).

Prologue: Challenges for the third millennium1 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 variables2.1 Compression2.2 Principal components analysis 2.3 Regression2.4 Regression models based on latent variables2.5 Multivariate Exploratory Data Analysis2.6 Missing data3 Batch data equalization 3.1 Introduction 3.2 Challenges in batch equalization3.3 Equalization of variables within a batch3.4 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 approach6.4 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