Data Science for Batch Processes : Statistical Learning, Monitoring and Understanding

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Author(s):	Borrás-Ferrís, Joan Camacho-P?ez, Jos? Camacho-Páez, José Ferrer, Alberto
ISBN No.:	9783527326402
Pages:	256
Year:	202601
Format:	Trade Cloth (Hard Cover)
Price:	$ 227.61
Dispatch delay:	Dispatched between 7 to 15 days
Status:	Available (Forthcoming)

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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.

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