Recenze Data Science for Batch Processes

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