José M. González-Martínez: Data Science for Batch Processes, Gebunden

Klappentext

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.