Data Science for Batch Processes

Batch Processes: Monitoring and Process Understanding offers a comprehensive examination of batch process modeling, monitoring, and control, emphasizing PAT and LSB methods such as principal component analysis (PCA) and partial least squares (PLS). Batch Processes: Monitoring and Process Understanding addresses practical challenges in batch data analysis, such as preprocessing, missing data imputation, equalization, synchronization (DTW, RGTW, Multisynchro), and multi-phase modeling. Real-world case studies and hands-on MATLAB examples using the MVBatch toolbox bridge theory and practice, demonstrating how LSB methods improve quality, safety, and economic and ecological outcomes across chemical, biotech, and pharmaceutical industries. Batch Processes: Monitoring and Process Understanding 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.