Batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an industrial example how to use machine learning to reduce this apparent excess of data while maintaining the relevant information for process engineers. Two common use cases will be presented: 1) AutoML analysis to quickly find correlations in batch process data, and 2) trajectory analysis to monitor and identify anomalous batches leading to process control improvements.
@article{arxiv.2209.09660,
title = {Industrial Data Science for Batch Manufacturing Processes},
author = {Imanol Arzac-Garmendia and Mattia Vallerio and Carlos Perez-Galvan and Francisco J. Navarro-Brull},
journal= {arXiv preprint arXiv:2209.09660},
year = {2022}
}