Chapter 15: Industrial Data Science for Batch Reactor Monitoring and Fault Detection

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.