Mattia Vallerio, Advanced Process Control Specialist, Solvay
Francisco Navarro, Industrial Data Scientist, Solvay
Carlos Perez-Galvan, Industrial Data Scientist, Solvay
Batch processes are subject to high variability: raw material composition, initial condition, unit degradation, and their intrinsic dynamic nature. Additionally, they are characterized by several distinct phases and steps that drastically change the conditions during the manufacturing process. In this presentation, we will illustrate with an industrial example how to use data science and machine learning to convert this high variability and apparent excess of data into valuable information. First, we will show how to summarize batch properties into features and use the open-source Predictor Explainer Add-in to identify the most relevant ones using AutoML and ExplainableAI. Then, we will discuss the need to align the data timewise before performing trajectory analysis, briefly introducing the pros and cons of different methodologies to achieve this result. Finally, we will dive into trajectory analysis. In this last step, we will use the Functional Data Explorer functionality of JMP Pro to monitor and identify deviations. Analyzing these deviations will lead us to identify key process control improvements to optimize production further.
