Applications of Big Data Analytics for Manufacturing Process Improvements

Chemical and Process Industry (CPI) always has abundant data available via installed sensors and measuring units available in process plants. The data has been used primarily for process monitoring & control and quite often for process analytics to develop additional insights. With digitalization, the CPI has embraced “big data” - defined as increasing volume, variety and velocity of data – from “softer” industries and the role of data scientist is often being heard in plant performance discussions. The combination of the process plant data with data from operational staff & analytical labs in various different formats (text, images, graphics, process signals, times series etc.) increases complexity, size, variety and uncertainty (noise) making it challenging to analyze and build models using traditional approaches. New approaches are constantly being developed and put into practice where industry educates “big data” on how to utilize the available data to make the process plants economical, efficient and safer. This session aims to highlight use of such big data analytics possibly combined with machine learning for systematic improvement of processes or products at industrial scale. We solicit original contributions that emphasize such approaches and industrial applications.