Big Data Analytics in Chemical Engineering | AIChE

Big Data Analytics in Chemical Engineering


Chiang, L., Dow Inc.


Samudra, A. P., Rockwell Automation
Singh, R., Rutgers, The State University of New Jer
Maddala, J., West Virginia University

Advanced sensing technologies and increased communications are increasing the amount and variety of data available to monitor chemical processes. This session will cover analytics techniques to translate Big Data into actionable insights(such as: improve product quality, improve productivity and yield, and save money) using this step: Data -> Information -> Knowledge -> Wisdom Analytics based on data-driven models (such as multivariate analysis, machine learning & data mining techniques) are typically used in chemical processes. Presentations will address the Big Data 4 Vs (Volume, Velocity, Variety, and Veracity): Volume: Typical data historian stores thousands of process variables every minute. Data volume is an important aspect to consider in off-line multivariate batch data analysis Velocity: Real-time process monitoring and control need to address process dynamics. On-line data are available rapidly and therefore model maintenance issues are of particular interest here Variety: Chemical processes collect data from various sources including raw material, process, product quality, customer feedback, and other unstructured sources (i.e., text data). Analytics are more insightful when data are coming from multiple sources Veracity: Data pre-processing approaches are used to address data accuracy and integrity issues (such as missing data, outlier, noisy data, and batch alignment) This session welcomes both methodological contributions as well as industrial practitioner case studies.



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