Big Data Analytics and Process Safety Joint Plenary I

Chiang, L. H., The Dow Chemical Company
Castillo, I., Dow Inc.

In this joint plenary session the importance of utilizing big data analytics to improve process safety, reliability, and productivity will be highlighted. Presentations will cover a wide spectrum of topics from approaches that improve decision making on process safety to how process industries (such as chemical, oil & gas, semiconductor, and food) find actionable insights from Big Data to optimize process operation, and therefore, increase plant reliability and integrity. Big data can be identified by the four Vs (Volume, Velocity, Variety, and Veracity). With the continuing increase of plant data (Volume), there is a need to identify approaches that efficiently bring relevant information from data to improve process safety (for example, accelerating investigations into the root causes of safety incidents utilizing big data analytics). Speed in data preprocessing and analysis (Velocity) contributes to actions in real-time that avoid disruptions in production; novel approaches to identify process safety indicators that proactively anticipate safety abnormalities are an area of interest for this session. Contributions that benefit from utilizing data from various sources (Variety) and create an impact on decision making, development of leading indicators and Hazard Risk Assessment are also welcome in this session. Examples of data sources include raw materials, process operation, product quality, customer feedback, and other unstructured sources (i.e., text data, images). Finally, big data can have biases and inaccuracies (Veracity) that need to be identified and accounted for in order to reduce inference errors and improve the accuracy of generated insights.



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