Big Data and Data Analytics

In this session the importance of utilizing big data and data science to improve process productivity, reliability, and control will be highlighted. Presentations would 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. We seek contributions that reflect the use of new information streams (e.g., real-time asset tracking, large numbers of wireless sensors and actuators, big data analytics), ubiquitous distributed computing and cloud-based computational resources, to enhance decision making, both in process design and in process operations. Theoretical developments and applications of interest include (but are not limited to) the use of high-fidelity (e.g., computational fluid dynamics) models in real-time operations, integration of operations and supply chains across multiple industries (including industrial demand response) and methods for tighter coordination between the control, scheduling and planning functions of the chemical supply chain.