Combinatorial Evaluation of Physical Feature Engineering and Deep Temporal Modeling for Synchrophasor Data at Scale

This research will develop a digital twin of a centrifugal contactor system that receives data from traditional and real time sensors, constructs a digital representation or simulation of the chemical separations component within the nuclear fuel cycle, and performs data analysis through machine learning to determine anomalies, failures, and trends. The research will include the identification and implementation of advanced artificial intelligence, machine learning, and data analysis techniques advised by a team of nuclear safeguards experts.