The Nuclear Energy Institute (NEI) jointly recognized Constellation (named Exelon Generation at the time) and Blue Wave AI Labs with a Top Innovation Practice (TIP) award for “Moisture Carryover (MCO) Predictions through Neural Networks” at the recent NEI 2021 Annual Meeting. The prestigious award in the nuclear fuel category recognizes creative ideas that have substantial impact on improving the safety, reliability, and economics of nuclear energy.
The TIP award underscores an industry first-of-a-kind and state-of-the-art strategy for the predictions of MCO at the design stage of a nuclear fuel cycle. MCO has been near impossible to predict by conventional methods, where the traditional approach to mitigate high MCO is to design the core with a larger than required reload batch size, thereby introducing potentially unnecessarily high reload fuel costs.
Excess moisture in the steam is problematic for many reasons, most importantly due to the ability to carry impurities dissolved in the water throughout the entire plant. MCO can increase erosion of the internal surfaces of the main steam isolation valves (MSIVs) and at the turbine, potentially causing costly repairs.
Blue Wave, in collaboration with Constellation Energy Generation (formerly Exelon Generation), leveraged existing plant data to innovate an AI-based predictive model for MCO that increases operating flexibility and reduces reload fuel costs. As the TIP award highlights, the application to reload design realizes up to $1.5M reduction in fuel costs per reload. In addition to fuel cost reductions, the added operational flexibility recently saved nearly $6M in power generation losses at one of Constellation’s generating stations.
The concept of MCO.ai was also selected to be part of EPRI’s Plant Modernization Toolbox as a Modernization Technology Assessment. EPRI’s Plant Modernization Toolbox is a resource to facilitate decision making and execution of the modernization process at nuclear power plants (NPPs). The Toolbox includes a variety of tools and aids to assist NPPs to identify and evaluate cost savings from technology and process improvements.
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