SBIR/STTR Research Award

36c. Virtual Subject Matter Expert for Computer Aided Decision Making

Award identifier f9a443208a76aec2f362138294f0601dec9aaa438a9c33b0f3a1408e8e94a75bAwarding agency Department of Energy

Award amount

$199,999

Program and phase

SBIR · Phase I

Award year

2022

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Award details

Awarding agency
Department of Energy
Branch
Not reported
Recipient company
EXPERT MICROSYSTEMS, INC
Proposal award date
2022-06-27
Contract end date
2023-03-26
UEI
TK8JN3Z2MCM3
Solicitation number
Not reported
Topic code
C54-36c

Research abstract

Our nation’s investment in our nuclear power plants is extensive and valuable. However, high operating costs, relative to alternative technologies, threaten the economic viability our nuclear fleet. Operation and maintenance are labor intensive, accounting for nearly two-thirds of the operating cost of a nuclear plant. Artificial intelligence and machine learning can automate monitoring and planning to help optimize decision making and substantially lower operating costs. The proposed solution creates a virtual expert that will continuously monitor each important item of equipment in the plant. This virtual expert never sleeps and will monitor and detect any abnormal changes in the operation of the equipment. When abnormal changes occur, the virtual expert will determine the most likely cause for the problem, based on the symptoms detected, and will propose the corrective action steps to resolve the problem with the lowest risk and cost. Additionally, the virtual expert will continuously estimate the remaining time available before the problem must be corrected to prevent the equipment from reaching an operating or maintenance limit, shutting down or failing. Phase I demonstrates feasibility of a Virtual Subject Matter Expert for the main steam turbine in a nuclear plant. Ultimately, the solution will entail a comprehensive library of these reusable virtual experts that is tailored and provided to equipment owners in nuclear power and other market segments. Three key innovations are integrated and verified in the project. These are 1) hybrid digital twin models for predicting the expected performance of the equipment while it is operating, 2) diagnostic decision models for evaluating any abnormal behavior and suggesting the best corrective action, and 3) prognostic models for accurately predicting the amount of time available to take a corrective action. These integrated models combine multiple artificial intelligence and machine learning algorithms to replicate the analysis methods used by human subject matter experts. The new innovations are built and demonstrated on top of an existing commercial software platform that is widely used across the power industry today for continuous online monitoring of plant equipment. The primary benefit to the public will be lower electricity rates and lower CO2 emissions. Public utilities will benefit from lower operating costs and avoided capital costs to replace nuclear assets with new generating assets. Commercially, the project innovations have broad applicability across nearly all process industries. As an established provider of online monitoring systems to all types of power plants, oil and gas plants and other manufacturers, the company is well positioned to put these innovations into immediate commercial use as an extension of our existing product lines.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

17 SBIR/STTR records share this UEI (1995–2022). Reported award amounts total $3,082,424. Reported amounts are not federal obligations.

Phases: Phase I, Phase II

Agencies: National Aeronautics and Space Administration, Department of Energy, Department of Defense, Department of Homeland Security

Agency intelligence

Department of Energy

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