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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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GAO: Grouting Hanford tank waste could cost more than $1.1B
Workers move a container of treated tank waste as part of Hanford’s Test Bed Initiative to grout around 2,000 gallons of LAW for off-site disposal. (Photo: DOE)
Grouting Hanford’s low-level radioactive liquid tank waste could cost between $480 million and $1.1 billion, according to a report by the Government Accountability Office, which has repeatedly found that grouting (immobilizing waste in a concrete-like mixture) can accelerate cleanup at the Hanford Site and save billions of dollars when compared to mixing the waste with molten glass through the vitrification process.
Technical Session|Panel|Sponsored by THD
Monday, November 16, 2020|1:00–3:10PM EST
Session Chair:
Richard B. Vilim
Session Organizer:
Alternate Chair:
Thomas Esselman
Staff Producer:
Janet Davis (American Nuclear Society)
The opportunities for application of artificial intelligence (AI) and machine learning (ML) to nuclear power for securing the energy future of the U.S. are legion. While applications to the existing fleet are currently underway, it is the advanced reactors operating in the future energy landscape that present the greatest opportunity. AI/ML can potentially transform the use of nuclear power and improve its economic competitiveness. This panel will focus on the staffing problem and the related competitiveness problem, which are already manifest in the current fleet. The challenge is to transform the human from a labor-intensive role to an overseer of technology that operates autonomously, safely, and fits into a dynamic energy network composed of an array of production and storage technologies.
Thomas Roettger
Northrop Grumman
Raghu Avali
Carnegie Mellon Univ.
Richard Wood
Univ. Tennessee, Knoxville
Ken Thomas
INL
Alison Hahn
Office of Nuclear Energy, DOE
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