The case for a near-term commercial demonstration of the Integral Fast Reactor
Bibliographic record
Abstract
Demonstrating a credible and acceptable way to safely recycle ‘used’ nuclear fuel will clear a socially acceptable pathway for nuclear fission to be a major low-carbon energy source for this century. Here we advocate for an accelerated timetable for commercial demonstration of Generation IV nuclear technology, via construction of a prototype metal-fueled fast neutron reactor and associated 100 t/year pyroprocessing facility to convert and recycle spent fuel (routinely mischaracterized as “nuclear waste”) that has accumulated from decades of light-water reactor use. Based on the pioneering research and development done during the ‘Integral Fast Reactor’ (IFR) program at Argonne National Laboratory, 1 a number of synergistic design choices are recommended: (a) a pool-type sodium-cooled reactor; (b) metal fuel based on a uranium–plutonium–zirconium alloy, and (c) recycling using electrorefining and pyroprocessing, thereby enabling the transmutation and repeated re-use of the actinides in the reactor system. We argue that alternative technology options for the coolant, fuel type and recycling system, while sometimes possessing individually attractive features, are challenging to combine into a sufficiently competitive overall system. A reactor blueprint that embodies these key design features, the General Electric-Hitachi 380 MWe PRISM, 2 based on the IFR, is ready for a commercial-prototype demonstration. A two-pronged approach for completion by 2020 could progress by a detailed design and demonstration of a 100 t/year pyroprocessing facility for conversion of spent oxide fuel from light-water reactors 3 into metal fuel for fast reactors, followed by construction of a prototype PRISM as a commercial-scale demonstration plant, with an initial focus on secure disposition of separated plutonium stocks. Ideally, this could be achieved via an international collaboration. Several countries have expressed great interest in such collaboration. Once demonstrated, this prototype would provide an international test facility for any concept improvements. It is expected to achieve significant advances in reactor safety, reliability, fuel resource sustainability, management of long-term waste, improved proliferation resistance, and economics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".