A self-optimised approach to synthesising DEHiBA for advanced nuclear reprocessing, exploiting the power of machine-learning
Author:
Affiliation:
1. Institute of Process Research and Development, School of Chemistry & School of Chemical and Process Engineering, University of Leeds, LS2 9JT, UK
Abstract
Funder
Engineering and Physical Sciences Research Council
Royal Academy of Engineering
Publisher
Royal Society of Chemistry (RSC)
Subject
Fluid Flow and Transfer Processes,Process Chemistry and Technology,Chemical Engineering (miscellaneous),Chemistry (miscellaneous),Catalysis
Link
http://pubs.rsc.org/en/content/articlepdf/2024/RE/D3RE00357D
Reference80 articles.
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2. IAEA , Nuclear Technology Review , IAEA.org , 2020
3. The Role of Nuclear Power in Meeting Current and Future Industrial Process Heat Demands
4. Step changes for decarbonising the energy system: research needs for renewables, energy efficiency and nuclear power
5. The Future of Low-Carbon Electricity
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