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Novel Clean Energy Technology in the U.S.

The U.S. Department of Energy (DOE) announced $175 million for 68 research and development projects aimed at developing disruptive technologies to strengthen the nation’s advanced energy enterprise. Led by DOE’s Advanced Research Projects Agency-Energy (ARPA-E), the OPEN 2021 program prioritises funding high-impact, high-risk technologies that support novel approaches to clean energy challenges. DOE’s Argonne National Laboratory was awarded $7.8 million for three projects.

The selected projects — spanning 22 states and coordinated at universities, national laboratories and private companies — will advance technologies for a wide range of areas, including electric vehicles, offshore wind, storage and nuclear recycling. These investments support President Biden’s climate goals to increase production of domestic clean energy technology, strengthen the nation’s energy security and uplift the economy by creating good-paying jobs.

Universities, companies, and our national labs are doubling down on advancing clean energy technology innovation and manufacturing in America to deliver critical energy solutions from renewables to fusion energy to tackle the climate crisis. DOE’s investments show our commitment to empowering innovators to develop bold plans to help America achieve net-zero emissions by 2050, create clean energy good-paying jobs and strengthen our energy independence

– Jennifer M. Granholm, U.S. Secretary of Energy

The selected projects will focus on technologies such as revolutionising fuel cells for light- and heavy-duty vehicles, and technologies to generate less nuclear waste and reduce the cost of fuel. Argonne’s OPEN 2021 project teams include:

Non-Neutron Transmutation of Used Nuclear Fuel:

This project will develop a technology that supports the establishment of commercially viable, dispatchable, zero-carbon nuclear energy for the future clean energy market. Partners: Massachusetts Institute of Technology; University of Michigan; University of California, Berkeley; Idaho National Laboratory and Brookhaven National Laboratory. (Award amount: $3,000,000).

Advanced Facility Design and Artificial Intelligence/Machine Learning Enabled Safeguards to Establish Secure, Economical Recycling of Fast Reactor Fuels:

This project will develop crucial technologies to support the commercialisation and licensing of pyroprocessing that enables the recovery and recycling of valuable nuclear materials from advanced reactor used nuclear fuel. Partner: Oklo. (Award amount: $3,600,000).

A Zero-Emission Process for Direct Reduction of Iron by Hydrogen Plasma in a Rotary Kiln Reactor:

This research seeks to disrupt the steel industry by developing a potentially zero-carbon ironmaking process that eliminates the use of coke or natural gas and requires less energy than current processes. Partners: the University of Illinois at Urbana-Champaign and ArcelorMittal. (Award amount: $1,200,000).

Argonne is committed to accelerating climate change solutions, which will help drive U.S. prosperity and security. As part of this effort, our researchers are transforming how we reuse nuclear fuels, design reactor safeguards, and manufacture zero-carbon steel. These scientific innovations would not be possible without ARPA-E’s support for clean-energy technology.

As reported by OpenGov Asia, In a new collaboration, Argonne computer scientists are putting the power of the laboratory’s automated machine learning expertise and supercomputers to use. By reducing the number of costly experiments and time-consuming simulations with a new machine learning approach, they can generate accurate models that provide valuable information about the welding process in much less time and at a fraction of the cost.

This approach, called DeepHyper, is a scalable automated machine learning package developed by Argonne computational scientist. Machine learning is a process by which a computer can train itself to find the best answers to a particular question.

DeepHyper automates the design and development of machine-learning-based predictive models, which often involve expert-driven, trial-and-error processes. Because no model is an absolute reflection of the truth. The researchers are not primarily trying to find the single best predictive model and the associated welding condition. Rather, they are generating hundreds of highly accurate models, combining them to assess uncertainties in the predictions, and then seeking to use these more tested predictions in the manufacturing process.

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