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Nuclear Reactors 1066 - SaskPower Bringing SMRs to Saskatchewan

     Canadian utility SaskPower has identified Estevan and Elbow, two areas in the province of Saskatchewan, for further study to determine the feasibility of hosting a small modular reactor (SMR).
     The Estevan study area incudes the areas around the Boundary/Rafferty Dam as well as the area around Grant Devine Dam. The Elbow study area contains the area around Lake Diefenbaker for Gardines Dam to the Diefenbaker Dam.
      In order to identify these study areas, SaskPower used technical criteria based on the requirements of various SMR technologies. These SMRs were evaluated by the utility earlier this year. These criteria include proximity to a suitable water supply, existing power infrastructure, workforce, nuclear regulations and standards, and learnings from past generation siting projects.
     Work will now begin on environmental and impact assessments and the Regional Evaluation Process (REP). This process will share current information about the project with regional and stakeholder organizations as well as indigenous groups potentially affected. It will also allow SaskPower to gather input on regional identity, siting considerations, and potential economic development. This information will be combined with future public participation preferences to support its regulatory and siting process. As a part of the REP, SaskPower will create a Regional and Indigenous and Stakeholder Committee made up of nominated representatives from each study area. This will be used in support of public participation on the SMR development project.
    Rupen Pandya is the SaskPower CEO. He said, “Feedback and perspectives from not just the regions but from the entire province are very important to SaskPower as we plan to potentially incorporate nuclear power into the generation mix. Engagement and consultation with Indigenous Rightsholders and the public is critical to this project, and I encourage the people of Saskatchewan to reach out and engage with us on this important project.”
     The utility noted that a final decision on whether or not to build an SMR will not be made until 2029. However, it said that to keep a nuclear power option for Saskatchewan, significant planning and regulatory work must be done. It also said that “A necessary step to advance this regulatory work is to identify and select a location to potentially host an SMR.”
     Don Morgan is the Minister Responsible for SaskPower. He said, “By identifying these two study areas, SaskPower has reached another critical milestone in its planning work to potentially bring nuclear power to Saskatchewan. Saskatchewan's commitment to a sustainable, reliable, and affordable electrical system is evident with today's announcement.”
     Last June, SaskPower selected GE Hitachi Nuclear Energy's BWRX-300 SMR for possible deployment in the province in the mid-2030s after an assessment process in which it looked at several SMR technology.
      Although all of Canada’s uranium production currently comes from Saskatchewan, the province does not use nuclear power. Saskatchewan’s government identified development of SMR technology as a goal for growth in its 2019 development roadmap. Earlier this year, along with the governments of Ontario, Saskatchewan, New Brunswick and Alverta, it released a joint strategic plan setting out a path for developing and deploying SMRs.
     OPG has already selected the GE-Hitachi BWRX-300 for their Darlington New Nuclear Project in Ontario, where Canada’s first commercial, grid-scale, SMR could be completed as early as 2028.

 

 

 

Nuclear News Roundup Sep 23, 2022

UN chief calls for an end to ‘nuclear blackmail’ and risk of ‘humanitarian Armageddon’ news.un.org

Ashtabula County EMA participates in Perry Nuclear Power Plant drill starbeacon.com

Nuclear threats hang over Europe as weapons leaders gather in Brussels politico.com

Stoltenberg warns Russia against "nuclear" rhetoric: Such war cannot be won Ukrinform.net

 

 

 

Ambient office = 73 nanosieverts per hour

Ambient outside = 107 nanosieverts per hour

Soil exposed to rain water = 108 nanosieverts per hour

Red bell pepper from Central Market = 100 nanosieverts per hour

Tap water = 105 nanosieverts per hour

Filter water = 83 nanosieverts per hour

Geiger Readings for Sep 23, 2022

Latitude 47.704656 Longitude -122.318745

Ambient office = 73 nanosieverts per hour

Ambient outside = 107 nanosieverts per hour

Soil exposed to rain water = 108 nanosieverts per hour

Red bell pepper from Central Market = 100 nanosieverts per hour

Tap water = 105 nanosieverts per hour

Filter water = 83 nanosieverts per hour

Nuclear Reactors 1065 - Combining Artificial Intelligence and Nuclear Power - Part 2 of 2 Parts

Part 2 of 2 Parts (Please read Part 1 first)
Nuclear Power
     Nuclear power is a reliable, low carbon energy source. It can benefit significantly from the incorporation of AI. By combining digital simulations of real nuclear with AI systems, the nuclear industry can optimize complex procedures and improve reactor design, performance and reduce maintenance costs.
     Machine learning is a process in which AI systems learn by analyzing huge amounts of data. It can help to automate tasks and thereby increase reliability and avoid errors. In addition, AI has considerable analytical and predictive potential to help monitor power plant processes and detect anomalies.
Nuclear Security and radiation protection
     More and more countries have chosen to employ nuclear technology for peaceful purposes and adopt power programs. The IAEA works continuously to ensure the protection of people and the environment from the potential harmful effects of ionizing radiation.
     AI can contribute to nuclear security and safety in a variety of ways. It can be used in the processing and analysis of data from radiation detection systems to enhance the detection and identification of nuclear and other radioactive material. AI can be applied to analyze data from physical protection systems to improve the detection of intruders at nuclear facilities. It can also assist in spotting anomalies that could indicate a cyber-attack on a nuclear facility. In the realm of radiation protection, the integration of AI in safety standards-related software can reinforce the protection of the millions of workers with occupational exposure in medicine, construction, mining, shipping, agriculture and nuclear power.
Safeguards
     Safeguards are technical verification measures that allows the IAEA to provide credible assurances that countries are honoring their legal obligations to use nuclear material for peaceful purposes only. The IAEA analyzes countries’ declared nuclear material and nuclear related activities. It then attempts to verify the absence of undeclared materials and activities through measures, such as inspections at nuclear facilities and sites.
     Safeguards rely on huge amounts of data obtained by various means. These include satellite imagery, environmental sampling, gamma ray spectroscopy and video surveillance. AI can assist nuclear inspectors and safeguard analysts by the analysis of collected data. Machine learning methods have already been used to detect outliers in large datasets. These methods assist in verifying spent fuel and analyzing surveillance recordings. AI is expected to further improve the efficiency of safeguard implementation by reducing the number of repetitive tasks that have to be performed by inspectors.
Final thoughts on the future
     The IAEA provides interdisciplinary forums for professionals to discuss and foster collaboration on the use of AI in nuclear applications, science and technology. It is committed to sharing knowledge and forging partnerships through its AI for Atoms platform. As part of this initiative, the IAEA collaborates with the International Telecommunication Union, the UN Interagency Working Group on AI and forty other UN organizations to provide a solid foundation for accelerated sustainable development of AI. (The AI for Good program is a year-round digital platform of the U.N. system. On the platform, AI innovators and problem owners learn, discuss and connect to identify practical AI solutions to advance U.N. Sustainable Development Goals.)