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Any nuclear site and its associated processes are meticulously screened by their staff to ensure correct operation within the licence boundaries. From electricity-generating sites to fuel cycle plants and shutdown facilities, many reports are generated. Some of those– especially in operating plants - are Condition Reports –part of the Corrective Action Program (CAP) in the US or with equivalent in any countries following WANO guidelines.
As fundamental blocks of the nuclear safety culture, CAP are part of the implementation and execution process for problem identification, tracking, resolution and continuous learning.
As an integral part of the life of a nuclear power plant, , Condition Reports are regularly generated and each year, a nuclear site can have about 20,000 reports to analyse – between 5,000 to 10,000 per reactor, which all need to be trended.
This process touches almost every person and process inside the organisation. It also happens to be a manually intensive process and costs each utility millions of dollars/euros/sterling in labour costs each year to run.
With the recent developments of machine learning – ML – and artificial intelligence – AI -, deploying technologies such CAP screening automation could definitely have a drastic and positive impact on resource allocation and ultimately costs of a nuclear site..
However, not everyone in the nuclear industry is familiar with the concept, or may only have a partial picture of the scope of that this type of automation can offer. Well, let us help you learn more about a very unique artificial intelligence platform tailored to the nuclear industry

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