Nuclear energy can make an important contribution to the low-carbon energy supply of Industry 4.0, and in return Industry 4.0 can transform the industry. As a typical and complex man-machine network integrated system, various obstacles, poor automation, and stressed human operators limit the further penetration of nuclear power plants (NPPs). Together, these problems can be solved with artificial intelligence (AI) technology.
Advances in advanced information technologies such as the Internet of Things, cloud computing, and artificial intelligence (AI) are enabling the Industry 4.0 vision. 4.0 represents the Fourth Industrial Revolution to achieve advanced automation levels. Nuclear energy can further contribute to the low-carbon energy supply of Industry 4.0, and in return Industry 4.0 can also transform the industry. A nuclear power system is a typical man-machine-network integrated system, and its research and development, construction, operation, etc. are sufficiently complicated.
Today’s nuclear power plants (NPPs) face three major barriers and risks. they are:
- First, as a complex system, NPP’s equipment, devices, or processes can experience various failures and failures, and these errors can have a significant impact on NPP’s performance and security.
- Secondly, another concern is the insufficient level of automation in nuclear power plant management. After decades of development, nuclear power plants were first digitized, but most nuclear power plants still use many traditional operation and control methods, which reduce operating efficiency and Increased risk of accidents.
- For the above reasons, human operators of nuclear power plants are under greater pressure due to sophisticated control requirements. Human factors engineering is important and has received a lot of attention in nuclear power plant design.
AI will play a key role in removing these barriers and developing top-level designs for future nuclear power plants.
Current AI-based Applications in Nuclear Power Plants
Nuclear fuel is the core material of nuclear power plants and is involved in the safe and reliable operation of nuclear power plants. In order to ensure the quality of fuel composition and prevent nuclear fuel accidents, it is necessary to standardize and systematically manage nuclear fuel. AI technology is utilized to efficiently manage and process nuclear fuel, and several simulation experiments are being conducted.
With the digitization of nuclear power plants, more and more nuclear data will be generated. How to deal with them is especially important for better management and maintenance of nuclear power plants. AI techniques such as neural networks are widely used in nuclear data processing and lifetime assessment of nuclear power plants. However, there are several problems, such as difficulty in quantifying nuclear data, low diagnostic efficiency and accuracy, uniformity of lifetime metrics, and impact of manpower on nuclear power plants.
Autonomous control is like a symbol of the realization of advanced automation. If applied, this could greatly alleviate the problem of poor automation. Less human involvement and intervention is believed to reduce the potential for error, increase efficiency, and improve the economics of nuclear power plant operation.
Fault detection and diagnosis (FDD) is an important research area for nuclear power plant safety. Currently, NPP suffers from over-intervention of human operators and inaccuracies and inefficiencies of his FDD. With the development of AI and other related technologies, more and more techniques are being applied to FDD.
Apart from physical plant-centric technology, AI also plays a key role in improving human operator-centric technology. Human-Machine Interaction (HMI) is a typical scenario in nuclear power plants, as human operators receive and make decisions based on data collected from plant sensors and devices. Very important. Well-designed in the control room, his HMI helps reduce operator errors and ensure NPP security.
Conclusion
There are still many other problems to solve when it comes to applying AI to nuclear power plants. For example, the interpretability of the model is low. Many AI models are “black boxes” with end-to-end architectures, making them difficult to describe and understand. Similarly, the generalization of the model should be further strengthened. Many current AI models are designed specifically for scenarios such as human face recognition, but we want to be able to generalize our models to similar scenarios.
