CityU Seminar on Design of Accident Prevention System for LWR using ANN and HS Simulator

The seminar entitled “Design of an Accident Prevention System for Light Water Reactors Using the Artificial Neural Network and a Highspeed Simulator” was organized by Department of Mechanical Engineering, CityU on 27^th Feb 2019.  Seven years after Fukushima, the last missing pieces of the jigsaw puzzle of light water reactor (LWR) safety have been put together. In the United States, the nuclear power industry has implemented a diverse and flexible strategy, FLEX (NEI 2012), for severe accidents.  The system makes rapid predictions using a high-speed simulation code and artificial intelligence (AI) diagnostics was proposed by Dr. Li-Chi Cliff Po.  I would like to summarize today speech for sharing below.

 In the beginning, Dr. Li-Chi Cliff Po (Founder president of Micro-Simulation Technology) introduced his company “Micro-Simulation Technology” which established in 1985 and had developed PC-Windows based nuclear power plant simulator PCTRAN.

Then Dr. Po briefed “Risks” for Nuclear Power Plant Safety and discussed the deficiencies on Nuclear plant of Probabilistic Risk Assessment (PRA).  He proposed to use Artificial Intelligence (AI) machine learning of analyzed cases to build diagnosis algorithms and using Fast-speed simulation software to predict consequence way ahead actual damage.  Dr. Po also told us some severe accident in LWR under different situations.

After that he demonstrated the PC TRAN CRP1000 DBA and Severe Accident Simulator as follows.  

Changed the failure fraction to 2000%!

The core of reactor was very hot!

And then Dr. Po quoted Ayo-Imoru R.M. and Cilliers A.C. (2018) paper named “Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant” to the introduction of a machine-learning tool like artificial neural networks (ANN) to train both the simulator and plant parameters.

Finally, he introduced that the US nuclear industry, working through the Nuclear Energy Institute (NEI), developed a framework for addressing this order, as documented in NEI 12-06 "Diverse and Flexible Coping Strategies (FLEX) Implementation Guide," published in August 2012.  SAFER program and equipment for accident was mentioned.

Lastly, newly developed ultimate response guidelines for BWR was also discussed.

At the end, Dr. Li-Chi Cliff Po concluded that not just for training and education, but also the entire technical staff entering into the industry.  US FLEX and SAFER programs were merely documents and methodologies that no practical hardware and/or software.  Online AI Accident Prevention System is the first of a kind.  That could be considered to resolve Inland Nuclear Construction Concerns.

Q&A Session

Dr. BL Luk and I had asked about the training of ANN for LWR because of limited cases data.  *From Ayo-Imoru’s paper (2018), the nuclear plant outputs are compared with the simulator outputs in steady state. Based on the results from the first stage, in the second stage the plant and simulator outputs are passed through an ANN, which are then compared for better fault identification in transient.

Reference:

Department of Mechanical Engineering, CityU - http://www.cityu.edu.hk/mne/

Seminar leaflet - http://www.cityu.edu.hk/mne/pdf/mneseminar1819_25.pdf

Ayo-Imoru R.M. and Cilliers A.C. (2018) “Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant” Annals of Nuclear Energy, 118, pp.61-70.

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