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Two steps forward for U.K. advanced nuclear
This week, two significant announcements have emerged from the United Kingdom’s advanced reactor sector.
On June 14, Rolls-Royce, the United Kingdom National Nuclear Laboratory, and the Japan Atomic Energy Agency announced that they had signed two trilateral memorandums of cooperation to collaborate on “advanced modular reactor (AMR) technology, specifically high-temperature gas-cooled reactors (HTGR), and the coated particle fuel these reactors will use.”
Separately, on June 16, Bellevue, Wash.–based TerraPower announced that its Natrium reactor design has been formally submitted for U.K. regulatory review. The company also announced the formation of a new subsidiary, TerraPower UK Ltd.
Jeeyea Ahn, Junyong Bae, Seung Jun Lee (UNIST)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 115-121
The most significant factor in nuclear power plant operations is safety. A lot of people in the nuclear industry have continued their unremitting efforts. After Three Miles Island accident, human factors came out into the open that it greatly contributes to the course of the accident of nuclear power plants. Thus, a lot of efforts have been made to reduce the human factor error. As nuclear power plant design developed, a new type of digitalized main control rooms has appeared, the conventional paper-based procedures have been left behind as backup. In advanced main control rooms (MCRs), computerized procedure system (CPS) is used to support human operators. Applying computer-based procedures in the main control room allows to reduce mental workload, enhance situation awareness, and produce lower errors of omission than paper-based procedure. However, current CPS does not yet utilize artificial intelligence technology. In order to reduce human errors, the framework which detects unsafe acts of human operators is suggested. The unsafe acts (UAs) detecting system implements Coloured Petri Nets, and deep neural networks to determine if an operating action is an error. The system uses two steps of filters to discover the effect of an operating action on the plant integrity.