SMART_TC: an R&D Programme on uses of artificial intelligence techniques for tritium monitoring in complex ITER-like tritium plant systems

被引：2

作者：

Iraola, Eduardo ^{[1
,2
]}

Sedano, Luis ^{[3
]}

Nougues, Jose M. ^{[1
]}

Feliu, Josep A. ^{[1
]}

Coya, Bruno ^{[2
]}

Batet, Lluis ^{[2
]}

机构：

[1] Inproc Technol & Consulting Grp, Gran Via Carles III 86, Barcelona 08028, Spain

[2] Univ Politecn Cataluna, Dept Phys, BarcelonaTech UPC, ETSEIB, Diagonal 647, Barcelona 08028, Spain

[3] FUS ALIANZ Sci Engn & Consulting, C Nord 19, Tarragona 43700, Spain

来源：

FUSION ENGINEERING AND DESIGN | 2021年 / 166卷

关键词：

Tritium; ITER; Artificial intelligence; Machine learning; Fault detection and diagnosis; FAULT-DETECTION; DIAGNOSIS;

D O I：

10.1016/j.fusengdes.2021.112409

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

The realization of nuclear fusion energy is nowadays based on the concept of tritium breeding and the success of the ITER experiment. The latter relies today on a static monitoring approach to fulfill the emission limits imposed by the regulatory institutions. Artificial intelligence applications for fault diagnosis and process monitoring anticipate potential for the dynamic management of tritium in complex plant systems. This paper explores the dynamic tritium inventory management issue in complex systems, reviews the diverse artificial intelligence techniques and discusses the most promising approaches for ITER-like plant system match balance monitoring.

引用

页数：6