Optimization of fuel reload for a BWR using neural networks and genetic algorithms

被引：0

作者：

Servin, JJO ^{[1
]}

Ramos, IR ^{[1
]}

机构：

[1] Inst Nacl Invest Nucl Mexico, Mexico City, DF, Mexico

来源：

INTELLIGENT TECHNIQUES AND SOFT COMPUTING IN NUCLEAR SCIENCE AND ENGINEERING | 2000年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this work we use a backpropagation neural network (NN) and a genetic algorithm (GA) to optimize the nuclear fuel reload in a BWR reactor. The NN was trained with a fuel reload set evaluated previously with a reactor simulator code [1,2], to estimate the k(eff) and thermal limits values and other parameters. The GA generates fuel reload patterns searching the optimal one. We use two criteria to qualify a fuel reload: the requirements or constraints at the Beginning of the cycle (BOC) and at the End of the cycle (EOC). From a theoretical point of view the fuel reloads satisfies the operational constraints for a BWR reactor similar to Laguna Verde Nuclear Power Plant in Mexico.

引用

页码：512 / 518

页数：7

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