A nuclear reactor core fuel reload optimization using artificial ant colony connective networks

Lima, Alan Miranda Monteiro de; Schirru, Roberto; Silva, Fernando Carvalho da; Medeiros, J.A.C.C.

doi:10.1016/j.anucene.2008.03.002

Cited by 57 publications

(11 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…De Lima et al [240] applied to the refuelling problem a technique of optimization known as Artificial Ant Colony Connective Networks. This is a technique inspired by biology (this phenomenon in computing or in robotics is known as biomimetism); in particular, it is inspired by pheromones in biology [241,242].…”

Section: A Survey Of the Application Of Other Techniques: Genetic Fumentioning

confidence: 99%

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

View full text Add to dashboard Cite

An important aspect of managing a nuclear reactor is how to design refuellings, and from the 1980s to the present different artificial intelligence (AI) techniques have been applied to this problem. A section of the reactor core resembles a symmetrical grid; long fuel assemblies are inserted there, some of them new, some of them partly spent. Rods of “burnable poisons” dangle above, ready to be inserted into the core, in order to stop the reactor. Traditionally, manual design was made by shuffling positions in the grid heuristically, but AI enabled to automatically generate families of candidate configurations, under safety constraints, as well as in order to optimize combustion, with longer cycles of operation between shutdown periods, thus delaying the end-of-cycle point (except in France, where shutdown is on an annual basis, and Canada, where individual fuel assemblies are replaced, with no need for shutdown for rearranging the entire batch). Rule-based expert systems, the first being FUELCON,1 were succeeded by projects combining neural and rule-based processing (a symbolic-to-neural compilation of rules we did not implement), and later on, genetic algorithms in FUELGEN.2 In the literature, one also comes across the application of fuzzy techniques, tabu search, cellular automata and simulated annealing, as well as particle swarms. Safety regulations require simulating the results using a parameter prediction tool; this is done using either nodal algorithms, or neural processing.

show abstract

Section: A Survey Of the Application Of Other Techniques: Genetic Fumentioning

confidence: 99%

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Nissan

2019

Designs

View full text Add to dashboard Cite

show abstract

“…Since the pioneering work of Kropaczek and Turinsky (1991), other researchers, including Mahlers (1994), Smuc et al (1994) and Stevens et al (1995), have developed SA variants to optimize PWR LPs or applied other stochastic/heuristic optimization methods to this problem and/or the closely related boiling water reactor (BWR) LP optimization problem. These other methods have included: genetic algorithms (GAs) (Poon and Parks, 1993, DeChaine and Feltus, 1995, Chapot et al, 1999, François and López, 1999, Ortiz and Requena, 2004; estimation of distribution algorithms (Jiang et al, 2006); ant colony optimization (De Lima et al, 2008, Esquivel-Estrada et al, 2011, Wang and Lin, 2009, Lin and Lin, 2012; particle swarm optimization (Alvarenga de Moura et al, 2009, Khoshahval et al, 2010, Liu and Cai, 2012; and harmony search (Poursalehi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Pressurized water reactor in-core nuclear fuel management by tabu search

Hill

Parks

2015

Annals of Nuclear Energy

View full text Add to dashboard Cite

Optimization of the arrangement of fuel assemblies and burnable poisons when reloading pressurized water reactors has, in the past, been performed with many different algorithms in an attempt to make reactors more economic and fuel efficient. The use of the tabu search algorithm in tackling reload core design problems is investigated further here after limited, but promising, previous investigations. The performance of the tabu search implementation developed was compared with established genetic algorithm and simulated annealing optimization routines. Tabu search outperformed these existing programs for a number of different objective functions on two different representative core geometries.

show abstract

“…Arrefecimento Simulado (Kropaczek & Turinsky, 1991), Busca Tabu (Lin et al, 1998), Algoritmos Genéticos (Chapot et al, 1999), Aprendizado Incremental Baseado em Populações (Schirru et al, 2006), Otimização com Colônia de Formigas (de Lima et al, 2008), Otimização com Colônia de…”

Section: Introductionunclassified

Computação evolucionária aplicada ao problema da recarga de reatores nucleares

Lima¹,

Nicolau²,

Oliveira³

et al. 2011

Computação Evolucionária Em Problemas De Engenharia

Self Cite

View full text Add to dashboard Cite

Resumo: O objetivo deste capítulo é apresentar o Problema da Recarga de Reatores Nucleares (PRRN), mostrando a viabilidade da utilização de alguns métodos da Inteligência Computacional na solução deste problema. Os métodos aqui abordados correspondem às estratégias de otimização por colônia de formigas, colônia de abelhas, enxames de partículas, algoritmos genéticos, além de uma abordagem com inspiração quântica para o algoritmo de colônia de formigas. São introduzidos os principais aspectos do PRRN, bem como as principais características de cada método utilizado. Os resultados apresentados comprovam a viabilidade e eciência destes métodos como técnicas de solução de problemas da área nuclear.

show abstract

A nuclear reactor core fuel reload optimization using artificial ant colony connective networks

Cited by 57 publications

References 11 publications

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

An Overview of AI Methods for in-Core Fuel Management: Tools for the Automatic Design of Nuclear Reactor Core Configurations for Fuel Reload, (Re)arranging New and Partly Spent Fuel

Pressurized water reactor in-core nuclear fuel management by tabu search

Computação evolucionária aplicada ao problema da recarga de reatores nucleares

Contact Info

Product

Resources

About