Using Hopfield neural network to optimize fuel rod loading patterns in VVER/1000 reactor by applying axial variation of enrichment distribution

Tayefi, Shima; Pazirandeh, Ali

doi:10.1016/j.asoc.2014.03.018

Cited by 16 publications

(2 citation statements)

References 14 publications

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“…Pazirandeh and Tayefi [ 44 ] employed a Hopfield artificial neural network (HANN) to obtain a suitable power peaking factor and evaluate the effective multiplication factor for VVER-1000 reactors in order to optimize fuel management. Tayefi and Pazirandeh [ 45 ] used HANN to determine the best axial variation distributions of enrichment in order to reach the flattening neutronic flux and guarantee safe operation. Radaideh, Wolverton [ 46 ] successfully applied RL to the optimization of fuel assemblies by establishing a connection between RL and the strategy of moving the fuel rods to meet specific constraints.…”

Section: Application Of Ai To Nuclear Reactor Design Optimizationmentioning

confidence: 99%

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

Huang

Peng

Deng

et al. 2023

Heliyon

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Section: Application Of Ai To Nuclear Reactor Design Optimizationmentioning

confidence: 99%

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

Huang

Peng

Deng

et al. 2023

Heliyon

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“…Feil et al presented semi‐mechanistic models for state‐estimation–soft sensor of polymer MI. As neural network (NN) model performs excellently in approximating any continuous nonlinear systems , it has been brought into the MI prediction. Shi et al developed a soft sensor model for MI prediction based on radial basis function (RBF) NN.…”

Section: Introductionmentioning

confidence: 99%

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

Zhang

Liu

2016

Journal of Chemometrics

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The accurate and reliable real‐time prediction of melt index (MI) is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a real‐time soft sensor based on optimized least squares support vector machine (LSSVM) for MI prediction. First, the hybrid continuous ant colony differential evolution algorithm (HACDE) is proposed to optimize the parameters of LSSVM. Then, considering the complexity and nondeterminacy of PP plant, an online correcting strategy (OCS) is adopted to update the modeling data and to revise the model's parameters adaptively. Thus, the real‐time prediction model, HACDE‐OCS‐LSSVM, is obtained. Based on the data from a real PP plant, the models of HACDE‐LSSVM, DE‐LSSVM and LSSVM are also developed for comparison. The research results show that the proposed real‐time model achieves a good performance in the practical industrial MI prediction process. Copyright © 2016 John Wiley & Sons, Ltd.

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Synchronization of a Class of Improved Neural Networks Based on Periodic Intermittent Control

Jiang

et al. 2017

Neural Process Lett

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Using Hopfield neural network to optimize fuel rod loading patterns in VVER/1000 reactor by applying axial variation of enrichment distribution

Cited by 16 publications

References 14 publications

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

A review of the application of artificial intelligence to nuclear reactors: Where we are and what's next

A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

Synchronization of a Class of Improved Neural Networks Based on Periodic Intermittent Control

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