Estimation of research reactor core parameters using cascade feed forward artificial neural networks

This communication primarily concentrates on developing reliable and accurate compositional oil formation volume factor ( B o ) models using several advanced and powerful machine learning (ML) models, namely, extra trees (ETs), random forest (RF), decision trees (DTs), generalized regression neural networks, and cascade-forward back-propagation network, alongside radial basis function and multilayer perceptron neural networks. Along with these models, seven equations of state (EoSs) were employed to estimate B o . The performance of the developed ML models and employed EoSs was assessed through various statistical and graphical evaluations. Overall, the ML models could provide much more accurate predictions in comparison to EoSs. However, the results indicated that tree-based models, specifically ET models, could outperform the other models and can be reliably applied for estimating B o . The most reliable ET model could predict B o with a total average error of 1.17%. Lastly, the outlier detection approach verified the dataset’s consistency detecting only 17 (out of 1224) data points as outliers for the proposed B o models.

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“… 40 However, the higher number of adjustable weights may result in higher computational costs. 41 Figure 4 depicts a typical CFBPN.…”

Section: Intelligent Methodsmentioning

confidence: 99%

“…Usually, CFBPNs can provide more accurate results with a lower number of hidden neurons in comparison to conventional ANNs . However, the higher number of adjustable weights may result in higher computational costs Figure depicts a typical CFBPN.…”

Section: Intelligent Methodsmentioning

confidence: 99%

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

et al. 2022

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“…In terms of prediction, Xia et al [35] introduced a real-time monitor system for the reactor's 3D power distribution supervision. Hedayat [36] estimated the reactor core parameters using cascade feedforward artificial neural networks. An online simulator that works on a fuzzy network model was described to predict transient behavior of an NPP [37]; In terms of fault detection, many researchers [38]- [44] introduced the applications of fault detection and diagnosis methods in different scenarios of NPPs.…”

Section: B Why and How Artificial Intelligence Can Benefit Nppsmentioning

confidence: 99%

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Lyu

Zhang

et al. 2020

IEEE Access

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Nuclear energy can make an important contribution to low-carbon energy supply for Industry 4.0, while Industry 4.0 can reform this industry in return. As a typical and complex man-machine-network integration system, various faults, insufficient automation and stressed human operators limit the further popularization of nuclear power plants (NPPs) while these issues can be addressed by the aid of artificial intelligence (AI) technologies. In this work, we try to present a systemic review of how AI can benefit NPPs in a top-to-down fashion. We discuss limitations in current NPPs and introduce the concept of Nuclear Power Plant Human-Cyber-Physical System (NPPHCPS) as the top-level design. Then, we category AI-related nuclear power applications into Physical-Plant-Centered and Human-Operator-Centered technologies and review research works from 7 typical NPP functional scenarios in the recent two decades. In each NPP functional scenario, how researchers integrate AI into NPPs is presented following timeline. We hope this review can be used as the guideline for NPPs' Design in the future and contribute to green Industry 4.0.

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“…All layers have biases. The last layer is the network output (Hedayat, Davilu, Barfrosh, & Sepanloo, 2009). (ii) The Elman NN (Fig.…”

Section: The Nn Architecture Designmentioning

confidence: 99%

Development of a model for urban heat island prediction using neural network techniques

Gobakis

Κολοκότσα

Synnefa

et al. 2011

Sustainable Cities and Society

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Estimation of research reactor core parameters using cascade feed forward artificial neural networks

Cited by 58 publications

References 13 publications

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Compositional Modeling of the Oil Formation Volume Factor of Crude Oil Systems: Application of Intelligent Models and Equations of State

Nuclear Power Plants With Artificial Intelligence in Industry 4.0 Era: Top-Level Design and Current Applications—A Systemic Review

Development of a model for urban heat island prediction using neural network techniques

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