A new Cross-section calculation method in HTGR engineering simulator system based on Machine learning methods

Li, Zeguang; Sun, Jun; Wei, Chunlin; Shi, Zhe; Qian, Xiaoye

doi:10.1016/j.anucene.2020.107553

Cited by 10 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The developed ANN model successfully predicts the condition of oil and gas pipelines with average percent validity between 97 and 98 %. Zeguang Li et al [96] proposed a new method for calculating the cross section of high-temperature gas-cooled reactor (HTGR) using machine learning. The author used deep neuron network to study the non-linear relationships between reactor parameters and cross section and also used tree regression to accurately attain the cross-section.…”

Section: [90] Bp (Ann)mentioning

confidence: 99%

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

View full text Add to dashboard Cite

The field of machine learning has proven to be a powerful approach in smart manufacturing and processing in the chemical and process industries. This review provides a systematic overview of current state of artificial intelligence and machine learning and their applications in textile, nuclear power plant, fertilizer, water treatment, and oil and gas industries. Moreover, this study reveals the current dominant machine learning methods, pre and post processing of models, increased utilization of machine learning in terms of fault detection, prediction, optimization, quality control, and maintenance in these sectors. In addition, this review gives the insight into the actual benefits and impact of each method, and complications in their extensive deployment. Finally in the current impressive state, challenges, future development in terms of algorithm and infrastructure aspects are highlighted.

show abstract

Section: [90] Bp (Ann)mentioning

confidence: 99%

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

View full text Add to dashboard Cite

show abstract

“…More recently, these techniques have been applied to other reactor types including high temperature gas cooled reactors [24][25][26][27], molten salt reactors [28], and research reactors [29][30][31][32]. A more complete summary of the open-ended nuclear design R&D that predates and motivated this work are provided in Section 2.1.…”

Section: Artificial Intelligence For Nuclear Engineeringmentioning

confidence: 99%

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

Ade¹,

Hiscox²,

Popov³

et al. 2021

View full text Add to dashboard Cite

High-fidelity resonance self-shielding calculation

Cao

Zhang

et al. 2023

Resonance Self-Shielding Calculation Methods in Nuclear Reactors

View full text Add to dashboard Cite

A new Cross-section calculation method in HTGR engineering simulator system based on Machine learning methods

Cited by 10 publications

References 9 publications

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

High-fidelity resonance self-shielding calculation

Contact Info

Product

Resources

About