Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine
In this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under t...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
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PeerJ Inc.
2023-02-01
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Series: | PeerJ Computer Science |
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Online Access: | https://peerj.com/articles/cs-1218.pdf |
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author | Manli Lv Jianping Zhao Shengxian Cao Tao Shen Zhenhao Tang |
author_facet | Manli Lv Jianping Zhao Shengxian Cao Tao Shen Zhenhao Tang |
author_sort | Manli Lv |
collection | DOAJ |
description | In this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under typical working conditions. Based on the air distribution mode, the simulation results are divided into six subclasses. Then the K-means clustering method is applied to find out the benchmark working condition of each subclass. Moreover, the random sampling method is used to extract samples to reduce computational complexity. Modeling inputs are selected according to the CFD boundary conditions and combustion mechanisms, and data sets are reconstructed based on the increments of each actual working condition from the benchmark working condition. Finally, an IDBN-based prediction model is built in each subclass. The experimental results show that the IDBN-based model has a promising predictive ability with less than 11% symmetric mean absolute percentage error. |
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institution | Directory Open Access Journal |
issn | 2376-5992 |
language | English |
last_indexed | 2024-04-10T09:29:19Z |
publishDate | 2023-02-01 |
publisher | PeerJ Inc. |
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spelling | doaj.art-eacdf7c09b564dedb13bd4966d093ac92023-02-19T15:05:06ZengPeerJ Inc.PeerJ Computer Science2376-59922023-02-019e121810.7717/peerj-cs.1218Prediction of temperature distribution in a furnace using the incremental deep extreme learning machineManli Lv0Jianping Zhao1Shengxian Cao2Tao Shen3Zhenhao Tang4College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaCollege of Computer Science and Technology, Changchun University of Science and Technology, Changchun, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin, ChinaHarbin Boiler Company Limited, Harbin, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin, ChinaIn this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under typical working conditions. Based on the air distribution mode, the simulation results are divided into six subclasses. Then the K-means clustering method is applied to find out the benchmark working condition of each subclass. Moreover, the random sampling method is used to extract samples to reduce computational complexity. Modeling inputs are selected according to the CFD boundary conditions and combustion mechanisms, and data sets are reconstructed based on the increments of each actual working condition from the benchmark working condition. Finally, an IDBN-based prediction model is built in each subclass. The experimental results show that the IDBN-based model has a promising predictive ability with less than 11% symmetric mean absolute percentage error.https://peerj.com/articles/cs-1218.pdfTemperature distributionIDELM algorithmCFD simulationK-means clustering |
spellingShingle | Manli Lv Jianping Zhao Shengxian Cao Tao Shen Zhenhao Tang Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine PeerJ Computer Science Temperature distribution IDELM algorithm CFD simulation K-means clustering |
title | Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine |
title_full | Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine |
title_fullStr | Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine |
title_full_unstemmed | Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine |
title_short | Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine |
title_sort | prediction of temperature distribution in a furnace using the incremental deep extreme learning machine |
topic | Temperature distribution IDELM algorithm CFD simulation K-means clustering |
url | https://peerj.com/articles/cs-1218.pdf |
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