Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

Manli Lv; Jianping Zhao; Shengxian Cao; Tao Shen; Zhenhao Tang

doi:10.7717/peerj-cs.1218

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

PeerJ Comput Sci. 2023 Feb 17:9:e1218. doi: 10.7717/peerj-cs.1218. eCollection 2023.

Authors

Manli Lv^{1

2}, Jianping Zhao¹, Shengxian Cao², Tao Shen³, Zhenhao Tang²

Affiliations

¹ College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
² School of Automation Engineering, Northeast Electric Power University, Jilin, China.
³ Harbin Boiler Company Limited, Harbin, China.

Abstract

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.

Keywords: CFD simulation; IDELM algorithm; K-means clustering; Temperature distribution.

Associated data

figshare/10.6084/m9.figshare.21828354.v2

Grants and funding

This work was supported by the Jilin Science and Technology Project under grant 20200401085GX. There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.