Next Article in Journal
Special Issue on “Machine-Learning-Assisted Intelligent Processing and Optimization of Complex Systems”
Previous Article in Journal
Unveiling the Latest Breakthroughs in Menaquinone-7 Research through Fermentation-Based Production
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Processes
Volume 11
Issue 9
10.3390/pr11092594
processes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure

by
Jun Wang
1,
Lun Sun
1,*,
Heng Li
2,
Ruoxi Ding
3 and
Ning Chen
1
1
School of Energy and Power Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
2
State Grid Henan Provincial Power Company Xinyang Power Supply Company, Xinyang 464000, China
3
East China Architectural Design & Research Institute Company, Shanghai 200011, China
*
Author to whom correspondence should be addressed.
Processes 2023, 11(9), 2594; https://doi.org/10.3390/pr11092594
Submission received: 21 July 2023 / Revised: 24 August 2023 / Accepted: 28 August 2023 / Published: 30 August 2023
Browse Figures
Review Reports Versions Notes

Abstract

Heat exchangers in operation often experience scaling, which can lead to a decrease in heat exchange efficiency and even safety accidents when fouling accumulates to a certain thickness. To address this issue, manual intervention is currently employed to monitor fouling thickness in advance. In this study, we propose a two-layer LSTM neural network model with an attention mechanism to effectively learn fouling thickness data under different working conditions. The model accurately predicts the scaling thickness of the heat exchanger during operation, enabling timely human intervention and ensuring that the scaling remains within a safe range. The experimental results demonstrate that our proposed neural network model (TA-LSTM) outperforms both the traditional BP neural network model and the LSTM neural network model in terms of accuracy and stability. Our findings provide valuable technical support for future research on heat exchanger descaling and fouling growth detection.
Keywords: heat exchanger fouling; neural network; deep learning; attention mechanism; two-layer LSTM heat exchanger fouling; neural network; deep learning; attention mechanism; two-layer LSTM

Share and Cite

MDPI and ACS Style

Wang, J.; Sun, L.; Li, H.; Ding, R.; Chen, N. Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure. Processes 2023, 11, 2594. https://doi.org/10.3390/pr11092594

AMA Style

Wang J, Sun L, Li H, Ding R, Chen N. Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure. Processes. 2023; 11(9):2594. https://doi.org/10.3390/pr11092594

Chicago/Turabian Style

Wang, Jun, Lun Sun, Heng Li, Ruoxi Ding, and Ning Chen. 2023. "Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure" Processes 11, no. 9: 2594. https://doi.org/10.3390/pr11092594

APA Style

Wang, J., Sun, L., Li, H., Ding, R., & Chen, N. (2023). Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure. Processes, 11(9), 2594. https://doi.org/10.3390/pr11092594

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop