Articles & Issues
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received January 2, 2025
Revised May 7, 2025
Accepted May 29, 2025
Available online August 25, 2025
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
All issues
Data‑Driven Predictive Maintenance for Heat Exchangers: Real‑Time Monitoring and Long‑Term Performance Prediction Using Integrated ML Models
https://doi.org/10.1007/s11814-025-00493-2
Abstract
This study addresses the high maintenance costs of heat exchangers in petrochemical processes by developing a deep learningbased
predictive maintenance (PdM) model for performance monitoring and scheduling. Using a mathematical model, the
overall heat transfer coefficient (U) was derived to evaluate heat exchanger performance, resulting in a performance indicator
(DI). An artificial neural network-genetic algorithm (ANN-GA) technique was employed to create a real-time DI prediction
model based on industrial process data. A long short-term memory (LSTM) model was then used to predict heat exchanger
performance over 3 days using short-term operating data (12 h). The model's hyperparameters were optimized, achieving a
real-time monitoring model with a mean absolute percentage error (MAPE) of 0.59% and a maintenance-cycle prediction
model with an MAPE of 2.41%. This integrated system, akin to soft sensors, accurately predicted a 72-h performance profile
using 12-h history data owing to our implemented data augmentation strategies, demonstrating robustness and potential for
improving uptime and maintenance scheduling.
