Heejin Kim, Eunseok Sim, Gbadago Dela Quarme, Sungwon Hwang
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Data-Driven Predictive Maintenance for Heat Exchangers: Real-Time Monitoring and Long-Term Performance Prediction Using Integrated ML Models
This study addresses the high maintenance costs of heat exchangers in petrochemical processes by developing a deep learning-based 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.
期刊介绍:
The Korean Journal of Chemical Engineering provides a global forum for the dissemination of research in chemical engineering. The Journal publishes significant research results obtained in the Asia-Pacific region, and simultaneously introduces recent technical progress made in other areas of the world to this region. Submitted research papers must be of potential industrial significance and specifically concerned with chemical engineering. The editors will give preference to papers having a clearly stated practical scope and applicability in the areas of chemical engineering, and to those where new theoretical concepts are supported by new experimental details. The Journal also regularly publishes featured reviews on emerging and industrially important subjects of chemical engineering as well as selected papers presented at international conferences on the subjects.