Data-Driven Predictive Maintenance for Heat Exchangers: Real-Time Monitoring and Long-Term Performance Prediction Using Integrated ML Models

IF 3.2 4区 工程技术 Q2 CHEMISTRY, MULTIDISCIPLINARY
Heejin Kim, Eunseok Sim, Gbadago Dela Quarme, Sungwon Hwang
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Abstract

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.

Abstract Image

数据驱动的热交换器预测性维护:使用集成ML模型的实时监测和长期性能预测
本研究通过开发一种基于深度学习的预测性维护(PdM)模型,用于性能监控和调度,解决了石化过程中热交换器的高维护成本问题。通过建立数学模型,推导出换热器的总传热系数U来评价换热器的性能,从而得到换热器的性能指标DI。采用人工神经网络遗传算法(ANN-GA)建立了基于工业过程数据的DI实时预测模型。然后使用长短期记忆(LSTM)模型使用短期运行数据(12 h)预测换热器在3天内的性能。对模型的超参数进行了优化,得到了平均绝对百分比误差(MAPE)为0.59%的实时监测模型和MAPE为2.41%的维修周期预测模型。由于我们实施了数据增强策略,该集成系统类似于软传感器,可以使用12小时历史数据准确预测72小时的性能概况,展示了鲁棒性和改善正常运行时间和维护计划的潜力。
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来源期刊
Korean Journal of Chemical Engineering
Korean Journal of Chemical Engineering 工程技术-工程:化工
CiteScore
4.60
自引率
11.10%
发文量
310
审稿时长
4.7 months
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.
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