Fouling degrades the thermal and hydraulic performances of the heat exchanger (HE), leading to failure if undetected. It occurs due to the accumulation of undesired material on the heat transfer surface. Knowledge about the HE fouling dynamics is required to plan mitigation strategies, ensuring a sustainable and safe operation. This paper aims to propose a feature-based technique to predict the fouling status of the HE based on historical data. Three thermal and two hydraulic features are extracted from the HE. Random forest (RF) is employed to detect the dominant features using the Gini index. These dominant features are used to predict the fouling resistance using a deep neural network based on the Long Short-Term Memory (LSTM) model. Also, these dominant features provide reliable inferences to reason out the fouling dynamics. A diagnostic flag is derived based on the dominant feature and is used to diagnose the ongoing fouling phenomena, which is vital to formulate mitigation. The proposed technique is investigated on the data acquired from the HEs used in a thermal power plant (Case 1) and petroleum refinery (Case 2). Prediction accuracy of 99% and 97% is observed for Cases 1 and 2, respectively. Experimental results illustrate that RF enables the LSTM to achieve faster training and reliable prediction of fouling.

中文翻译：

---

基于 RF-LSTM 的换热器结垢预测与诊断方法

结垢会降低热交换器 (HE) 的热性能和水力性能，如果未被发现，则会导致故障。它的发生是由于在传热表面上堆积了不需要的材料。需要了解 HE 结垢动态来规划缓解策略，确保可持续和安全的运行。本文旨在提出一种基于特征的技术，以根据历史数据预测 HE 的结垢状态。从 HE 中提取了三个热特征和两个水力特征。随机森林 (RF) 用于使用基尼指数检测主要特征。这些主要特征用于使用基于长短期记忆 (LSTM) 模型的深度神经网络来预测抗污能力。此外，这些主要特征提供了可靠的推论来推断结垢动态。诊断标志是基于主要特征得出的，用于诊断正在进行的结垢现象，这对于制定缓解措施至关重要。所提出的技术是根据从热电厂（案例 1）和炼油厂（案例 2）中使用的 HE 获得的数据进行研究的。对于案例 1 和案例 2，分别观察到 99% 和 97% 的预测准确度。实验结果表明，RF 使 LSTM 能够实现更快的训练和可靠的污染预测。