化工学报2025，Vol.76Issue(4)：1671-1679,9.DOI:10.11949/0438-1157.20241137
基于CNN-LSTM的换热器污垢因子预测研究
Prediction of scale factor of heat exchangers based on CNN-LSTM neural network
摘要
Abstract
Accurate prediction of the fouling state of the heat exchanger can timely understand the degree of fouling,so as to implement targeted cleaning,which is of great significance to improving the economic use and production safety of the heat exchanger.The model integration of convolutional neural network(CNN)and long short-term memory network(LSTM)was used to predict the heat exchanger fouling factor.A large number of historical data of heat exchanger are used to train the CNN-LSTM model,and excellent prediction results are obtained.Compared with the single CNN and LSTM models and the multi-layer perceptron neural network(MLPNN)models in the literature,the CNN-LSTM model is more accurate and more stable.In the case shown,the coefficient of determination(R2)is 0.98167,and the average absolute percentage error(MAPE)is 3.199×10-3.The establishment of the model not only provides a theoretical basis for solving the scale problem of the heat exchanger,but also provides a more scientific and accurate basis for the safe operation and maintenance strategy of the heat exchanger.It helps to improve the economy and production safety of the entire heat exchange section.关键词
换热器/模型/预测/神经网络/集成/算法Key words
heat exchanger/model/prediction/neural networks/integration/algorithm分类
能源科技引用本文复制引用
张晗筱,王瑞琪,张亚婷..基于CNN-LSTM的换热器污垢因子预测研究[J].化工学报,2025,76(4):1671-1679,9.基金项目
陕西省创新能力支撑计划项目(2019-TD-021) （2019-TD-021）
西安科技大学高层次人才引进计划项目(2050122018) （2050122018）
