关键词: 热油管道, 非稳态油温, seq2seq, 预测模型

Abstract: After the start of the heating furnace in the upstream station of the hot oil pipeline, the unsteady trend of the oil temperature in the downstream inlet station will affect the hot⁃washing operation of the pipeline before the pigging and the emergency treatment for abnormal shutdown. Accurate prediction of unsteady oil temperature trend changes can effectively improve the safe operation of pipelines and energy conservation and consumption reduction. A deep learning framework is adopted to take the unsteady process of upstream and downstream oil temperature as the mapping relationship from sequence to sequence, and the classical seq2seq algorithm of Recurrent Neural Network (RNN) was used to establish a predictive model of unsteady oil temperature. Through the correlation analysis, the parameters such as the unsteady oil temperature, pipeline flow and ground temperature in the upstream outbound station are selected as the model influence factors. In the SCADA system database of the HY hot oil pipeline, the relevant parameter historical data is downloaded as sample data, and after the data is preprocessed, the model is trained and tested. The error between the predicted value and the true value is ±0.2 °C, the correlation coefficient R and the root mean square difference RMSE are 0.96 and 0.99. At the same time, the verification model can effectively predict the unsteady oil temperature of the downstream station, which has high accuracy and good generalization.

Key words: Hot oil pipeline, Unsteady oil temperature, seq2seq, Forecast model