Deeppipe: Theory-guided LSTM method for monitoring pressure after multi-product pipeline shutdown

Abstract

The pressure changes dramatically during the shutdown process of the multi-product pipeline. When the pipeline pressure comes to decrease, it is often mistaken as pipeline leakage or other abnormal condition which increases the burden of the operator on-site. At present, the method of pipeline shutdown pressure analysis is mainly based on numerical simulation which can not monitor shutdown pressure in real-time. In this work, the time-series approximate ability of long short-term memory (LSTM) is taken advantage of to construct a shutdown pressure prediction model. To overcome the drawback of this deep learning algorithm that is trained only by ample data, the scientific principle and theory are integrated into LSTM. Subsequently, the theory-guided long short-term memory (TG-LSTM) is proposed for pipeline shutdown pressure prediction. The proposed model is trained with available data and simultaneously guided by the theory (physical principle and engineering theory) of the underlying problem. In the training process, the data mismatch, as well as monotonicity constraints, and boundary constraints are coupled into loss function. After acquiring the parameters of the neural network, a TG-LSTM model is established which not only fits the data, but also follows the physical principle and the engineering theory. The proposed model is verified by three real-world multi-product pipelines. The results indicate that TG-LSTM achieves better accuracy than other prediction models, with MAPE being 0.246%, 0.186%, and 0.143%, respectively. Finally, the sensitivity analysis of different hyper-parameter is conducted to illustrate the robustness of TG-LSTM in pipeline shutdown pressure prediction.