Hybrid modeling of gas transport pipeline based on pressure signals

To monitor the operating state of gas pipelines, a hybrid model has been established on the basis of the combination of mechanism model and neural network model. Firstly, the mechanism model was built based on the basic transport flow equations with the consideration of the mass balance condition, momentum balance condition and state equation. The mechanism model acts as the primary model in hybrid modeling. Then the neural network model was used to compensate the error of the experiential model which was raised by simplification and ignorance of some dynamic parameters during the modeling. The neural network acts as the compensatory model to improve the modeling precision. In addition, for increasing the detection precision, the high-precision pressure sensor was used to sample the gas pipeline signal instead of the low-precision flow-meter. Finally, the precision contrast between mechanism model and hybrid model shows that the detection precision achieved by using hybrid model is improved obviously.