Dynamic Simulation of Natural Gas Transmission Pipeline Systems through Autoregressive Neural Networks

Transmission of natural gas from its sources to end users in various geographical locations is carried out mostly by natural gas transmission pipeline networks (NGTNs). Effective design and operation of NGTNs requires insights into their steady-state and, particularly, dynamic behavior. This, in turn, calls for efficient computer-aided approaches furnished with accurate mathematical models. The conventional mathematical methods for the dynamic simulation of NGTNs are computationally intensive. In this paper, the use of autoregressive neural networks for cost-effective dynamic simulation of NGTNs is proposed. Considering the length, diameter, roughness, and elevation as the main characteristics of a single pipeline, a neural network pipeline (NNPL) is designed and trained based on the data from a dynamic process simulator. Arbitrary NGTNs can then be easily constructed by connecting the developed NNPLs as the building blocks. The performance of the NNPL network is demonstrated through a number of benchmark pipeline systems, where a very good agreement with the benchmark results is found.