A self-adaptive deep learning algorithm for intelligent natural gas pipeline control

Energy Reports

Shuyu Sun¹  Tao Zhang²  Hua Bai³ 

[1] Gas Pipeline Control Center, 9 Dongzhimen North Street, Dongcheng District, Beijing, 100007, China;Computational Transport Phenomena Laboratory (CTPL), Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;;PetroChina Beijing Oil &

关键词: Natural gas pipeline;    Pipeline control;    Deep learning;    Artificial intelligence;    Compressor operations;   

DOI  :  

来源: DOAJ

【 摘 要 】

Natural gas has been recognized as a promising energy supply for modern society due to its relatively less air pollution in consumption, while pipeline transportation is preferred especially for long-distance transmissions. A simplified pipeline control scenario is proposed in this paper to deeply accelerate the management and decision process in pipeline dispatch, in which a direct relevance between compressor operations and the inlet flux at certain stations is established as the main dispatch logic. A deep neural network is designed with specific input and output features for this scenario and the hyper-parameters are carefully tuned for a better adaptability of this problem. The realistic operation data of two pipelines have been obtained and prepared for learning and testing. The proposed algorithm with the optimized network structure is proved to be effective and reliable in predicting the pipeline operation status, under both the normal operation conditions and abnormal situations. The successful definition of ”ghost compressors” make this algorithm to be the first self-adaptive deep learning algorithm to assist natural gas pipeline intelligent control.

【 授权许可】

Unknown