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

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 long-distance transmissions. A simplified control scenario proposed this paper deeply accelerate the management and decision process dispatch, which direct relevance between compressor operations inlet flux at certain stations established main dispatch logic. deep neural network designed with specific input output features hyper-parameters are carefully tuned better adaptability of problem. The realistic operation data two pipelines have obtained prepared learning testing. algorithm optimized structure proved be effective reliable predicting status, under both normal conditions abnormal situations. successful definition ”ghost compressors” make first self-adaptive assist natural intelligent control.