Intelligent Control on Urban Natural Gas Supply Using a Deep-Learning-Assisted Pipeline Dispatch Technique

Natural gas has been attracting increasing attentions all around the world as a relatively cleaner energy resource compared with coal and crude oil. Except for direct consumption fuel, electricity generation is now another environmentally-friendly utilization of natural gas, which makes it more favorable supply urban areas. Pipeline transportation main approach connecting production field areas thanks to safety economic reasons. In this paper, an intelligent pipeline dispatch technique proposed using deep learning methods predict change consequence compressor operations. Practical operation data collected prepared training validation models, accelerated predictions can help make controlling plans regarding operations meet requirement in supply. The neutral network equipped self-adaptability, enables general adaption on various temporal conditions including failure maintenance.