Abstract
Modern pipeline energy systems are structurally complex and geographically distributed engineering installations with hundreds of thousands of interconnected process facilities. The optimal control of such systems is a highly responsible task that cannot be solved without applied decision support software and automated control systems. This article addresses the promising area for the development of intelligent systems of the pipeline energy infrastructure control based on deep reinforcement learning technologies. The authors suggested approaches to the learning of digital models and proved them using the examples of operating gas industry systems. Also, they considered the peculiarities of introducing new technologies and outlined further research prospects.
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Belinsky, A., Afanasev, V. (2021). Optimal Control of Energy Pipeline Systems Based on Deep Reinforcement Learning. In: Popkova, E.G., Sergi, B.S. (eds) "Smart Technologies" for Society, State and Economy. ISC 2020. Lecture Notes in Networks and Systems, vol 155. Springer, Cham. https://doi.org/10.1007/978-3-030-59126-7_148
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DOI: https://doi.org/10.1007/978-3-030-59126-7_148
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