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- 저자 : Boxuan Gao (검색결과 2 건)
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Tingting Wu,Hong Zhao,Boxuan Gao,Fanbo Meng 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.1
The pipe isolation tool (PIT) demonstrates remarkable advantages in safety and efficiency compared with traditional plugging devices. However, its utilization in plugging operations is limited by the operation duration. In addition, the existing energy recovery system has low energy saving efficiency. In this paper, a real-time control energy-saving system of the PIT was designed based on a reinforcement learning algorithm. First, an experimental device for energy-saving was designed. Secondly, the energy distribution scheme of a hydraulic pump and accumulator based on experimental data was proposed. Finally, the reinforcement learning algorithm was used to adjust the opening of the hydraulic pump and the accumulator valves in real time during the plugging process to improving energy saving efficiency. The results verify that the energy saving efficiency of the PIT control system based on reinforcement learning could reach 23.71%, which satisfies the objectives of energy-saving and environmental applicability.
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Xingyuan Miao,Hong Zhao,Boxuan Gao,Tingting Wu,Yanguang Hou 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.6
Compared with traditional plugging methods, the in-pipe intelligent isolation plugging tool (IPT) is advantageous in safety and work efficiency. However, during the plugging process, the flow field around the IPT changes drastically, resulting in vortex-induced vibration and potential failure of the plugging operation. In this study, three foldable spoilers were designed at the tail of the IPT to optimize the flow field. The vibration of the IPT can be alleviated by adjusting the angles of the spoilers. A vibration reduction control system of the IPT was designed based on deep reinforcement learning. First, we conducted an experiment for vibration reduction system. Second, a nonlinear model of the pressure difference based on experimental data was established. Then, a multi-agent self-learning system based on the deep Q-network (DQN) was designed, and the optimal actions were selected in each agent to adjust the spoiler angles during the plugging process. Finally, a controller based on fuzzy reinforcement learning was proposed to flip the spoilers to the optimized angles. The results show that the vibration reduction control system of the IPT reduced the pressure difference by an average of 28.32%, which indicates the stability of the plugging process and a successful reduction of the IPT vibration.
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