Energy-Saving for a Velocity Control System of a Pipe Isolation Tool Based on a Reinforcement Learning Method
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 efficienc...
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| Veröffentlicht in: | International journal of precision engineering and manufacturing-green technology 2022-01, Vol.9 (1), p.225-240 |
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| container_title | International journal of precision engineering and manufacturing-green technology |
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| creator | Wu, Tingting Zhao, Hong Gao, Boxuan Meng, Fanbo |
| description | 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. |
| doi_str_mv | 10.1007/s40684-021-00309-8 |
| format | Article |
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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.</description><identifier>ISSN: 2288-6206</identifier><identifier>EISSN: 2198-0810</identifier><identifier>DOI: 10.1007/s40684-021-00309-8</identifier><language>eng</language><publisher>Heidelberg: Springer Nature B.V</publisher><subject>Accumulators ; Algorithms ; Control algorithms ; Control systems ; Design ; Electric vehicles ; Energy conservation ; Energy consumption ; Energy distribution ; Energy efficiency ; Energy management ; Energy recovery ; Energy recovery systems ; Hydraulic equipment ; Hydraulics ; Learning ; Machine learning ; Natural gas ; Pipes ; Plugging ; Real time ; Reinforcement ; Simulation ; Velocity</subject><ispartof>International journal of precision engineering and manufacturing-green technology, 2022-01, Vol.9 (1), p.225-240</ispartof><rights>Korean Society for Precision Engineering 2021.</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c353t-96dfd5f4373aef57660b109e284606c1808f896db60d86f8de7568bab42624013</citedby><cites>FETCH-LOGICAL-c353t-96dfd5f4373aef57660b109e284606c1808f896db60d86f8de7568bab42624013</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Wu, Tingting</creatorcontrib><creatorcontrib>Zhao, Hong</creatorcontrib><creatorcontrib>Gao, Boxuan</creatorcontrib><creatorcontrib>Meng, Fanbo</creatorcontrib><title>Energy-Saving for a Velocity Control System of a Pipe Isolation Tool Based on a Reinforcement Learning Method</title><title>International journal of precision engineering and manufacturing-green technology</title><description>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.</description><subject>Accumulators</subject><subject>Algorithms</subject><subject>Control algorithms</subject><subject>Control systems</subject><subject>Design</subject><subject>Electric vehicles</subject><subject>Energy conservation</subject><subject>Energy consumption</subject><subject>Energy distribution</subject><subject>Energy efficiency</subject><subject>Energy management</subject><subject>Energy recovery</subject><subject>Energy recovery systems</subject><subject>Hydraulic equipment</subject><subject>Hydraulics</subject><subject>Learning</subject><subject>Machine learning</subject><subject>Natural gas</subject><subject>Pipes</subject><subject>Plugging</subject><subject>Real time</subject><subject>Reinforcement</subject><subject>Simulation</subject><subject>Velocity</subject><issn>2288-6206</issn><issn>2198-0810</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNotkEFLAzEQhYMoWLR_wFPAc3SS7GazRy1VCxXFVq8hu5vUlW1Sk1TYf29qPc0M7_Hm8SF0ReGGAlS3sQAhCwKMEgAONZEnaMJoLQlICqd5Z1ISwUCco2mMfQMMKlYKARO0nTsTNiNZ6Z_ebbD1AWv8YQbf9mnEM-9S8ANejTGZLfY2i6_9zuBF9INOvXd47bN-r6PpcL40fjO9yymt2RqX8NLo4A7BzyZ9-u4SnVk9RDP9nxfo_WG-nj2R5cvjYna3JC0veSK16GxX2oJXXBtbVrlpQ6E2TBYCREslSCuzqRHQSWFlZ6pSyEY3BROsAMov0PUxdxf8997EpL78Prj8UrGaM1bVgsrsYkdXG3yMwVi1C_1Wh1FRUAey6khWZbLqj6yS_BcN9WrG</recordid><startdate>20220101</startdate><enddate>20220101</enddate><creator>Wu, Tingting</creator><creator>Zhao, Hong</creator><creator>Gao, Boxuan</creator><creator>Meng, Fanbo</creator><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope></search><sort><creationdate>20220101</creationdate><title>Energy-Saving for a Velocity Control System of a Pipe Isolation Tool Based on a Reinforcement Learning Method</title><author>Wu, Tingting ; Zhao, Hong ; Gao, Boxuan ; Meng, Fanbo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-96dfd5f4373aef57660b109e284606c1808f896db60d86f8de7568bab42624013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Accumulators</topic><topic>Algorithms</topic><topic>Control algorithms</topic><topic>Control systems</topic><topic>Design</topic><topic>Electric vehicles</topic><topic>Energy conservation</topic><topic>Energy consumption</topic><topic>Energy distribution</topic><topic>Energy efficiency</topic><topic>Energy management</topic><topic>Energy recovery</topic><topic>Energy recovery systems</topic><topic>Hydraulic equipment</topic><topic>Hydraulics</topic><topic>Learning</topic><topic>Machine learning</topic><topic>Natural gas</topic><topic>Pipes</topic><topic>Plugging</topic><topic>Real time</topic><topic>Reinforcement</topic><topic>Simulation</topic><topic>Velocity</topic><toplevel>online_resources</toplevel><creatorcontrib>Wu, Tingting</creatorcontrib><creatorcontrib>Zhao, Hong</creatorcontrib><creatorcontrib>Gao, Boxuan</creatorcontrib><creatorcontrib>Meng, Fanbo</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><jtitle>International journal of precision engineering and manufacturing-green technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wu, Tingting</au><au>Zhao, Hong</au><au>Gao, Boxuan</au><au>Meng, Fanbo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Energy-Saving for a Velocity Control System of a Pipe Isolation Tool Based on a Reinforcement Learning Method</atitle><jtitle>International journal of precision engineering and manufacturing-green technology</jtitle><date>2022-01-01</date><risdate>2022</risdate><volume>9</volume><issue>1</issue><spage>225</spage><epage>240</epage><pages>225-240</pages><issn>2288-6206</issn><eissn>2198-0810</eissn><abstract>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. 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| ispartof | International journal of precision engineering and manufacturing-green technology, 2022-01, Vol.9 (1), p.225-240 |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Accumulators Algorithms Control algorithms Control systems Design Electric vehicles Energy conservation Energy consumption Energy distribution Energy efficiency Energy management Energy recovery Energy recovery systems Hydraulic equipment Hydraulics Learning Machine learning Natural gas Pipes Plugging Real time Reinforcement Simulation Velocity |
| title | Energy-Saving for a Velocity Control System of a Pipe Isolation Tool Based on a Reinforcement Learning Method |
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