Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving
Beam pumping system which is widely used in petroleum enterprises of China is one of the most energy-consuming equipment. It is difficult to be modeled and optimized due to its complication and nonlinearity. To address this issue, a novel intelligent computing based method is proposed in this paper....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/317130 |
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| author | Xiaohua Gu Taifu Li Zhiqiang Liao Liping Yang Ling Nie |
| author_facet | Xiaohua Gu Taifu Li Zhiqiang Liao Liping Yang Ling Nie |
| author_sort | Xiaohua Gu |
| collection | DOAJ |
| description | Beam pumping system which is widely used in petroleum enterprises of China is one of the most energy-consuming equipment. It is difficult to be modeled and optimized due to its complication and nonlinearity. To address this issue, a novel intelligent computing based method is proposed in this paper. It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). The inputs of GRNN include the number of punching, the maximum load, the minimum load, the effective stroke, and the computational pump efficiency, while the outputs are the electric power consumption and the oil yield. Experimental results show that there is good overlap between model estimations and unseen data. Then sixty-one sets of optimum parameters are found based on the obtained model. Also, the results show that, under the optimum parameters, more than 5.34% oil yield is obtained and more than 3.75% of electric power consumption is saved. |
| format | Article |
| id | doaj-art-e6e3e20b4cbc4af592e240ae1427cf87 |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-e6e3e20b4cbc4af592e240ae1427cf872025-08-20T02:21:10ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/317130317130Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy SavingXiaohua Gu0Taifu Li1Zhiqiang Liao2Liping Yang3Ling Nie4Department of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaDepartment of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaCollege of Electronic Engineering, Xi’an Shiyou University, Xi’an 710065, ChinaCollege of Optoelectronic Engineering, Chongqing University, Chongqing 400044, ChinaDepartment of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing 401331, ChinaBeam pumping system which is widely used in petroleum enterprises of China is one of the most energy-consuming equipment. It is difficult to be modeled and optimized due to its complication and nonlinearity. To address this issue, a novel intelligent computing based method is proposed in this paper. It firstly employs the general regression neural network (GRNN) algorithm to obtain the best model of the beam pumping system, and secondly searches the optimal operation parameters with improved strength Pareto evolutionary algorithm (SPEA2). The inputs of GRNN include the number of punching, the maximum load, the minimum load, the effective stroke, and the computational pump efficiency, while the outputs are the electric power consumption and the oil yield. Experimental results show that there is good overlap between model estimations and unseen data. Then sixty-one sets of optimum parameters are found based on the obtained model. Also, the results show that, under the optimum parameters, more than 5.34% oil yield is obtained and more than 3.75% of electric power consumption is saved.http://dx.doi.org/10.1155/2014/317130 |
| spellingShingle | Xiaohua Gu Taifu Li Zhiqiang Liao Liping Yang Ling Nie Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving Journal of Applied Mathematics |
| title | Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving |
| title_full | Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving |
| title_fullStr | Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving |
| title_full_unstemmed | Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving |
| title_short | Modeling and Optimization of Beam Pumping System Based on Intelligent Computing for Energy Saving |
| title_sort | modeling and optimization of beam pumping system based on intelligent computing for energy saving |
| url | http://dx.doi.org/10.1155/2014/317130 |
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