Multi-objective shape optimization of helico-axial multiphase pump impeller based on NSGA-II and ANN

In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influenc...

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Vydané v:	Energy conversion and management Ročník 52; číslo 1; s. 538 - 546
Hlavní autori:	Zhang, Jinya, Zhu, Hongwu, Yang, Chun, Li, Yan, Wei, Huan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 2011 Elsevier
Predmet:	algorithms Applied sciences Computer simulation Crude oil, natural gas and petroleum products Crude oil, natural gas, oil shales producing equipements and methods Energy Energy. Thermal use of fuels equipment performance Exact sciences and technology experimental design Fuels Genetic algorithm Learning theory Materials and auxiliary equipments used in energy engineering Mathematical analysis Mathematical models Multiphase Multiphase pump Neural network Neural networks Numerical simulation Oil extraction from wells. Pumping. Shale oil extraction. Well workover Optimization design prediction Prospecting and production of crude oil, natural gas, oil shales and tar sands Pump impellers Pumps Numerical simulation Multiphase pump Neural network Genetic algorithm Optimization design
ISSN:	0196-8904, 1879-2227
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Abstract	In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influencing the impeller’s performance were chosen as the optimization variables, and the sample spaces were structured according to the orthogonal experimental design method. Then the pressure rise and efficiency in specific working conditions were obtained about all the elements in the sample space by numerical simulation. With the simulated results as the input specimen, a multiphase pump performance prediction model was designed through BP neural network. With the obtained prediction model as the fitness value evaluation method, the pump impeller was optimized using the NSGA-II multi-objective genetic algorithm, which finally offered an improved impeller structure with enhanced pressure rise and efficiency. Furthermore, five stages of optimized compression cells were manufactured and applied in experiment test. The result shows compared to the original design, the pressure rise of the optimized pump has increased by ∼10% and the efficiency has increased by ∼3%, which is in keeping with our optimal result and confirms our method is feasible.
AbstractList	In order to improve the prototype's performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influencing the impeller's performance were chosen as the optimization variables, and the sample spaces were structured according to the orthogonal experimental design method. Then the pressure rise and efficiency in specific working conditions were obtained about all the elements in the sample space by numerical simulation. With the simulated results as the input specimen, a multiphase pump performance prediction model was designed through BP neural network. With the obtained prediction model as the fitness value evaluation method, the pump impeller was optimized using the NSGA-II multi-objective genetic algorithm, which finally offered an improved impeller structure with enhanced pressure rise and efficiency. Furthermore, five stages of optimized compression cells were manufactured and applied in experiment test. The result shows compared to the original design, the pressure rise of the optimized pump has increased by [inline image]10% and the efficiency has increased by [inline image]3%, which is in keeping with our optimal result and confirms our method is feasible. In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by combining the artificial neural network (ANN) with non-dominated sorting genetic algorithm-II (NSGA-II). The main geometric parameters influencing the impeller’s performance were chosen as the optimization variables, and the sample spaces were structured according to the orthogonal experimental design method. Then the pressure rise and efficiency in specific working conditions were obtained about all the elements in the sample space by numerical simulation. With the simulated results as the input specimen, a multiphase pump performance prediction model was designed through BP neural network. With the obtained prediction model as the fitness value evaluation method, the pump impeller was optimized using the NSGA-II multi-objective genetic algorithm, which finally offered an improved impeller structure with enhanced pressure rise and efficiency. Furthermore, five stages of optimized compression cells were manufactured and applied in experiment test. The result shows compared to the original design, the pressure rise of the optimized pump has increased by ∼10% and the efficiency has increased by ∼3%, which is in keeping with our optimal result and confirms our method is feasible.
Author	Wei, Huan Zhu, Hongwu Yang, Chun Zhang, Jinya Li, Yan
Author_xml	– sequence: 1 givenname: Jinya surname: Zhang fullname: Zhang, Jinya email: zhjinya@163.com – sequence: 2 givenname: Hongwu surname: Zhu fullname: Zhu, Hongwu – sequence: 3 givenname: Chun surname: Yang fullname: Yang, Chun – sequence: 4 givenname: Yan surname: Li fullname: Li, Yan – sequence: 5 givenname: Huan surname: Wei fullname: Wei, Huan
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Snippet	In order to improve the prototype’s performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by... In order to improve the prototype's performance of the helico-axial multiphase pump, a multi-objective optimal method for the pump impeller was developed by...
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SubjectTerms	algorithms Applied sciences Computer simulation Crude oil, natural gas and petroleum products Crude oil, natural gas, oil shales producing equipements and methods Energy Energy. Thermal use of fuels equipment performance Exact sciences and technology experimental design Fuels Genetic algorithm Learning theory Materials and auxiliary equipments used in energy engineering Mathematical analysis Mathematical models Multiphase Multiphase pump Neural network Neural networks Numerical simulation Oil extraction from wells. Pumping. Shale oil extraction. Well workover Optimization design prediction Prospecting and production of crude oil, natural gas, oil shales and tar sands Pump impellers Pumps
Title	Multi-objective shape optimization of helico-axial multiphase pump impeller based on NSGA-II and ANN
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