Multiple Performance Optimization of a Single Stage Centrifugal Pump using an Intelligent System Approach

Document Type : Regular Article


Eskisehir Osmangazi University, Faculty of Engineering and Architecture, Mechanical Engineering Department), Batı Meşelik, 26480, Eskisehir, Turkey



This study aims the use of an intelligent system to analyze and enhance the performance of a single stage centrifugal pump (SSCP) with respect to the blade number of impeller, which is crucial for the centrifugal pump design. In general, maximizing the efficiency (h) is the most common performance cost. Moreover, maximizing the pump head (H) and minimizing the power (P) are the other important criteria that should be considered for the optimal blade number. These goals are not simultaneously considered usually in the studies on this topic. The motivation of this study is to expand the system management perspective by evaluating the performance of the system with these multiple criteria. The centrifugal pump design is a typical Multi-Objective Optimization (MOO) problem. The MOO approach consists of five decision variables and three objective functions with different blade numbers for impellers. To determine the optimal solution of this MOO problem, the experimental data is initially expanded using a learning by examples methods based on an intelligent network model training. Then, extended data is used for calculating performance measures at each input-output pairs. In order to evaluate the results of the proposed approach on a real-time system, B50-200/100 type model pump with the speed of 2950 rpm and an outlet angle of 22.150 was tested at State Sugar Machine Plant, Eskisehir (Turkey). Since the main purpose is to determine the optimal blade number, different blade numbers has been used while other geometric parameters were kept constant. To determine the optimal solution, experimental data has used to train a selected soft computing model known as FWNN model. This model has advanced to express the relationship between the inlet and outlet values of the centrifugal pump. FWNN model achieves the general characteristics of the performance measure. The analysis with the use of FWNN model shows that, the optimum number of blades by considering the specified performance parameters for the centrifugal pump design is seven. Comparing with pump impeller with number of blades 5, 6, 8 and 9 increases in efficiency rates are 0.4%, 2.57%, 6.4% and 7.2%, respectively. The FWNN model over the performance analysis algorithm completes the missing data if exists and indicates the best performance solution as given.


Bellary S., A. Husain, A. Samad and R. A. Kanai (2018). Performance optimization of centrifugal pump for crude oil delivery. Journal of Engineering Research 15(1), 88-101.##
Bozorgasareh, H., J. Khalesi, M. Jafari and H. O. Gazori (2021). Performance improvement of mixed-flow centrifugal pumps with new impeller shrouds: Numerical and experimental investigations. Renewable Energy 163, 635-648.##
Chakraborty, S. and K. M. Pandey (2011). Numerical studies on effects of blade number variations on performance of centrifugal pumps at 4000 rpm. International Journal of Engineering and Technology (IACSIT) 3(4), 410-416.##
Derakhshan, S., M. Pourmahdavi, E. Abdolahnejad, A. Reihani and A. Ojaghi (2013). Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm. Computers and Fluids 81(9), 145-151.##
Ding, H., Z. Li, X. Gong and M. Li (2019). The influence of blade outlet angle on the performance of centrifugal pump with high specific speed. Vacuum 159, 239-246.##
Duan, B., M. Luo, C. Yuan and X. Luo (2015). Multi-objective hydraulic optimization and analysis in a minipump. Science Bulletin 17, 1517-1526.##
Elyamin, A. G. R. H., M. A. Bassily, K. Y. Khalil and M. S. H. Gomaa (2019). Effect of impeller blades number on the performance of a centrifugal pump. Alexandria Engineering Journal 58(1), 39-48.##
Fu, L. and Z. Hong (2016). Optimization design of lower speed pump based on genetic algorithm. Journal of Chinese Agricultural Mechanization 37(2), 233-236.##
Gölcü, M. (2006). Artificial neural network based modeling of performance characteristics of deep well pumps with splitter blade. Energy Conversion and Management 47(18), 3333-3343.##
Gülich, J. F. (2010). Centrifugal Pumps. New York: Springer, 2nd Ed.##
Haifeng, L., W. Yulin and Z. Zhimei (2001). The determination of centrifugal pump impeller’s design with three-dimensional turbulent flow simulation. Fluidmachinery 29(9), 18-21.##
Han, W., L. Nan, M. Su, Y. Chen, R. Li and X. Zhang (2019). Research on the prediction method of centrifugal pump performance based on a double hidden layer BP neural network. Energies (MPDI) 12(14), 1-14.##
Heinz, B., P. Bloch and R. A.  Budris (2006). Pump users’ handbook Lilburn, The Fairmont Press Inc.##
Houlin, L., W. Yong, Y. Shouqi, T. Minggao and W. Kai (2010). Effects of blade number on characteristics of centrifugal pumps. Chinese Journal of Mechanical Engineering 23(06), 742-747.##
Jafarzadeh, B., A. Hajari, M. M. Alishahi and M. H. Akbari (2011). The flow simulation of a low-specific-speed high-speed centrifugal pump. Applied Mathematical Modelling 35(1), 242–249.##
Karassik, I. J., J. P. Messina, P. Cooper and C. C. Heald (2001). Pump handbook. New York, McGraw-Hill.##
Li, W. G., F. Z. Su and C. Xiao (2002). Influence of the number of impeller blades on the performance of centrifugal oil pumps. World Pumps 427, 32-35.##
Matlaka, M. E., D. V. V. Kallon., S. P. Simelane and P. M. Mashinini (2019). Impact of Design Parameters on the Performance of Centrifugal Pumps. Procedia Manufacturing 35, 197-206.##
Namazizadeh, M., M. T. Gevari, M. Mojaddam and M. Vajdi (2020). Optimization of the splitter blade configuration and geometry of a centrifugal pump impeller using design of experiment. Journal of Applied Fluid Mechanics 13, 89-101.##
Öztekin, A., B. R. Seymour and E. Varley (2002). Unsteady stratified swirling shear flows. Mathematical and Computer Modeling 36(3), 321-337.##
Papierski, A. and A. Błaszczyk (2011). Multiobjective optimization of the semi-open impeller in a centrifugal pump by a multilevel method. Journal of Theoretical and Applied Mechanics 49(2), 327–341.##
Rababa, K. S. (2011). The effect of blades number and shape on the operating characteristics of groundwater centrifugal pumps. European Journal of Scientific Research 52(2), 243-251.##
Safikhani, H., A. Khalkhali and M. Farajpoor (2011). Pareto based multi-objective optimization of centrifugal pumps using CFD, neural networks and genetic algorithms Engineering Applications of Computational Fluid Mechanics 5(1), 37-48.##
Sakthivel, N. R., V. Sugumaran and B. B. Nair (2012). Automatic rule learning using roughset for fuzzy classifier in fault categorization of mono-block centrifugal pump. Applied Soft Computing 12(1), 196-203.##
Siddique, M. H., A. Afzal and A. Samad (2018). Design optimization of the centrifugal pumps via low fidelity models. Mathematical Problems in Engineering ID (3987594), 1-14.##
Singh V. R., M. J. Zinzuwadia, S. Sheth and R. J. Desai (2017). Parametric study and design optimization of centrifugal pump impeller. International Conference on Research and Innovations in Science, Engineering &Technology (ICRISET) 1, 507-515##
Škerlavaj, A., M. Morgut, D. Jošt and E. Nobile (2017). Optimization of a single‐stage double‐suction centrifugal pump. In: IOP Conf. Series: Journal of Physics Conference 796(1), 1-11.##
Stephanoff, A. J. (1986). Centrifugal and axial flow pumps: theory, design, and application. New York, Amazon Company##
Wang, W., M. K. Osman, J. Pei, X. Gan and T. Yin (2019a). Artificial neural networks approach for a multi-objective cavitation optimization design in a double-suction centrifugal pump. Processes (MDPI) 246 (7), 1-23.##
Wang, W., J. Pei, S. Yuan, X. Gan and T. Yin (2019b). Artificial neural network for the performance improvement of a centrifugal pump. IOP Conf. Series: Earth and Environmental Science 240(3), 1-11.##
Wu, Q., Q. Shen, X. Wang and Y. Yang (2016). Estimation of centrifugal pump operational state with dual neural network architecture based model. Neurocomputing 216, 102-108.##
Wu, X. F., X. Tian, M. G. Tan and H. L. Liu (2020). Multi-parameter optimization and analysis on performance of a mixed flow pump. Journal of Applied Fluid Mechanics 13(1), 199-209.##
Yilmaz, S. and Y. Oysal (2010). Fuzzy wavelet neural network models for prediction and identification of dynamical systems. IEEE Transactions on Neural Networks 21(10), 1599-1609.##
Yuan, S. Q., C. Q. Chen and W. Cao (1993). Design method of obtaining stable head-flow curves of centrifugal pumps. In: ASME Pumping Machinery Meeting FED 154, 171-175.##
Volume 15, Issue 4
July and August 2022
Pages 1269-1280
  • Received: 24 May 2021
  • Revised: 06 March 2022
  • Accepted: 03 April 2022
  • First Publish Date: 17 May 2022