The feasibility of using artificial neural networks (ANN) in the prediction of head reduction of centrifugal pumps handling slurries is examined. An ANN model is proposed and compared with the empirical correlation given by the present authors earlier. The comparison showed that the ANN could successfully be used for the prediction of head reductions of centrifugal slurry pumps. The mean deviation between predicted and experimental values is 5.86% which is reasonable for slurry handling processes.
Issue Section:
Technical Briefs
1.
Vocadlo, J. J., Koo, J. K., and Prang, A. J., 1974, “Performance of Centrifugal Pumps in Slurry Services,” Proc. Hydrotransport-3, Paper J2, BHRA Fluid Engineering, pp. 17–32.
2.
Burgess
, K. E.
, and Reizes
, A.
, 1976
, “The Effect of Sizing, Specific Gravity and Concentration on the Performance of Centrifugal Pumps
,” Proc. Inst. Mech. Eng.
, 190-36/76
, pp. 391
–399
.3.
Cave, I., 1976, “Effects of Suspended Solids on the Performance of Centrifugal Pumps,” Proc. Hydrotransport-4, Paper H3, BHRA Fluid Engineering, pp. 35–52.
4.
Sellgren, A., 1979, “Performance of Centrifugal Pumps When Pumping Ores and Industrial Minerals,” Proc. Hydrotransport-6, Paper G1, BHRA Fluid Engineering, pp. 291–304.
5.
Gahlot
, V. K.
, Seshadri
, V.
, and Malhotra
, R. C.
, 1992
, “Effect of Density, Size Distribution, and Concentration of Solids on the Characteristics of Centrifugal Pumps
,” ASME J. Fluids Eng.
, 114
, pp. 386
–389
.6.
Kazim
, K. A.
, Maiti
, B.
, and Chand
, P.
, 1997
, “A Correlation to Predict the Performance Characteristics of Centrifugal Pumps Handling Slurries
,” Proc. Inst. Mech. Eng.
, 211A
, pp. 147
–157
.7.
Engin
, T.
, and Gur
, M.
, 2003, “Comparative Evaluation of Some Existing Correlations to Predict Head Degradation of Centrifugal Slurry Pumps,” ASME J. Fluids Eng., 125.8.
Mez, W., 1984, “The Influence of Solid Concentration, Solid Density and Grain Size Distribution on the Working Behavior of Centrifugal Pumps,” Proc. Hydrotransport-9, Paper H1, BHRA Fluid Engineering, pp. 345–358.
9.
Kosko, B., 1991, Neural Networks for Signal Processing, Prentice-Hall, Englewood Cliffs, NJ.
10.
Fukushima
, K.
, Miyake
, S.
, and Ito
, T.
, 1983
, “Neocognition: A Neural Network Model for a Mechanism of Visual Pattern Recognition
,” IEEE Trans. Syst. Man Cybern.
, SMC-13
, pp. 826
–834
.11.
White, H., 1988, “Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns,” Proceedings of the IEEE International Conference on Neural Networks, Institute of Electrical and Electronic Engineers, San Diego, CA, pp. 41–58.
12.
Pu
, H. C.
, and Hung
, Y. T.
, 1995
, “Use of Artificial Neural Networks: Predicting Trickling Filter Performance in a Municipal Wastewater Treatment Plant
,” Environ. Manage. Health
, 6
(2
), pp. 16
–27
.13.
Mansour
, A.
, Karkoub
, B.
, Osama
, E.
, and Rabie
, M. G.
, 1999
, “Predicting Axial Piston Pump Performance Using Neural Networks
,” Mech. Mach. Theory
, 34
, pp. 1211
–1226
.14.
Farshad
, F. F.
, Garber
, J. D.
, and Lorde
, J. N.
, 2000
, “Predicting Temperature Profiles in Producing Oil Wells Using Artificial Neural Networks
,” Eng. Comput.
, 17
(6
), pp. 735
–754
.15.
Sinha
, A. N.
, Mukherjee
, P. S.
, and De
, A.
, 2000
, “Assessment of Useful Life of Lubricants Using Artificial Neural Network
,” Indust. Lub. Tribol.
, 52
(3
), pp. 105
–109
.16.
Kalogirou
, S. A.
, 2000
, “Applications of Artificial Neural Networks for Energy Systems
,” Appl. Energy
, 67
, pp. 17
–35
.17.
Mei
, L.
, and Levermore
, G. J.
, 2002
, “Simulation and Validation of a VAV System With an ANN Fan Model and a non-Linear VAV Box Model
,” Build. Environ.
, 37
, pp. 277
–284
.18.
Haykin, S., 1994, Neural Networks: A Comprehensive Foundation, Macmillan, New York.
19.
Taccani, R., Pediroda, V., Reini, M., and Giadrossi, A., 2000, “Slurry Pumping: Pump Performance Prediction,” Proceedings of the 25th Int. Conference on Coal Utilization and Fuel Systems, Coal Technology Association, Clearwater, FL, USA.
Copyright © 2003
by ASME
You do not currently have access to this content.
