Transporting slurry is a difficult task and industries use a kinetic or centrifugal pump or a screw or progressive cavity pump to deliver it. On the other hand, approximation models can help predicting performance and avoiding the expensive experiments of pumps with slurries. In this work, bentonite-based slurries were prepared and pumped by a centrifugal pump and a progressive cavity pump. The experimental facilities were developed in-house and artificial neural network-based approximation models were developed to predict performances. The approximation models say that it can eliminate the expensive testing to draw performance curve a pump. The relative merits of the pumps show that the progressive cavity pump has a better capability to handle the slurries or high viscosity fluids.
CengelYA. Fluid mechanics, New York, NY: Tata McGraw-Hill Education, 1999.
5.
BurgessKEReizesJA. The effect of sizing, specific gravity and concentration on the performance of centrifugal slurry pumps. Proc Inst Mech Eng1976; 190: 391–399.
6.
KazimKAMaitiBChandP. A correlation to predict the performance characteristics of centrifugal pumps handling slurries. Proc IMechE, Part A: J Power and Energy1997; 211: 147–157.
7.
SellgrenAAddieGYuWC. Effects of non-Newtonian mineral suspensions on the performance of centrifugal pumps. Miner Process Extract Metall Rev2000; 20: 239–249.
KhalilMFKassabSZNabyAAAet al.Performance characteristics of centrifugal pump conveying soft slurry. Am J Mech Eng2013; 1: 103–112.
12.
KumarSGandhiBKMohapatraSK. Performance characteristics of centrifugal slurry pump with multi - sized particulate bottom and fly ash mixtures. Particul Sci Technol2014; 32: 466–476.
13.
Whittaker LV. Evaluation and analysis of Wear in progressive cavity pumps. PhD Thesis, University of Hull, UK, 2003.
14.
Martin AM, Kenyery F and Tremante. An experimental study of two phase pumping in progressive cavity pumps. In: SPE 53967, SPE Latin American and Caribbean petroleum engineering conference, Caracas, Venezuela, 1999.
15.
Olivet A, Gamboa J and Kenyery F. Experimental study of two-phase pumping in a progressive cavity pump metal to metal. In: SPE 77730, SPE annual technical conference and exhibition, San Antonio, TX, USA, 29 September–2 October 2002.
16.
Bratu C. Progressing cavity pump (PCP) behavior in multiphase conditions. In: SPE annual technical conference and exhibition, SPE 952727, Dallas, Texas, USA, 9–12 October 2005.
17.
Glier MV. An experimental examination of a progressing cavity pump operating at very high gas volume fractions. USA: Master of Science Thesis, Texas A&M University, 2011.
18.
ChandelSSinghSNSeshadriVA. Comparative study on the performance characteristics of centrifugal and progressive cavity slurry pumps with high concentration fly ash slurries. Particul Sci Technol2011; 29: 378–396.
19.
WarnerBMisraM. Understanding neural networks as statistical tools. Am Stat1996; 50: 284–293.
20.
MrinalKRSiddiqueMHSamadA. Performance prediction of a centrifugal pump delivering non-Newtonian slurry. Particul Sci Technol2018; 36: 38–45.
21.
MrinalKRSamadA. Leakage flow correlation of a progressive cavity pump delivering shear thinning non-Newtonian Fluids. Int J Oil Gas Coal Technol2016; 16: 166–186.
22.
GölcüMPancarYErgürHSet al.Prediction of head, efficiency, and power characteristics in a semi-open impeller. Math Comput Appl2010; 15: 137–147.
23.
EnginTKurtA. Prediction of centrifugal slurry pump head reduction: an artificial neural networks approach. J Fluids Eng2003; 125: 199–202.
24.
AlabiSBWilliamsonCJ. Centrifugal pump-based predictive models for Kraft Black Liquor Viscosity: An artificial neural network approach. Ind Eng Chem Res2011; 50: 10320–10328.
25.
HaddadSBenghanemMMellitAet al.ANNs-based modeling and prediction of hourly flow rate of a photovoltaic water pumping system: Experimental validation. Renew Sustain Energy Rev2015; 43: 635–643.
MathioulakisEPanarasGBelessiotisV. Artificial neural networks for the performance prediction of heat pump hot water heaters. Int J Sustain Energy2018; 37: 173–192.
28.
GholamrezaeiMGhorbanianK. Application of integrated fuzzy logic and neural networks to the performance prediction of axial compressors. Proc IMechE, Part A: J Power and Energy2015; 229: 928–947.
29.
SamadAKimKY. Shape optimization of an axial compressor blade by multi-objective genetic algorithm. Proc IMechE, Part A: J Power and Energy2008; 222: 599–611.
30.
Samad A, Kim KY, Goel T, et al. Shape optimization of turbomachinery blade using multiple surrogate models. In: ASME 2006 2nd joint us-European fluids engineering summer meeting collocated with the 14th international conference on nuclear engineering, USA, January 2006, pp.827–836.
WalkerCIGoulasA. Performance characteristics of centrifugal pumps when handling non-Newtonian homogeneous slurries. Proc IMechE, Part A: J Power and Energy1984; 198: 41–49.
33.
Sery G and Slatter PT. Centrifugal pump derating for non-Newtonian slurries. In: Heywood N (ed) Proceedings of 15th international conference on slurry handling and pipeline transport: Hydro transport, BHR Group, Banff, Cranfield, Canada, 2002, pp.679–692.
34.
PullumLGrahamLJWRudmanM. Centrifugal pump performance calculation for homogeneous and complex heterogeneous suspensions. J South Afr Inst Mining Metall2007; 107: 373–380.
35.
Kabamba BM. Evolution of centrifugal pump performance derating procedures for non-Newtonian slurries. MTech Thesis, Cape Peninsula University of Technology, Cape Town, South Africa, 2006.
36.
Moineau R. A new capsulism. PhD Thesis, The University of Paris, France, 1930.
37.
Fairchild PH and Fairchild IR. High solids bentonite slurries and method for preparing same. Patent 6,399,544, USA, 2002.
38.
WilsonKCAddieGRSellgrenAet al.Slurry transport using centrifugal pumps, New York, NY, USA: Springer Science & Business Media, 2006.
39.
KarnlandOOlssonSNilssonU. Mineralogy and sealing properties of various bentonites and smectite-rich clay materials, Stockholm: SKB, 2006.
ChenJCHuangB. An in-process neural network-based surface roughness prediction (INN-SRP) system using a dynamometer in end milling operations. Int J Adv Manuf Technol2003; 21: 339–347.
44.
BirbilisNCavanaughMKSudholzADet al.A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys. Corrosion Sci2011; 53: 168–176.
45.
KawakamiSNumaoNOkuboYet al.Development, validation, and head-to-head comparison of logistic regression-based nomograms and artificial neural network models predicting prostate cancer on initial extended biopsy. Eur Urol2008; 54: 601–611.
46.
El-NaggarMA. A one-dimensional flow analysis for the prediction of centrifugal pump performance characteristics. Int J Rotat Mach2013.
47.
SrinivasanKM. Positive displacement pumps, India: University Science Press, 2011.
