Modelling and performance prediction of a centrifugal cargo pump on a chemical tanker

References

• Allen DM. 1971. Mean square error of prediction as a criterion for selecting variables. Technometrics. 13(3):469–475. doi: 10.1080/00401706.1971.10488811
   Web of Science ®Google Scholar
• An Z, Zhounian L, Peng W, Linlin C, Dazhuan W. 2016. Multi-objective optimization of a low specific speed centrifugal pump using an evolutionary algorithm. Eng Optim. 48(7):1251–1274. doi: 10.1080/0305215X.2015.1104987
   Web of Science ®Google Scholar
• Armstrong JS, Collopy F. 1992. Error measures for generalizing about forecasting methods empirical comparisons. Int J Forecast. 8(1):69–80. doi: 10.1016/0169-2070(92)90008-W
   Web of Science ®Google Scholar
• Azadeh A, Ebrahimipour V, Bavar P. 2010. A fuzzy inference system for pump failure diagnosis to improve maintenance process: The case of a petrochemical industry. Expert Syst Appl. 37(1):627–639. doi: 10.1016/j.eswa.2009.06.018
   Web of Science ®Google Scholar
• Azadeh A, Saberi M, Kazem A, Ebrahimipour V, Nourmohammadzadeh A, Saberi Z. 2013. A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization. Appl Soft Comput. 13(3):1478–1485. doi: 10.1016/j.asoc.2012.06.020
   Web of Science ®Google Scholar
• Bachus L, Custodio A. 2003. Know and understand centrifugal pumps. Oxford: Elsevier. ISBN-13: 978-1856174091.
   Google Scholar
• Benini E, Cenzon M. 2009. Calibration of a meanline centrifugal pump model using evolutionary algorithms. J Power Energy. 223(7):835–847. doi: 10.1243/09576509JPE742
   Google Scholar
• Bluman AG. 2000. Elementary statistics. New York: McGraw Hill.
   Google Scholar
• Derakhshan S, Bashiri M. 2018. Investigation of an efficient shape optimization procedure for centrifugal pump impeller using eagle strategy algorithm and ANN (case study: slurry flow). Struct Multidiscipl Optim, 1-15.
   Web of Science ®Google Scholar
• Derakhshan S, Pourmahdavi M, Abdolahnejad E, Reihani A, Ojaghi A. 2013. Numerical shape optimization of a centrifugal pump impeller using artificial bee colony algorithm. Comput Fluids. 81:145–151. doi: 10.1016/j.compfluid.2013.04.018
   Web of Science ®Google Scholar
• Dreyfus G. 2005. Neural networks: methodology and applications. Berlin: Springer Science & Business Media.
   Google Scholar
• Durmusoglu Y, Kocak G, Deniz C, Zincir B. 2015. Energy efciency analysis of pump systems in a ship power plant and a case study of a container ship. Opatija: IAMU 16th AGA.
   Google Scholar
• Ertoz Ö. 2003. Energy efficiency in pumps. In: 6th National Installations Engineering and Congress; Istanbul.
   Google Scholar
• IMO. 2016. International code for the construction and equipment of ships carrying dangerous chemicals in bulk (IBC Code), 2016 Edition (ID100E), ISBN 978-92-801-1595-6 (9789280115956).
   Google Scholar
• Kaya D, Yagmur EA, Yigit KS, Kilic FC, Eren AS, Celik C. 2008. Energy efficiency in pumps. Energy Convers Manage. 49(6):1662–1673. doi: 10.1016/j.enconman.2007.11.010
   Web of Science ®Google Scholar
• Khataee AR, Kasiri MB. 2010. Artificial neural networks modeling of contaminated water treatment process by homogeneous and heterogeneous nanocatalysis. J Mol Catal A: Chem. 331, 86–100. doi: 10.1016/j.molcata.2010.07.016
   Web of Science ®Google Scholar
• Kocak G, Durmusoglu Y. 2017. Energy efficiency analysis of a ship’s central cooling system using variable speed pump. J Marine Eng Technol. DOI:10.1080/20464177.2017.1283192.
   Web of Science ®Google Scholar
• Köseoğlu B. 2015. Port modeling and applying optimization techniques (book in Turkish). İzmir: Dokuz Eylul University.
   Google Scholar
• Lomakin VO, Chaburko PS, Kuleshova MS. 2017. Multi-criteria optimization of the flow of a centrifugal pump on energy and vibroacoustic characteristics. Procedia Eng. 176:476–482. doi: 10.1016/j.proeng.2017.02.347
   Google Scholar
• Makridakis S, Wheelwright S, Hyndman R. 1998. Forecasting methods and applications. 3rd edn. New York, NY: Wiley.
   Google Scholar
• Mathworks. 2018. Simulation and model-based design, [accessed 2017 October 22]. https://www.mathworks.com/products/simulink.html.
   Google Scholar
• Najafi A, Nowruzi H, Ghassemi H. 2018. Performance prediction of hydrofoil-supported catamarans using experiment and ANNs. Appl Ocean Res. 75:66–84. doi: 10.1016/j.apor.2018.02.017
   Web of Science ®Google Scholar
• Nourbakhsh A, Safikhani H, Derakhshan S. 2011. The comparison of multi-objective particle swarm optimization and NSGA II algorithm: applications in centrifugal pumps. Eng Optim. 43(10):1095–1113. doi: 10.1080/0305215X.2010.542811
   Web of Science ®Google Scholar
• Nowruzi H, Ghassemi H, Ghiasi M. 2017. Performance predicting of 2D and 3D submerged hydrofoils using CFD and ANNs. J Mar Sci Technol. 22(4):710–733. doi: 10.1007/s00773-017-0443-0
   Web of Science ®Google Scholar
• Olszewski P. 2016. Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps. Appl Energy. 178:527–539. doi: 10.1016/j.apenergy.2016.06.084
   Web of Science ®Google Scholar
• Oztemel E. 2016. Artificial neural networks (book in Turkish). 4th ed. İstanbul: Ozkaracan Publishing, Papatya Bilim.
   Google Scholar
• Pillon LZ. 2007. Interfacial properties of petroleum products. Boca Raton, FL: CRC Press.
   Google Scholar
• Sakar C, Zorba Y. 2013. Operational efficiency at chemical tankers (book in Turkish). İstanbul: Beta Publishing.
   Google Scholar
• Sakthivel NR, Nair BB, Sugumaran V. 2012. Soft computing approach to fault diagnosis of centrifugal pump. Appl Soft Comput. 12(5):1574–1581. doi: 10.1016/j.asoc.2011.12.009
   Web of Science ®Google Scholar
• Sakthivel NR, Sugumaran V, Babudevasenapati S. 2010. Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Syst Appl. 37(6):4040–4049. doi: 10.1016/j.eswa.2009.10.002
   Web of Science ®Google Scholar
• Shora MM, Ghassemi H, Nowruzi H. 2018. Using computational fluid dynamic and artificial neural networks to predict the performance and cavitation volume of a propeller under different geometrical and physical characteristics. J Marine Eng Technol. 17(2):59–84. doi: 10.1080/20464177.2017.1300983
   Web of Science ®Google Scholar
• Su Z, Tang B, Liu Z, Qin Y. 2015. Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing. 157:208–222. doi: 10.1016/j.neucom.2015.01.016
   Web of Science ®Google Scholar
• Taghva HR, Ghassemi H, Nowruzi H. 2018. Seakeeping performance estimation of the container ship under irregular wave condition using artificial neural network. Am J Civil Eng Architecture. 6(4):147–153. doi: 10.12691/ajcea-6-4-3
   Google Scholar
• Wang C, Shi W, Wang X, Jiang X, Yang Y, Li W, Zhou L. 2017. Optimal design of multistage centrifugal pump based on the combined energy loss model and computational fluid dynamics. Appl Energy. 187:10–26. doi: 10.1016/j.apenergy.2016.11.046
   Web of Science ®Google Scholar
• Wu P, Lai Z, Wu D, Wang L. 2014. Optimization research of parallel pump system for improving energy efficiency. J Water Res Planning Manage. 141(8):04014094. doi: 10.1061/(ASCE)WR.1943-5452.0000493
   Web of Science ®Google Scholar
• Yumurtaci Z, Sarıgul A. 2011. Energy efficiency of centrifugal pumps and its applications (paper in Turkish). Makina Mühendisleri Odası Tesisat Mühendisliği Dergisi. 122:49–58.
   Google Scholar