Abstract
Neural networks are a tool that can be used for the modelling of many systems and process behavior. The artificial neural networks can “understand” the information from health care processes. For the estimations between these two concepts we use intuitionistic fuzzy sets. Here, for the learning process of the neural networks, we will use 60 heavy oils that have been characterized for their distillation characteristics by ASTM D-5236 and ASTM D-1160 in the Research laboratory of LUKOIL Neftochim Burgas. The aim is to recognize the type of crude oil based on six of their properties.
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References
ASTM D-1160-06, Standard Test Method for Distillation of Petroleum Products at Reduced Pressure
ASTM D-2892-05, Standard Test Method for Distillation of Crude Petroleum (15-Theoretical Plate Column)
ASTM D-5236-03, Standard Test Method for Distillation of Heavy Hydrocarbon Mixtures (Vacuum Potstill Method)
Atanassov, K.: Intuitionistic Fuzzy Sets. Springer, Heidelberg (1999)
Atanassov, K.: On Intuitionistic Fuzzy Sets Theory. Springer, Berlin (2012)
Atanassov, K.: Intuitionistic fuzzy sets. In: Proceedings of VII ITKR’s Session, Sofia, June 1983. (in Bulgarian)
Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. Elsevier 20(1), 87–96 (1986)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (2000). ISBN 0 19 853864 2
Golden, S., Barletta, T., White, S.: Myth of high cut point in dry vacuum units, p. 4 (2014). www.digitalrefining.com/article/1000929
Hagan, M.T., Demuth, H.B., Beale, M.: Neural Network Design. PWS Publishing Company, Boston (1996)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)
Kaes, G.L.: Modeling of Oil Refining Processes and Some Practical Aspects of Modeling Crude Oil Distillation, VMGSim User’s Manual. www.virtualmaterials.com
Miquel, J., Hernandez, J., Castells, F.: A new method for petroleum fractions and crude oil characterization. SPE Reservoir Eng. 5, 265–270 (1992)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Sanchez, S., Ancheyta, J., McCaffrey, W.C.: Comparison of probability distribution functions for fitting distillation curves of petroleum. Energy Fuels 21, 2955–2963 (2007)
Satyro, M.A., Yarranton, H.: Oil characterization from simulation of experimental distillation data. Energy Fuels 8, 3960–3970 (2009)
Stratiev, D.S., Dinkov, R.K., Kirilov, K.E.: Evaluation of crude oil data. In: Proceedings of the 43th International Petroleum Conference, Bratislava, 25–26 September 2007
Stratiev, D.S., Dinkov, R.K., Kirilov, K.E., Petkov, K.P.: Method calculates crude properties. Oil Gas J. 1, 48–52 (2008)
Stratiev, D.S., Dinkov, R.K., Nikolaev, N., Stanulov, K.: Evaluation of impact of crude oil quality on refinery profit. Erdoel Erdgas Kohle 1, 17 (2010)
Tovar, L.P., Maciel, M.R.W., Filho, R.M., Batistella, C.B., Celis Ariza, O.J., Medina, L.C.: Overview and computational approach for studying the physicochemical characterization of high-boiling-point petroleum fractions (350 °C+). Oil Gas Sci. Technol. – Rev. IFP Energies Nouv. 5, 451–477 (2012)
Yu, H., Wilamowski, B.M.: Levenberg-Marquardt training. In: The Industrial Electronics Handbook, vol. 5, pp. 1–15 (2011)
Nikolaychuk, E., Stratiev, D., Velkov, I., Veli, A., Sotirov, S., Mitkova, M.: Conversion of heavy oil distillation data from ASTM D-1160 to ASTM D-5236. Pet. Coal 57(3), 266–279 (2015)
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Sotirov, S., Sotirova, E., Stratiev, D., Stratiev, D., Sotirov, N. (2018). An Application of Neural Network to Heavy Oil Distillation with Recognitions with Intuitionistic Fuzzy Estimation. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_27
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DOI: https://doi.org/10.1007/978-3-319-67137-6_27
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