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Received November 18, 2015
Accepted March 18, 2016
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Kriging models for forecasting crude unit overhead corrosion
Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea 1Korea Institute of Industrial Technology, 55 Jongga-ro, Jung-gu, Ulsan 44413, Korea 2Engineering Development Research Center, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Korean Journal of Chemical Engineering, July 2016, 33(7), 1999-2006(8), 10.1007/s11814-016-0083-9
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Abstract
Crude unit overhead corrosion is a major issue in the refinery field. However, the corrosion models in the literature are difficult to apply to real refinery processes due to the characteristics of corrosion. We propose a Kriging model, an advanced statistical tool for geostatistics, to forecast the corrosion rate in a real refinery plant. Instead of spatial coordinates, the proposed model employs the non-spatial coordinates of six key corrosion variables: H2S, Cl., Fe2+, NH3, pH, and flowrate. The Kriging model is compared with two well-known forecasting models, multiple linear regression and an artificial neural network. To overcome the insufficiency of the number of data sets measured in the plant to use the six non-spatial coordinates, the significance probability is applied to reduce the dimensions from six to four. Among all the developed models in this paper, the Kriging model with four corrosion variables showed the best forecasting performance.
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References
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Sun W, Nesic S, Corrosion, 64, 334 (2008)
Kim S, Kim J, Moon I, Ind. Eng. Chem. Res., 50(22), 12626 (2011)
Song FM, Electrochim. Acta, 55(3), 689 (2010)
Gruber T, Scharler R, Obernberger I, Biomass Bioenerg., 79, 145 (2015)
Dhanapal A, Boopathy SR, Balasubramanian V, Mater. Des., 32, 5066 (2011)
Khadom AA, Korean J. Chem. Eng., 30(12), 2197 (2013)
Kim J, Tak K, Moon I, Ind. Eng. Chem. Res., 51(30), 10191 (2012)
Singer M, Brown B, Camacho A, Nesic S, Corrosion, 67, 015004 (2011)
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Lee KJ, A mechanistic modelling of CO2 corrosion of mild steel in the presence of H2S, Ph.D. Dissertation, Ohio University, Athens, OH (2004).
Kim J, Lim W, Lee Y, Kim S, Park SR, Suh SK, Moon I, Ind. Eng. Chem. Res., 50(13), 8272 (2011)
Journel AG, Huijbregts CJ, Mining Geostatistics, 5th Ed., Academic Press, London (1991).
Lang YD, Zitney SE, Biegler LT, Comput. Chem. Eng., 35(9), 1705 (2011)
De Oliveira MA, Possamai O, Valentina LVOD, Flesch CA, Expert Syst. Appl., 40, 272 (2013)
Movagharnejad K, Mehdizadeh B, Banihashemi M, Kordkheili MS, Energy, 36(7), 3979 (2011)
Song K, Lee S, Shin S, Lee HJ, Han C, Ind. Eng. Chem. Res., 53(13), 5539 (2014)
Satya EJ, Chandrakar N, Korean J. Chem. Eng., 33(4), 1318 (2016)
Cho SG, No KT, Goh EM, Kim JK, Shin JH, Joo YD, Seong S, Bull. Korean Chem. Soc., 26, 399 (2005)
