130 Development of a numerical model prediction of condensate stabilization unit of south pars gas processing plant


In present study, Least Square Support Vector Machine (LSSVM) and Radial Basis Function (RBF) are employed to develop models to predict Reid vapor pressure of a sour condensate which is the output variable of stabilizer column of Assaluyeh industrial natural gas sweetening plant. A set of 4 input/output plant data each consisting of 660 data has been used to train, optimize, and test the models. Model development that consists of training, optimization and test was performed using randomly selected 80%, 10%, and 10% of available data respectively. Test results from the LSSVM developed model showed to be in better agreement with operating plant data. Squared correlation coefficients for developed models are 0.83 and 0.91 for RBF and LSSVM based results, respectively. According to the results of the present case study, LSSVM could be regarded as a reliable accurate approach for modeling of a natural gas processing plant.

Keyword: Gas sweetening plant, Stabilizer column, Least square support vector machine, RBF


[1] M. Scott, J. Stake, The American Oil and Gas Report, (2013).

[2] E. Moaseri, O. Mostaghisi, A. Shahsavand, B. Bazubandi, M. Karimi, J. Ahmadi, Journal of Natural Gas Science and Engineering, 12 (2013) 34-42.

[3] B. Karimkhani, Z. Khorrami, in:  Society of Petroleum Engineers – Kuwait International Petroleum Conference and Exhibition, KIPCE 2009: Meeting Energy Demand for Long Term Economic Growth, 2009, pp. 316-323.

[4] H. Ghanbarabadi, B. Khoshandam, Journal of Natural Gas Science and Engineering, 22 (2015) 415-420.

[5] A. de Angelis, Applied Catalysis B: Environmental, 113 (2012) 37-42.

[6] N. Moghadam, M. Samadi, International Journal of Chemical Engineering and Applications, 3 (2012) 461-465.

[7] N. Rahmanian, I.B. Ilias, K. Nasrifar, Journal of Natural Gas Science and Engineering, 26 (2015) 730-736.

[8] R. da Silva, R. Cataluña, E.W.d. Menezes, D. Samios, C.M.S. Piatnicki, Fuel, 84 (2005) 951-959.

[9] P.E. Flecher, W.T. Welch, S. Albin, J.B. Cooper, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 53 (1997) 199-206.

[10] A.B. Jensen, C. Webb, Enzyme and Microbial Technology, 17 (1995) 2-10.

[11] V. Chandra Srivastava, RSC Advances, 2 (2012) 759-783.

[12] M. Bonyadi, F. Esmaeilzadeh, D. Mowla, M. Nematollahi, Journal of Petroleum Science and Engineering, 114 (2014) 74-81.

[13] J. Benoy, R.N. Kale, Offshore World, India; Chemtech Foundation, (2010) 34-37.

[14] S. Mokhatab, W.A. Poe, Chapter 6 – Condensate Stabilization, in:  Handbook of Natural Gas Transmission and Processing (Second Edition), Gulf Professional Publishing, Boston, 2012, pp. 239-251.

[15] M.M. Bernhardsen, in, NTNU: Norwegian University of Science and Technology, Trondheim, 2012.

[16] H. Adib, R. Haghbakhsh, M. Saidi, M.A. Takassi, F. Sharifi, M. Koolivand, M.R. Rahimpour, S. Keshtkari, Journal of Natural Gas Science and Engineering, 10 (2013) 14-24.

[17] R. Haghbakhsh, H. Adib, P. Keshavarz, M. Koolivand, S. Keshtkari, Thermochimica Acta, 551 (2013) 124-130.

[18] H. Adib, A. Sabet, A. Naderifar, M. Adib, M. Ebrahimzadeh, Journal of Natural Gas Science and Engineering, 27 (2015) 74-81.

[19] N.M. Kazerooni, H. Adib, A. Sabet, M.A. Adhami, M. Adib, Journal of Natural Gas Science and Engineering, 28 (2016) 365-371.

[20] C. Cortes, V. Vapnik, Machine Learning, 20 (1995) 273-297.

[21] J.A.K. Suykens, T. Van Gestel, J. De Brabanter, Least Squares Support Vector Machines, World Scientific, 2002.

[22] K. Pelckmans, J.A. Suykens, T. Van Gestel, J. De Brabanter, L. Lukas, B. Hamers, B. De Moor, J. Vandewalle, Tutorial. KULeuven-ESAT. Leuven, Belgium, 142 (2002) 1-2.

[23] M. Curilem, G. Acuña, F. Cubillos, E. Vyhmeister, in:  Chemical Engineering Transactions, 2011, pp. 761-766.

[24] H. Adib, F. Sharifi, N. Mehranbod, N.M. Kazerooni, M. Koolivand, Journal of Natural Gas Science and Engineering, 14 (2013) 121-131.

[25] M. Mesbah, E. Soroush, M. Rezakazemi, Development of least square support vector machine model for prediction of natural gas hydrate formation temperature, Chinese Journal of Chemical Engineering, 25 (2017) 1238-1248.

[26] B. Zheing, P. Daoying, S. Youxian, Nonlinear model predictive control based on support vector machine with multi kernel, Chinese Journal of Chemical Engineering, 15 (2007) 691-697.

[27] X. Wang, J. Chen, C. Liu, F. Pan, Chemical Engineering Research and Design, 88 (2010) 415-420.

[28] S. Agarwal, V. Vijaya Saradhi, H. Karnick, Neurocomputing, 71 (2008) 1230-1237.

[29] L.H. Chiang, M.E. Kotanchek, A.K. Kordon, Computers & Chemical Engineering, 28 (2004) 1389-1401.

[30] D.P. Bertsekas, Nonlinear Programming, Athena Scientific, 2016.

[31] B. Mehdizadeh, K. Movagharnejad, Chemical Engineering Research and Design, 89 (2011) 2420-2427.

[32] H.M. Yao, H.B. Vuthaluru, M.O. Tadé, D. Djukanovic, Fuel, 84 (2005) 1535-1542.

[33] A. Eslamimanesh, F. Gharagheizi, M. Illbeigi, A.H. Mohammadi, A. Fazlali, D. Richon, Fluid Phase Equilibria, 316 (2012) 34-45.

[34] A. Kulkarni, V.K. Jayaraman, B.D. Kulkarni, Computers & Chemical Engineering, 29 (2005) 2128-2133.

[35] L. Li, H. Su, J. Chu, Modeling of isomerization of C8 aromatics by online least square support vector machine, Chinese Journal of Chemical Engineering, 17 (2009) 437-444.

[36] J. Ding, Y. Cao, E. Mpofu, Z. Shi, Chemical Engineering Research and Design, 90 (2012) 1197-1207.

[37] S. Lahiri, K. Ghanta, Prediction of pressure drop of slurry flow in pipeline by hybride support vector regression and genetic algorithm model, Chinese Journal of Chemical Engineering, 16 (2008) 841-848.

[38] E. Çomak, A. Arslan, Expert Systems with Applications, 35 (2008) 564-568.

[39] V.N. Vapnik, Statistical learning theory, Wiley, 1998.

[40] X. Peng, Pattern Recognition, 44 (2011) 2678-2692.

[41] G. Zanghirati, L. Zanni, Parallel Computing, 29 (2003) 535-551.

[42] J.R.M. Smits, W.J. Melssen, L.M.C. Buydens, G. Kateman, Chemometrics and Intelligent Laboratory Systems, 22 (1994) 165-189.

[43] F. Miller, A. Vandome, J. Mc Brewster, in, Alpha‐script Publishing, 2011.

[44] F. Marini, Analytica Chimica Acta, 635 (2009) 121-131.