1887

Abstract

Summary

Accurate prediction of wax deposition is of vital interest in digitalized systems to avoid issues that interrupt the flow assurance during production of hydrocarbon fluids. The present investigation aims at establishing rigorous intelligent schemes for predicting wax deposition under extensive production conditions. To do so, multilayer perceptron (MLP) optimized with Levenberg-Marquardt algorithm (MLP-LMA) and Bayesian Regularization algorithm (MLP-BR) were established using 88 experimental measurements. The obtained results showed that MLP-LMA achieved the best performance with an overall root mean square error of 0.2198 and a coefficient of determination (R²) of 0.9971. The performance comparison revealed that MLP-LMA outperforms the prior approaches in the literature.

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/content/papers/10.3997/2214-4609.202032021
2020-11-30
2024-04-26
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References

  1. Benamara, C., Nait Amar, M., Gharbi, K., Hamada, B.
    [2019a] Modeling Wax Disappearance Temperature Using Advanced Intelligent Frameworks. Energy & Fuels, 33: 10959–68. doi:10.1021/acs.energyfuels.9b03296.
    https://doi.org/10.1021/acs.energyfuels.9b03296 [Google Scholar]
  2. Benamara, C., Gharbi, K., Nait Amar, M., Hamada, B.
    [2019b] Prediction of Wax Appearance Temperature Using Artificial Intelligent Techniques. Arab J Sci Eng, 1–12. doi:10.1007/s13369‑019‑04290‑y.
    https://doi.org/10.1007/s13369-019-04290-y [Google Scholar]
  3. Elsharkawy, A.M., Al-Sahhaf, T.A., Fahim, M.A.
    [2000] Wax deposition from Middle East crudes. Fuel, 79: 1047–55.
    [Google Scholar]
  4. Ji, H-Y., Tohidi, B., Danesh, A., Todd, A.C.
    [2004] Wax phase equilibria: developing a thermodynamic model using a systematic approach. Fluid Phase Equilibria, 216: 201–17.
    [Google Scholar]
  5. Garcia, M.D.C. and Carbognani, L.
    [2001] Asphaltene- paraffin structural interactions. Effect on crude oil stability. Energy & Fuels, 15: 1021–7.
    [Google Scholar]
  6. Hansen, J.H., Fredenslund, A., Pedersen, K.S., Rønningsen, H.P.
    [1988] A thermodynamic model for predicting wax formation in crude oils. AIChE J, 34: 1937–42.
    [Google Scholar]
  7. Lira-Galeana, C., Firoozabadi, A., Prausnitz, J.M.
    [1996] Thermodynamics of wax precipitation in petroleum mixtures. AIChE J, 42: 239–48.
    [Google Scholar]
  8. Coutinho, J.A.P.
    [1998] Predictive UNIQUAC: a new model for the description of multiphase solidliquid equilibria in complex hydrocarbon mixtures. Ind Eng Chem Res, 37: 4870–5.
    [Google Scholar]
  9. Kamari, A., Khaksar-Manshad, A., Gharagheizi, F., Mohammadi, A.H., Ashoori, S.
    [2013] Robust model for the determination of wax deposition in oil systems. Ind Eng Chem Res, 52: 15664–72.
    [Google Scholar]
  10. Bian, X.Q., Huang, J.H., Wang, Y., Liu, Y.B., Kaushika Kasthuriarachchi, D.T., Huang, L.J.
    [2019] Prediction of Wax Disappearance Temperature by Intelligent Models. Energy and Fuels, 33: 2934–49. doi:10.1021/acs.energyfuels.8b04286.
    https://doi.org/10.1021/acs.energyfuels.8b04286 [Google Scholar]
  11. Hemmati-Sarapardeh, A., Varamesh, A., Husein, M.M., Karan, K.
    [2018] On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment. Renew Sustain Energy Rev, 81: 313–29.
    [Google Scholar]
  12. Haykin, S.
    [2001] Neural Networks and Learning Machines. Third Edition, vol. 40. Pearson Upper Saddle River, NJ, USA. doi:10.1002/1521‑3773(20010316)40:6<9823::AID‑ANIE9823>3.3.CO;2‑C.
    https://doi.org/10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C [Google Scholar]
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