Prediction of methanol loss by hydrocarbon gas phase in hydrate inhibition unit by back propagation neural networks

Document Type : Research Paper


Young Researchers and Elite Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran


Gas hydrate often occurs in natural gas pipelines and process equipment at high pressure and low temperature. Methanol as a hydrate inhibitor injects to the potential hydrate systems and then recovers from the gas phase and re-injects to the system. Since methanol loss imposes an extra cost on the gas processing plants, designing a process for its reduction is necessary. In this study, an accurate back propagation neural network (BPNN) is designed for the prediction of methanol loss by the gas phase as a function of temperature, pressure, and methanol composition in the aqueous phase. Different configurations of BPNN were trained, tested, and a configuration providing the smallest absolute average relative deviation (AARD%) was chosen as an optimum structure. Finally, comparisons made among the accuracy of the developed BPNN model, process simulators, and probabilistic neural network (PNN). Results confirm that the designed BPNN model is more accurate than the other considered predictive tools. The BPNN provided an AARD=5.75% for prediction of experimental data, while Aspen-HYSYS, Aspen-Plus, and PNN presented an AARD% of 9.71, 12.57, and 13.27, respectively.  


[1]     Ghaedi H, Javanmardi J, Rasoolzadeh A, Mohammadi AH. Experimental Study and Thermodynamic Modeling of Methane Hydrate Dissociation Conditions in the Simultaneous Presence of BMIM-BF4 and Ethanol in Aqueous Solution. Journal of Chemical & Engineering Data. 2018 Apr 16;63(5):1724-32.
[2]     Hammerschmidt EG. Formation of gas hydrates in natural gas transmission lines. Industrial & Engineering Chemistry. 1934 Aug 1;26(8):851-5.
[3]     Kvamme B, Selvåg J, Saeidi N, Kuznetsova T. Methanol as a hydrate inhibitor and hydrate activator. Physical Chemistry Chemical Physics. 2018;20(34):21968-87.
[4]     Hammerschmidt EG. Gas hydrate formations, A further study on their prevention and elimination from natural gas pipe lines. Gas. 1939 May;15(5):30-4.
[5]     Covington, Kimberly C., John T. Collie III, and Steven D. Behrens. "Selection of hydrate suppression methods for gas streams." 78th GPA Annual Convention, Nashville, TN. 1999.
[6]     Esteban A, Hernandez V, Lunsford K. Exploit the benefits of methanol. InProceedings of the 79th Gas Processors Association Annual Convention (GPA’00) 2000 Mar.
[7]     Eslamimanesh A, Mohammadi AH, Richon D, Naidoo P, Ramjugernath D. Application of gas hydrate formation in separation processes: A review of experimental studies. The Journal of Chemical Thermodynamics. 2012 Mar 1;46:62-71.
[8]     Iraci LT, Essin AM, Golden DM. Solubility of methanol in low-temperature aqueous sulfuric acid and implications for atmospheric particle composition. The Journal of Physical Chemistry A. 2002 Apr 25;106(16):4054-60.
[9]     Bahadori A, Vuthaluru HB. Prediction of methanol loss in vapor phase during gas hydrate inhibition using Arrhenius-type functions. Journal of loss Prevention in the Process Industries. 2010 May 1;23(3):379-84.
[10]  Bahadori A, Vuthaluru HB. Predictive tool for the estimation of methanol loss in condensate phase during gas hydrate inhibition. Energy & Fuels. 2010 Apr 14;24(5):2999-3002.
[11]  Ghiasi MM, Arabloo M, Bahadori A, Zendehboudi S. Prediction of methanol loss in liquid hydrocarbon phase during natural gas hydrate inhibition using rigorous models. Journal of Loss Prevention in the Process Industries. 2015 Jan 1;33:1-9.
[12]  Yousefinejad S, Eftekhari R, Honarasa F, Zamanian Z, Sedaghati F. Comparison between the gas-liquid solubility of methanol and ethanol in different organic phases using structural properties of solvents. Journal of Molecular Liquids. 2017 Sep 1;241:861-9.
[13]  Teixeira AM, de Oliveira Arinelli L, de Medeiros JL, Ofélia de Queiroz FA. Recovery of thermodynamic hydrate inhibitors methanol, ethanol and MEG with supersonic separators in offshore natural gas processing. Journal of Natural Gas Science and Engineering. 2018 Apr 1;52:166-86.
[14]  Freire MG, Santos LM, Marrucho IM, Coutinho JA. Evaluation of COSMO-RS for the prediction of LLE and VLE of alcohols+ ionic liquids. Fluid Phase Equilibria. 2007 Jul 15;255(2):167-78.
[15]  Ng HJ, Chen CJ. Vapour-liquid and Vapour-liquid-liquid Equilibria for H2S, CO2, Selected Light Hydrocarbons and a Gas Condensate in Aqueous Methanol Or Ethylene Glycol Solutions: GPA Project 905. Gas Processors Association; 1995.
[16]  Ng HJ, Robinson DB. The solubility of methanol or glycol in water-hydrocarbon systems. Gas Processors Association Research Reports. 1988 Mar;117.
[17]  Vaferi B, Eslamloueyan R, Ayatollahi S. Automatic recognition of oil reservoir models from well testing data by using multi-layer perceptron networks. Journal of Petroleum Science and Engineering. 2011 Jun 1;77(3-4):254-62.
[18]  Vaferi B, Samimi F, Pakgohar E, Mowla D. Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes. Powder Technology. 2014 Nov 1;267:1-0.
[19]  Amini Y, Fattahi M, Khorasheh F, Sahebdelfar S. Neural network modeling the effect of oxygenate additives on the performance of Pt–Sn/γ-Al 2 O 3 catalyst in propane dehydrogenation. Applied Petrochemical Research. 2013 Jul 1;3(1-2):47-54.
[20]  Davoudi E, Vaferi B. Applying artificial neural networks for systematic estimation of degree of fouling in heat exchangers. Chemical Engineering Research and Design. 2018 Feb 1;130:138-53.
[21]  Hoseinpour SA, Barati‐Harooni A, Nadali P, Mohebbi A, Najafi‐Marghmaleki A, Tatar A, Bahadori A. Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra‐n‐butylammonium bromide solutions. Journal of Chemometrics. 2018 Feb;32(2):e2956.
[22]  Liu Z, Zuo Q, Wu G, Li Y. An artificial neural network developed for predicting of performance and emissions of a spark ignition engine fueled with butanol–gasoline blends. Advances in Mechanical Engineering. 2018 Jan;10(1):1687814017748438.
[23]  Oparaji U, Sheu RJ, Bankhead M, Austin J, Patelli E. Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems. Neural Networks. 2017 Dec 1;96:80-90.
[24]  Gholami E, Vaferi B, Ariana MA. Prediction of viscosity of several alumina-based nanofluids using various artificial intelligence paradigms-Comparison with experimental data and empirical correlations. Powder Technology. 2018 Jan 1;323:495-506.
[25]  Vaferi B, Eslamloueyan R, Ghaffarian N. Hydrocarbon reservoir model detection from pressure transient data using coupled artificial neural network—Wavelet transform approach. Applied Soft Computing. 2016 Oct 1;47:63-75.
[26]  Dua V. A mixed-integer programming approach for optimal configuration of artificial neural networks. Chemical Engineering Research and Design. 2010 Jan 1;88(1):55-60.
[27]  Specht DF. Probabilistic neural networks. Neural networks. 1990 Jan 1;3(1):109-18.
[28]  Zeinali Y, Story BA. Competitive probabilistic neural network. Integrated Computer-Aided Engineering. 2017 Jan 1;24(2):105-18.
[29]  Yi JH, Wang J, Wang GG. Improved probabilistic neural networks with self-adaptive strategies for transformer fault diagnosis problem. Advances in Mechanical Engineering. 2016 Jan 6;8(1):1687814015624832.
Volume 53, Issue 2
December 2019
Pages 253-264
  • Receive Date: 22 June 2019
  • Revise Date: 25 August 2019
  • Accept Date: 04 September 2019
  • First Publish Date: 01 December 2019