Jump to ContentJump to Main Navigation
Show Summary Details
More options …

International Journal of Chemical Reactor Engineering

Ed. by de Lasa, Hugo / Xu, Charles Chunbao

IMPACT FACTOR 2017: 0.881
5-year IMPACT FACTOR: 0.908

CiteScore 2017: 0.86

SCImago Journal Rank (SJR) 2017: 0.306
Source Normalized Impact per Paper (SNIP) 2017: 0.503

See all formats and pricing
More options …
Volume 14, Issue 2


Volume 17 (2019)

Volume 9 (2011)

Volume 8 (2010)

Volume 7 (2009)

Volume 6 (2008)

Volume 5 (2007)

Volume 4 (2006)

Volume 3 (2005)

Volume 2 (2004)

Volume 1 (2002)

Experimental Study and Mathematical Modeling of Propane-SCR-NOx Using Group Method of Data Handling and Artificial Neural Network

N. Ghasemian
  • Corresponding author
  • Department of Polymer Science and Engineering, University of Bonab, P.O. Box 55517–61167, Bonab, Iran
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ H. Nourmoradi
  • Department of Environmental Health Engineering, School of Health, Ilam University of Medical Sciences, Ilam, Iran
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-02-16 | DOI: https://doi.org/10.1515/ijcre-2015-0159


In this study, the catalytic behavior of protonated clinoptilolite in propane-SCR-NOx was investigated. The experiments were carried out in the temperature range of 200–500 °C as a function of zeolite mesh size 20, 35 and 70 at different weights of zeolite (0.45–1 g) and flow rates (300–600 ml/min) and consequently at various gas hourly space velocities (GHSV). Group method of data handling (GMDH) and artificial neural network (ANN) system were applied for mathematical modeling of NOx conversion to N2 in propane-SCR-NOx. The operating temperature (T), volumetric flow rate (F) and the weight of clinoptilolite zeolite (W) and the conversion of NOx to N2 (X) were considered as the inputs and output, respectively. In order to evaluate the models performance, conversions of NOx obtained from the GMDH and ANN systems were compared with those obtained from the experimental method. It is concluded that the ANN could successively estimate the conversion and the results were in a good agreement with the experimental data.

Keywords: NOx conversion; propane-SCR-NOx; model; GMDH; artificial neural network


  • 1. Bogdan, S., Gosak, D., Vasic-Racki, D., 1995. Mathematical Modeling of Liquid-Liquid Equilibria in Aqueous Polymer Solution Containing Neutral Proteinase and Oxytetracycline Using Artificial Neural Network Comput. J. Chem. Eng. 19, 791–796.Google Scholar

  • 2. Capek, L., Dedecek, J., Wichterlova, B., 2004. Co-Beta Zeolite Highly Active in Propane –SCR-NOx in the Presence of Water Vapor: Effect of Zeolite Preparation and Al Distribution in the Framework. J. Catal. 227, 352–366.Google Scholar

  • 3. Feeley, J.S., Deeba, M., Farrauto, R.J., Beri, G., Haynes, A., 1995. Lean NOx Reduction with Hydrocarbons Over Ga/S-ZrOx and S-GaZr/zeolite Catalysts. Appl. Catal. B Environ. 6, 79–96.Google Scholar

  • 4. Ganguly, S., 2003. Prediction of VLE Data Using Radial Basis Function Network. J. Comput. Chem. Eng. 27, 1445–1454.Google Scholar

  • 5. Ghanadzadeh, H., Ganji, M., Fallahi, S., 2012. Mathematical Model of Liquid–Liquid Equilibrium for a Ternary System Using the GMDH-Type Neural Network and Genetic Algorithm. Appl. Math. Model. 36, 4096–4105.Web of ScienceGoogle Scholar

  • 6. Ghasemian, N., Falamaki, C., Kalbasi, M., 2014. Clinoptilolite Zeolite as a Potential Catalyst for Propane-SCR-NOx: Performance Investigation and Kinetic Analysis. Chem. Eng. J. 236, 464–470.Web of ScienceGoogle Scholar

  • 7. Ghasemian, N., Kalbasi, M., Pazuki, G.R., 2013. Experimental Study and Mathematical Modeling of Solubility of CO2 in Water: Application of Artificial Neural Network and Genetic Algorithm. J. Disper. Sci. Technol. 34, 347–355.Web of ScienceGoogle Scholar

  • 8. Huang, K., Chen, F.Q., Lu, D.W., 2001. Artificial Neural Network Aided Design of a Multi-Component Catalyst for Methane Oxidative Coupling. Appl. Catal. A Gen. 219, 61–68.Google Scholar

  • 9. Ivakhnenko, A.G., 1966. Group Method of Data Handling- a Rival of the Method of Stochastic Approximation. Sov. Automatic. Control. 13, 43–71.Google Scholar

  • 10. Ivakhnenko, A.G., 1971. Polynomial Theory of Complex Systems. IEEE Trans. Syst. Man Cybern. 1, 364–378.Google Scholar

  • 11. Izadkhah, B., Nabavi, S., Niaei, A., Salari, D., Mahmuodi Badiki, T., Caylak, N., 2012. Design and Optimization of Bi-Metallic Ag-ZSM5 Catalysts for Catalytic Oxidation of Volatile Organic Compounds. J. Ind. Eng. Chem. 18, 2083–2091.Google Scholar

  • 12. Ketabchi, S., Ghanadzadeh, H., Ghanadzadeh, A., Fallahi, S., Ganji, M., 2010. Estimation of VLE of Binary Systems (Tert-Butanol + 2-Ethyl-1-Hexanol) and (n-Butanol + 2- Ethyl-1- Hexanol) Using GMDH-Type Neural Network. J. Chem. Thermodyn. 42, 1352–1355.Google Scholar

  • 13. Kiovski, J.R., Koradia, P.B., 1980. Catalytic Reduction of Oxides of Nitrogen by Ammonia in Presence of Modified Clinoptilolite, US patent, 7944641.

  • 14. Levenberg, K., 1944. A Method for the Solution of Certain Problems in Least Squares. Appl. Math. 2, 164–168.Google Scholar

  • 15. Li, Z., Flytzani-Stephanopoulos, M., 1997. Selective Catalytic Reduction of Nitric Oxide by Methane over Cerium and Silver Ion-Exchanged ZSM-5 Zeolites. Appl. Catal. A Gen. 165, 15–34.Google Scholar

  • 16. Marquardt, D.W., 1963. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J. Soc. Ind. Appl. Math. 11, 431–441.Google Scholar

  • 17. Mjalli, F.S., 2005. Neural Model-Based Predictive Control of Liquid-Liquid Extraction Contractors. J. Chem. Eng. Sci. 60, 239–253.Google Scholar

  • 18. Moreno-Tost, R., Santamaria-Gonzalez, J., Rodriguez-Castellon, E., Jimenez-Lopez, A., Autie, M.A., Gonzalez, E., Carreras Glacial, M., De las Pozas, C., 2004. Selective Catalytic Reduction of Nitric Oxide by Ammonia over Cu-Exchanged Cuban Natural Zeolites. Appl. Catal. B Environ. 50, 279–288.Google Scholar

  • 19. Mousavi, S.M., Niaei, A., Salari, D., Panahi, P.N., Samandari, M., 2013. Modelling and Optimization of Mn/Activate Carbon Nanocatalysts for NO Reduction: Comparison of RSM and ANN Techniques. Environ. Technol. 34, 1377–1384.Web of Science

  • 20. Nakhostin Panahi, P., Niaei, A., Tseng, H.-H., Salari, D., Mousavi, M., 2015. Modeling of Catalyst Composition–Activity Relationship of Supported Catalysts in NH3–NO-SCR Process Using Artificial Neural Network. Neural. Comput. Appl. 26, 1515–1523.Web of ScienceGoogle Scholar

  • 21. Nariman-Zadeh, N., Jamali, A., 2007. Pareto Genetic Design of GMDH-Type Neural Networks for Nonlinear Systems, in: Drchal, J., Koutnik, J. (Eds.), Proceedings of the International Workshop on Inductive Modelling, Czech Technical University, Prague, Czech Republic, pp. 96–103.Google Scholar

  • 22. Ohtsuka, H., Tabata, T., 1999. Effect of Water Vapor on the Deactivation of Pd-Zeolite Catalysts for Selective Catalytic Reduction of Nitrogen Monoxide by Methane. Appl. Catal. B Environ. 21, 133–139.Google Scholar

  • 23. Onwubolu, G.C., 2009. Hybrid Self-Organizing Modeling Systems, Springer, Berlin.Google Scholar

  • 24. Park, J., Sandberg, I., 1991. Universal Approximation Using Radial-Basis-Function Networks. J. Neural. Comput. 3, 246–257.Google Scholar

  • 25. Pazuki, G.R., Seyfi Kakhki, S., 2013. A Hybrid GMDH Neural Network to Investigate Partition Coefficients of Penicillin G Acylase in Polymer–Salt Aqueous Two-Phase Systems. J. Mol. Liq. 188, 131–135.Web of ScienceGoogle Scholar

  • 26. Powell, M.J.D., Mason, J.C., Cox, M.G. (Eds.), 1987. Radial Basis Functions for Multivariable Interpolation: A Review in Algorithms for Approximation, Clarendon Press, Oxford, UK.Google Scholar

  • 27. Reyhani, S.Z., Ghanadzadeh, H., Puigjaner, L., Recances, F., 2009. Estimation of Liquid–Liquid Equilibrium for a Quaternary System Using the GMDH Algorithm. J. Ind. Eng. Chem. Res. 48, 2129–2134.Web of ScienceGoogle Scholar

  • 28. Royaee, S.J., Falamaki, C., Sohrabi, M., Ashraf, S.S., 2008. A New Langmuir–Hinshelwood Mechanism for the Methanol to Dimethylether Dehydration Reaction over Clinoptilolite-Zeolite Catalyst. Appl. Catal. A General. 338, 114–120.Web of ScienceGoogle Scholar

  • 29. Sheikholeslami, M., Bani Sheykholeslami, F., Khoshhal, S., Mola-Abasia, H., Ganji, D.D., Rokni, H.B., 2014. Effect of Magnetic Field on Cu–Water Nanofluid Heat Transfer Using GMDH-Type Neural Network. Neural. Comput. Appl. 25, 171–178.Web of ScienceGoogle Scholar

  • 30. Tamura, T., Kumagai, M., 1991. Method for Removing Nitrogen Oxides from Exhaust Gases. US patent, 5041272.

  • 31. Tasdemir, S., Saritas, I., Ciniviz, M., Allahverdi, N., 2011. Artificial Neural Network and Fuzzy Expert System Comparison for Prediction of Performance and Emission Parameters on a Gasoline Engine. Expert. Syst. Appl. 38, 13912–13923.Web of ScienceGoogle Scholar

  • 32. Torrecilla, J.S., Deetlefs, M., Seddon, KR., Rodríguez, F., 2008. Estimation of Ternaryliquid-Liquid Equilibria for Arene/Alkane/Ionic Liquid Mixtures Using Neural Networks. Phys. Chem. Chem. Phys. 10, 5114–5120.Web of ScienceGoogle Scholar

  • 33. William, W.H., 1985. Catalyst for Reduction of Nitrogen Oxides. US patent, 789690.

  • 34. Yentys, A., Lercher, J.A., van Bekkum, H., Flanigen, E.M., Jacobs, P.A., Jansen, J.C., (Eds.), 2001. Introduction to Zeolite Science and Practice, Amsterdam: Elsevier, pp. 345.Google Scholar

  • 35. Zhang, H., Wang, J., Wang, Y.Y., 2016. Optimal Dosing and Sizing Optimization for a Ground Vehicle Diesel Engine Two-Cell Selective Catalytic Reduction System. IEEE Trans. Veh. Technol. pp. 1–1 doi:.Crossref

About the article

Published Online: 2016-02-16

Published in Print: 2016-04-01

Citation Information: International Journal of Chemical Reactor Engineering, Volume 14, Issue 2, Pages 559–569, ISSN (Online) 1542-6580, ISSN (Print) 2194-5748, DOI: https://doi.org/10.1515/ijcre-2015-0159.

Export Citation

©2016 by De Gruyter.Get Permission

Comments (0)

Please log in or register to comment.
Log in