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Modeling of catalyst composition–activity relationship of supported catalysts in NH3–NO-SCR process using artificial neural network

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Abstract

This paper presents an artificial neural network (ANN) for modeling the relationship between catalyst composition and catalytic performance in the NH3-SCR of NO process. The supported catalysts with different transition metals (Mn, Fe, Co and Cu) and (γ-Al2O3, ZSM5 and SAPO-34) supports were prepared and tested in NH3–NO-SCR reaction to generate required data for neural network development. The ANN was constructed using the experimental dataset, and all the data were integrated using support and metal atomic descriptors for the construction of general catalyst design model. The statistical analysis of the results indicated that the R 2 values for the training and test data were high, more than 0.9, and this indicates that ANN-based model developed in this work can predict catalyst performance correctly. More evaluation of the obtained model revealed that metal has more influence than support on catalyst activity at supported catalysts.

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References

  1. Forzatti P (2001) Present status and perspectives in de-NO x SCR catalysis. J Appl Catal A 222(1):221–236

    Article  Google Scholar 

  2. ACEA (2003) Final report on selective catalytic reduction. http://europa.eu.int/comm/enterprise/automotive/mveg_meetings/meeting94/scr_paper_final.pdf

  3. Koebel M, Strutz EO (2003) Thermal and hydrolytic decomposition of urea for automotive selective catalytic reduction systems: thermochemical and practical aspects. Ind Eng Chem Res 42(10):2093–2100

    Article  Google Scholar 

  4. Sjövall H, Olsson L, Fridell E, Blint RJ (2006) Selective catalytic reduction of NO x with NH3 over Cu-ZSM-5—the effect of changing the gas composition. J Appl Catal B 64(3):180–188

    Article  Google Scholar 

  5. Chmielarz L, Kuśtrowski P, Zbroja M, Rafalska-Łasocha A, Dudek B, Dziembaj R (2003) SCR of NO by NH3 on alumina or titania-pillared montmorillonite various modified with Cu or Co: part I. General characterization and catalysts screening. J Appl Catal B 45(2):103–116

    Article  Google Scholar 

  6. Liu Q, Liu Z, Su J (2010) Al2O3-coated cordierite honeycomb supported CuO catalyst for selective catalytic reduction of NO by NH3: surface properties and reaction mechanism. Catal Today 158(3–4):370–376

    Article  Google Scholar 

  7. Panahi PN, Salari D, Niaei A, Mousavi S (2013) NO reduction over nanostructure M-Cu/ZSM-5 (M: Cr, Mn, Co and Fe) bimetallic catalysts and optimization of catalyst preparation by RSM. J Ind Eng Chem 19(6):1793–1799

    Article  Google Scholar 

  8. Irfan MF, Goo JH, Kim SD (2008) Co3O4 based catalysts for NO oxidation and NO x reduction in fast SCR process. J Appl Catal B 78(3):267–274

    Article  Google Scholar 

  9. Iwasaki M, Yamazaki K, Shinjoh H (2011) NO x reduction performance of fresh and aged Fe-zeolites prepared by CVD: effects of zeolite structure and Si/Al2 ratio. J Appl Catal B 102(1–2):302–309. doi:10.1016/j.apcatb.2010.12.016

    Article  Google Scholar 

  10. Mousavi SM, Niaei A, Illán Gómez MJ, Salari D, Nakhostin Panahi P, Abaladejo-Fuentes V (2014) Characterization and activity of alkaline earth metals loaded CeO2–MO x (M = Mn, Fe) mixed oxides in catalytic reduction of NO. Mater Chem Phys 143(3):921–928. doi:10.1016/j.matchemphys.2013.09.017

    Article  Google Scholar 

  11. Mousavi SM, Salari D, Niaei A, Nakhostin Panahi P, Shafiei S (2014) A modelling study and optimization of catalytic reduction of NO over CeO2–MnO x (0.25)–Ba mixed oxide catalyst using design of experiments. Environ Technol 35(5):581–589. doi:10.1080/09593330.2013.837964

    Article  Google Scholar 

  12. Clark JY, Warwick K (1998) Artificial keys for botanical identification using a multilayer perceptron neural network (MLP). Artif Intell Rev 12(1–3):95–115

    Article  MATH  Google Scholar 

  13. Brace C (1998) Prediction of diesel engine exhaust emissions using artificial neural networks. In: IMechE Seminar S591, neural networks in systems design

  14. Huang K, Chen F-Q, Lü D-W (2001) Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling. J Appl Catal A 219(1–2):61–68. doi:10.1016/S0926-860X(01)00659-7

    Article  Google Scholar 

  15. Izadkhah B, Nabavi S, Niaei A, Salari D, Mahmuodi Badiki T, Çaylak N (2012) Design and optimization of Bi-metallic Ag-ZSM5 catalysts for catalytic oxidation of volatile organic compounds. J Ind Eng Chem 18(6):2083–2091

    Article  Google Scholar 

  16. Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160(3):249–264

    Article  Google Scholar 

  17. Oliveira MLM, Silva CM, Moreno-Tost R, Farias TL, Jiménez-López A, Rodríguez-Castellón E (2011) Modelling of NO x emission factors from heavy and light-duty vehicles equipped with advanced aftertreatment systems. Energy Convers Manag 52(8–9):2945–2951. doi:10.1016/j.enconman.2011.02.025

    Article  Google Scholar 

  18. de Oliveira MLM, Silva CM, Moreno-Tost R, Farias TL, Jiménez-López A, Rodríguez-Castellón E (2009) Simulation of SCR equipped vehicles using iron-zeolite catalysts. J Appl Catal A 366(1):13–21. doi:10.1016/j.apcata.2009.06.020

    Article  Google Scholar 

  19. Oliveira MLM, Silva CM, Moreno-Tost R, Farias TL, Jiménez-López A, Rodríguez-Castellón E (2009) A study of copper-exchanged mordenite natural and ZSM-5 zeolites as SCR–NO x catalysts for diesel road vehicles: simulation by neural networks approach. J Appl Catal B 88(3–4):420–429. doi:10.1016/j.apcatb.2008.10.015

    Article  Google Scholar 

  20. Choudhary VR, Dumbre DK (2009) Magnesium oxide supported nano-gold: a highly active catalyst for solvent-free oxidation of benzyl alcohol to benzaldehyde by TBHP. Catal Commun 10(13):1738–1742. doi:10.1016/j.catcom.2009.05.020

    Article  Google Scholar 

  21. Beale R, Jackson T (1990) Introduction to neural networks. Adam Hilger Ed., Bristol

    Google Scholar 

  22. Holeňa M, Baerns M (2003) Feedforward neural networks in catalysis: a tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction. Catal Today 81(3):485–494

    Article  Google Scholar 

  23. Sasaki M, Hamada H, Kintaichi Y, Ito T (1995) Application of a neural network to the analysis of catalytic reactions Analysis of NO decomposition over Cu/ZSM-5 zeolite. J Appl Catal A 132(2):261–270

    Article  Google Scholar 

  24. Hormik K, Stinchcombe H, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  25. Molga E (2003) Neural network approach to support modelling of chemical reactors: problems, resolutions, criteria of application. Chem Eng Process 42(8):675–695

    Article  Google Scholar 

  26. Günay ME, Yıldırım R (2008) Neural network aided design of Pt-Co-Ce/Al2O3 catalyst for selective CO oxidation in hydrogen-rich streams. Chem Eng J 140(1–3):324–331. doi:10.1016/j.cej.2007.09.047

    Article  Google Scholar 

  27. Wang J, Yu T, Wang X, Qi G, Xue J, Shen M, Li W (2012) The influence of silicon on the catalytic properties of Cu/SAPO-34 for NO x reduction by ammonia-SCR. J Appl Catal B 127:137–147. doi:10.1016/j.apcatb.2012.08.016

    Article  Google Scholar 

  28. Ertl G, Knozinger H, Schuth F, Weitkamp J (2014) Handbook of heterogeneous catalysis. Wiley Online Library, Hoboken

    Google Scholar 

  29. www.webelements.com (11.3.2014)

  30. Kröcher O, Devadas M, Elsener M, Wokaun A, Söger N, Pfeifer M, Demel Y, Mussmann L (2006) Investigation of the selective catalytic reduction of NO by NH3 on Fe-ZSM5 monolith catalysts. J Appl Catal B 66(3):208–216

    Article  Google Scholar 

  31. Garson GD (1991) Interpreting neural-network connection weights. AI Expert 6:47–51

    Google Scholar 

  32. Sultana A, Nanba T, Haneda M, Hamada H (2009) SCR of NO x with NH3 over Cu/NaZSM-5 and Cu/HZSM-5 in the presence of decane. Catal Commun 10(14):1859–1863

    Article  Google Scholar 

  33. Sultana A, Nanba T, Sasaki M, Haneda M, Suzuki K, Hamada H (2011) Selective catalytic reduction of NO x with NH3 over different copper exchanged zeolites in the presence of decane. Catal Today 164(1):495–499

    Article  Google Scholar 

  34. Tang X, Hao J, Xu W, Li J (2007) Low temperature selective catalytic reduction of NO x with NH3 over amorphous MnO x catalysts prepared by three methods. Catal Commun 8(3):329–334

    Article  Google Scholar 

  35. Park TS, Jeong SK, Hong SH, Hong SC (2001) Selective catalytic reduction of nitrogen oxides with NH3 over natural manganese ore at low temperature. Ind Eng Chem Res 40(21):4491–4495

    Article  Google Scholar 

  36. Niaei A, Badiki TM, Nabavi SR, Salari D, Izadkhah B, Çaylak N (2013) Neuro-genetic aided design of modified H-ZSM-5 catalyst for catalytic conversion of methanol to gasoline range hydrocarbons. J Taiwan Inst Chem Eng 44(2):247–256. doi:10.1016/j.jtice.2012.11.008

    Article  Google Scholar 

  37. Fedeyko JM, Chen B, Chen H-Y (2010) Mechanistic study of the low temperature activity of transition metal exchanged zeolite SCR catalysts. Catal Today 151(3):231–236

    Article  Google Scholar 

  38. Busca G, Lietti L, Ramis G, Berti F (1998) Chemical and mechanistic aspects of the selective catalytic reduction of NO x by ammonia over oxide catalysts: a review. J Appl Catal B 18(1):1–36

    Article  Google Scholar 

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Acknowledgments

The authors would like to acknowledge the financial support from University of Tabriz and Iranian Nanotechnology Initiative.

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Authors and Affiliations

  1. Reactor & Catalyst Research Lab., Department of Chemical Engineering and Applied Chemistry, University of Tabriz, Tabriz, Iran

    Aligholi Niaei & Darush Salari

  2. Department of Chemistry, Faculty of Science, University of Zanjan, Zanjan, Iran

    Parvaneh Nakhostin Panahi

  3. School of Occupational Safety and Health, Chung Shan Medical University, Taichung, 402, Taiwan, ROC

    Hui-Hsin Tseng

  4. Faculty of Chemistry, University of Kashan, Kashan, Iran

    Seyed Mahdi Mousavi

Authors
  1. Parvaneh Nakhostin Panahi
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  2. Aligholi Niaei
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  3. Hui-Hsin Tseng
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  4. Darush Salari
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  5. Seyed Mahdi Mousavi
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Correspondence to Aligholi Niaei.

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Nakhostin Panahi, P., Niaei, A., Tseng, HH. et al. Modeling of catalyst composition–activity relationship of supported catalysts in NH3–NO-SCR process using artificial neural network. Neural Comput & Applic 26, 1515–1523 (2015). https://doi.org/10.1007/s00521-014-1781-z

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  • DOI: https://doi.org/10.1007/s00521-014-1781-z

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