Development of a neural network model for selective catalytic reduction (SCR) catalytic converter and ammonia dosing optimization using multi objective genetic algorithm

Chemical Engineering Journal

Volumn 165, Issue 2, 2010, Pages 508-516

  Faghihi, Ehsan Majd ^a   Shamekhi, Amir H ^a  

^a K N TOOSI UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Genetic algorithm; Neural network; NOx emission control; Optimization; SCR catalytic converter

Indexed keywords

AMMONIA CONCENTRATIONS; CALIBRATION TESTS; MULTI-OBJECTIVE GENETIC ALGORITHM; NEURAL NETWORK MODEL; NOX EMISSION CONTROL; OPTIMIZATION PROCESS; SCR CATALYTIC CONVERTER; STEADY STATE;

AMMONIA; CATALYTIC CONVERTERS; CONCENTRATION (PROCESS); EMISSION CONTROL; GENETIC ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; REDUCTION; STATISTICAL TESTS;

SELECTIVE CATALYTIC REDUCTION;

EID: 78349311965     PISSN: 13858947     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cej.2010.09.055     Document Type: Article

References (19)

1. Greening P. European vehicle emission legislation-present and future. Top. Catal. 2001, 16/17:5-13.
2. http://www.dieselnet.com/standards.
3. Q. Song, G. Zhu, Model-based Closed-Loop Control of Urea SCR Exhaust Aftertreatment System for Diesel Engine. SAE Paper No. 2002-01-0287, 2002.
4. x SCR catalysis. Appl. Catal. A 2001, 222:221-236.
5. M. Koebel, M. Elsener, G. Madia, Recent Advances in the Development of Urea-SCR for Automotive Applications. SAE Paper No. 2001-01-3625, 2001.
6. P.G. Blakeman, G.R. Chandler, G.A. John, A.J.J. Wilkins, Investigations into NOx Aftertreatment with Urea SCR for Light-Duty Diesel Vehicles. SAE Paper No. 2001-01-3624, 2001.
7. T. Maunula, R. Lylykangas, A. Lievonen, M. Harkonen, NOx Reduction by Urea in the Presence of NO2 on Metal Substrated SCR Catalysts for Heavy-duty Vehicles. SAE Paper No. 2003-01-1863, 2003.
8. J.Y. Kim, G. Cavataio, J.E. Patterson, P.M. Laing, C.K. Lambert, Laboratory Studies and Mathematical Modeling or Urea SCR Catalyst Performance. SAE Paper No. 2007-01-1573, 2007.
9. C. Winkler, P. Florchinger, M.D. Patil, J. Gieshoff, P. Spurk, M. Pfeifer. Modeling of SCR deNOx Catalyst-looking at the Impact of Substrate Attributes. SAE Paper No. 2003-01-0845, 2003.
10. Schiesser W.E., Griffiths G.W. A Compendium of Partial Differential Equations, Method of Line Analysis with MATLAB 2009, Cambridge University Press.
11. Allaire G. Numerical Analysis and Optimization. An Introduction to Mathematical Modeling and Numerical Simulation 2007, Oxford University Press.
12. Chae H.J., Choo S.T., Choi H., Nam I., Yang H.S., Song S.L. Direct use of kinetic parameters for modeling and simulation of a selective catalytic reduction process. Ind. Eng. Chem. Res. 2000, 39:1159-1170.
13. J.N. Chi, H.M. DaCosta, Modeling and Control of a Urea-SCR After-treatment System. SAE Paper No. 2005-01-0966, 2005.
14. Neural Network Toolbox User's Guide 2008, The MathWorks, Inc.
15. T. Hiroyasu, M. Miki, J. Kamiura, S. Watanabe, H. Hiroyasu, Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy using Genetic Algorithms and Phenomenological Model. SAE Paper No. 2002-01-2779, 2002.
16. T. Hiroyasu, M. Miki, M. Kim, S. Watanabe, H. Hiroyasu, H. Miao. Reduction of Heavy Duty Diesel Engine Emission and Fuel Economy with Multi-objective Genetic Algorithm and Phenomenological Model. SAE Paper No. 2004-01-0531, 2004.
17. Konak A., Coit D.W., Smith A.E. Multi-objective optimization using genetic algorithms: a tutorial. RESS 2006, 91:992-1007.
18. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6(2):182-197.
19. Zitzler E., Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 1999, 3(4):257-271.