A Method for Pre-Calibration of DI Diesel Engine Emissions and Performance Using Neural Network and Multi-Objective Genetic Algorithm

Document Type: Research Article

Authors

1 Department of Mechanical Engineering, K.N. Toosi University of Technology, Tehran, I.R. IRAN

2 Department of Mechanical Engineering, Iran University of Science and Technology, Tehran, I.R. IRAN

Abstract

Diesel engine emission standards are being more stringent as it gains more publicity in industry and transportation. Hence, designers have to suggest new controlling strategies which result in small amounts of emissions and a reasonable fuel economy. To achieve such a target, multi-objective optimization methodology is a good approach inasmuch as several types of objective are minimized or maximized simultaneously. In this paper, this technique is implemented on a closed cycle two-zone combustion model of a DI (direct injection) diesel engine. The main outputs of this model are the quantity of NOx, soot (which are the two main emissions in diesel engines) and engine performance. The optimization goal is to minimize NOx and soot while maximizing engine performance. Fuel injection parameters are selected as design variables. A neural network model of the engine is developed as an alternative for the complicated and time-consuming combustion model in a wide range of engine operation. Finally design variables are optimized using an evolutionary genetic algorithm, called NSGA-II.

Keywords


[1] Jankovic, A., Valery, W. and Davis, E., Cement Grinding Optimisation, Minerals Engineering,
17, p. 1075 (2004).
[2] Farzanegan, A., Laplante, A.R. and Lowther, D.A., A Knowledge-based System for an Off-Line Optimization of Ball Milling Circuits, Proceedings of 29th CMP Conference, Ottawa, 165-185 (1997).
[3] Farzanegan, A., Knowledge-Based Optimization of Mineral Grinding Circuits, Ph.D. Thesis, McGill University, Montreal, Canada (1998).
[4] Irannajad, M., Farzanegan, A. and Razavian, S.M.,  Spreadsheet-based Simulation of Closed Ball Milling Circuits, Minerals Engineering, 19, 1495 (2006).
[5] Zhang, Y.M., Napier-Munn, T.J. and Kavetsky, A., Application of Comminution and Classification Modelling to Grinding of Cement Clinker, Trans. Inst. Min. Metall., Sect. C, 97, p. 207 (1988).
[6] Benzer, H., Ergün, L., Öner, M. and Lynch, A.J., Simulation of Open Circuit Clinker Grinding.  Minerals Engineering, 14 (7), p. 701 (2001a).
[7] Benzer, H., Ergün L., Lynch, A.J., Öner, M., Günlü, A., Çelik, I.B. and Aydońüan, N.,  Modeling Cement Grinding Circuits, Minerals Enginering, 14 (11), p. 1469 (2001b).
[8] Benzer, H.,  Modeling and Simulation of a Fully Air Swept Ball Mill in a Raw Material Grinding Circuit, Power Technology, 150, p. 145 (2004).
[9] Yadegar, Sh. and Pishvai, M.R., Mixed Qualitative / Quantitative Dynamic Simulation of Processing Systems, Iranian Journal of Chemistry & Chemical Engineering, 24, p. 53 (2005).
[10] Kolacz, J., Investigating Flow Conditions in Dynamic Air Classification, Minerals Engineering, 15, p. 131 (2002).
[11] Karunakumari, L. et al., Experimental and Numerical Study of a Rotating Wheel Air Classifier, AIChE  Journal, 5, p. 776 (2005).
[12] Griffiths W. and Boysan, F., Computational Fluid Dynamics (CFD) and Empirical Modelling of the Performance of a Number of Cyclone Separators, Journal of Aerosol Science, 27, p.  281 (1996).
[13] Wang,  Q.,  Melaaen,  M.C.  and  De  Silva,  S.R., Investigation and Simulation of a Cross-Flow Air Classifier, Powder Technology, 22, p. 273 (2001).
[14] Bakker,  A., Haidari,  A.H. and  Oshinowo, L.M., Realize Greater Benefits from CFD, AIChE Journal, 47, p.  45 (2001).
[15] Gorji  et al., CFD  Modeling  of Gas - Liquid Hydrodynamics in a Stirred Tank Reactor, Iranian Journal of Chemistry & Chemical Engineering, 42, p. 85 (2007).
[16] Nageswararao, K.,  Wiseman,  D.M.  and  Napier-Munn, T.J., Two Empirical Hydrocyclone Models Revisited, Minerals Engineering,17, p. 671 (2004).
[17] Napier-Munn, T.J., Morrell, S., Morrison, R.D. and Kojovic, T., “Mineral Comminution Circuits:
Their Operation and Optimization”, JKMRC, The University of Queensland (1999).
[18] Plitt, L.R., The Analysis of Solid-Solid Separations in Classifiers, CIM Bulletin, 64, p. 42 (1971).
[19] Finch, J.A., Modelling a Fish-Hook in Hydrocyclone Selectivity Curves, Powder Technology, 36, p. 128 (1983).
[20] Del Villar, R. and Finch, J.A.,  Modelling the Cyclone Performance with a Size Dependent Entrainment Factor, Minerals Engineering, 5 (6), p. 661 (1992).
[21]  Frachon, M. and  Cilliers, J. J.,  A  General Model for Hydrocyclone Partition Curves, Chemical Engineering Journal, 73, p. 53 (1999).
[22] Nageswararao K.,  A Critical Analysis of the Fish-Hook Effect in Hydrocyclone Classifiers, Chemicals Engineering Journal, 80, p. 251 (2000).
[23] Majumder, A.K., Shah, H., Shukla, P. and Barnwal, J.P., Effect of Operating Variables on Shape 
of “Fish-Hook” Curves in Cyclones, Minerals Engineering, 20, p. 204 (2007).
[24] Majumder, A.K., Yerriswamy, P. and Barnwal, J.P.,  The “Fish-Hook” Phenomenon in Centrifugal Separation of Fine Particles, Minerals Engineering, 16, p. 1005 (2003).
[25] Shah,  H.,  Majumder,  A.K.,  Barnwal,  J.P.  and Shukla, P.,  “New Understanding on “Fish-Hook” Effect in Hydrocyclone”, Proceedings of MPT 2007, pp. 425-428 (2007).
[26] Lynch et al., Simulation of Closed Circuit Clinker Grinding, Zement Kalk Gibs (English Translation), 53, p. 560 (2001)
[27] Spring, R.,  NORBAL 3: Software  for  Material Balance Reconciliation, Center de Recherché Noranda, Point-Claire, Quebec (1992).