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The Artificial Neural Network: A Tool For Nox Emission Estimation From Marine Engine

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents the preliminary investigations of nitric oxides (NOx) estimation from marine two-stroke engines. The Annex VI to Marpol Convention enforce to ship-owners necessity of periodical direct measurements of the NOx emission from the ship engines. It is very expensive procedure but with a low accuracy. Presented investigations show the possibility of estimation the NOx emission without direct measurements but using the artificial neural network (ANN). The paper presents method of choice the input data influenced on NOx emission and configuration of ANN and effects of calculations. The input data poses 15 parameters of engine working, influencing on NOx emission. The output data, necessary to learning the network, were NOx concentration in engine exhaust gases. We take into account two types of ANN; the 3-layer perceptron (MLP) with number of neurons in the hidden layer from 10 to 20 and the radial basis function neural network (RBF) with number of neurons in the hidden layer from 10 to 80. The input, validation and verification data was obtained from laboratory tests. After procedure of network configuration, the chosen ANN was learned by back propagation and conjugate gradient methods. During this operation the weights of neurons were changed to minimize the root mean square error. We obtained four ANN’s, which allow us to estimate the NOx emission from laboratory engine with accuracy, comparable with Annex VI regulations.
Rocznik
Strony
9--13
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
autor
  • Gdynia Maritime University Department of Engineering Sciences, Morska Street 81-87, 81-225 Gdynia, Poland, tel.+48 58 6901484, fax: +48 58 6901399, jerzy95@am.gdynia.pl
Bibliografia
  • [1] Barlow, R. S., Karpetis, A. N., Frank, J. H., Scalar profiles and no formation in laminar opposed-flow partially premixed methane/air flames, Combustion and Flame No. 127/2001, pp. 2102–2118, Elsevier Science Inc. 2001.
  • [2] Bebara, L., Kermesa, V., Stehlika, P., Canekb, J., Oralc, J., Low NOx burners—prediction of emissions concentration based on design, measurements and modeling, Waste Management No. 22/2002, pp. 443–451, Elsevier Science Inc. 2002.
  • [3] Blasco, J. A., Fueyo, N., Dopazo, C., Ballester, J., Modelling the temporal evolution of a reduced combustion chemical system with an artificial neural network, Combustion and Flame, Vol 113, Elsevier, 1998.
  • [4] Bowman, C. T, Hanson, R. K., Gardiner, W. C., Lissianski, V., Frenklach, M., Goldenberg, M., Smith, G. P., Crosley, D. R., Golden, D. M., GRI-Mech 2.11 An optimized detailed chemical reaction mechanism for methane combustion and NO formation and re-burning, Topical Report Gas Research Institute 6/94 - 2/96.
  • [5] Cerri, G., Michelassi, V., Monacchia, S., Pica S., Kinetic combustion neural modelling integrated into computational fluid dynamics, Proc. Instn. Mech. Engrs. Vol. 217 Part A, IMechE, 2003.
  • [6] Curran, H. J., Gaffuri, P., Pitz, W. J., Westbrook, C. K., A comprehensive modeling study of n-heptane oxidation, Combustion and Flame No. 114/1998, pp. 149-177, Elsevier Science Inc. 1998.
  • [7] Egolfopoulos, F. N., Validation of nitrogen kinetics in high pressure fames, Energy Conversion & Management No. 42/2001, pp. 21-34, Elsevier Science Inc. 2001.
  • [8] Hafner, M., Schuler, M., Nelles, O., Isermann, R., Fast neural networks for diesel engine control design, Control Engineering Practice, Vol. 8, Pergamon 2000.
  • [9] Heywood, J. B., Internal Combustion Engine Fundamentals, McGraw-Hill 1988.
  • [10] Heywood, J. B., Sher E., The Two-Stroke Cycle Engine. Its Development, Operation, and Design, Taylor&Francis N. Y. 1999.
  • [11] Interim Guidelines for the Application of the NOx Technical Code, IMO News, No. 1. 2000. pp. 6.
  • [12] Kesgin, U., Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission, Fuel Vol. 83, Elsevier, 2004.
  • [13] Konnov, A. A., Development and validation of a detailed reaction mechanism for the combustion of small hydrocarbons, 28-th Symposium (Int.) on Combustion. Abstr. Symp. Pap. p. 317. Edinburgh 2000.
  • [14] Kowalski, J., Tarelko, W., Nitric Oxides emission estimation based on measuring of work parameters of ship two-stroke engine, Proceedings of 2nd International Conference on Marine Research and Transportation. Ischia Naples Italy. 2007. Session C.
  • [15] Kuo, K. K., Principles of combustion, Wiley. New Jersey 2005.
  • [16] Kyrtatos, N. P., Dimopoulos, G. G., Theotokatos, G. P., Tzanos, E. I., Xiros, N. I., NOx-box: A software sensor for real-time exhaust emissions estimation in marine engine, Proceedings of IMAM 2002, Athens 2002.
  • [17] Lee, S. H., Howlett, R. J., Crua, C., Walters, S. D., Fuzzy logic and neuro-fuzzy modelling of diesel spray penetration: A comparative study, J. Intel. Fuz. Sys. Vol. 18, IOS Press, 2007.
  • [18] Lyle, K. H., Tseng, K. L., Gore, J. P., Laurendeau, N. M., A study of pollutant emission characteristics of partially premixed turbulent jet flames, Combustion and Flame No. 116/1999, pp. 627-639, Elsevier Science Inc. 1999.
  • [19] Masters, T., Practical neural network recipes in C++, Academic Press Inc., 1993.
  • [20] Oladsine, M., Bloch, G., Dovifaaz, X., Neural modelling and control of a diesel engine with pollution constrains, J. Intel. Robotic Systems, Vol. 41, Kluwer Academic Publishers, 2004.
  • [21] Parlak, A., Islamoglu, Y., Yasar, H., Egrisogut, A., Application of artificial neural network to predict fuel consumption and temperature for a diesel engine, Applied Thermal Engineering Vol. 26, Elsevier, 2006.
  • [22] Ramadhas, A. S., Jayaraj, S., Muraleedharan, C., Padmakumari, K., Artificial neural networks used for the prediction of the cetane number of biodiesel, Renewable Energy Vol. 31, Elsevier, 2006.
  • [23] Sayin, C., Ertunc, H. M., Hosoz, M., Kilicaslan, I., Canakci, M., Performance and exhaust emissions of a gasoline engine using artificial neural network, Applied Thermal Engineering Vol. 27, Elsevier, 2007.
  • [24] Shenvi, N., Geremia, J. M., Rabitz, H., Efficient chemical kinetic modelling through neural network maps, Journal of Chemical Phisics Vol. 120, No 21, American Institute of Physics, 2004.
  • [25] Stephan, V., Debes, K., Gross, H-M., Wintrich, F. Wintrich, H., A new control scheme for combustion processes using reinforcement learning based on neural networks, Int. J. Comput. Intel. Appl. Vol. 1, No 2, Imperial College Press, 2001.
  • [26] Thompson, G. J., Atkinson, C. M., Clark, N. N., Long, T. W., Hanzevack, E., Neutal network modelling of the emissions and performance of heavy-duty diesel engine, Proc. Instn. Mech. Engrs. Vol. 214 Part D, IMechE, 2000.
  • [27] Wang, S., Yu, D.L., Adaptive RBF network for parameter estimation and stable air–fuel ratio control, Neural Networks No 21/2008, pp. 102-112, Elsevier Science Inc. 2008.
  • [28] Wang, W., Chirwa, E. C., Zhou, E., Holmes, K., Nwagboso C., Fuzzy neural ignition timing control for a natural gas fuelled spark ignition engine, Proc. Instn. Mech. Engrs. Vol. 215 Part D, IMechE 2001.
  • [29] Werbos, P., Beyond regression: New tools for prediction and analysis in the behavioural sciences, Ph.D. Thesis, Harvard University 1974.
  • [30] Yang, H., Ring, Z., Briker, Y., McLean, N., Friesen, W., Fairbridge, C., Neural network prediction of cetane number and density of diesel fuel from its chemical composition determined by LC and GC-MS, Fuel Vol. 81, Elsevier, 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0008-0032
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