The NOx emission estimation by the artificial neural network: the analyze

• Autorzy: Kowalski J.
• 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 method. During this operation the weights of neurons were changed to minimize the root mean square error. We obtained ANN's, which allow us to estimate the NOx emission from laboratory engine with accuracy, comparable with Annex VI regulations.

Słowa kluczowe

EN

emission; NOx; nitric oxides; ANN; artificial neural network; perceptron; ship diesel engine

• Wydawca: Łukasiewicz Research Network - Institute of Aviation ; European Science Society of Powertrain and Transport KONES Poland ; Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONES
• Rocznik: 2008
• Tom: Vol. 15, No. 2
• Strony: 225--232
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Kowalski J.

• Gdynia Maritime University Department of Engineering Sciences Morska Street 81-87, 81-225 Gdynia, Poland tel.[phone], fax: [phone],

Bibliografia

• [1] Interim Guidelines for the Application of the NOx Technical Code, IMO News, No. 1, pp. 6, 2000.
• [2] 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.
• [3] Heywood, J. B., Sher, E., The Two-Stroke Cycle Engine. Its Development, Operation, and Design, Taylor&Francis N.Y., 1999.
• [4] 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,Session C, 2007.
• [5] 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.
• [6] Werbos, P., Beyond regression: New tools for prediction and analysis in the behavioural sciences, Ph.D. Thesis, Harvard University 1974.
• [7] 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.
• [8] 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,
• [9] Hafner, M., Schuler, M., Nelles, O., Isermann, R., Fast neural networks for diesel engine control design, Control Engineering Practice, Vol. 8, Pergamon 2000.
• [10] 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.
• [11] 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.
• [12] 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.
• [13] 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.
• [14] 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 anf Flame, Vol. 113, Elsevier, 1998.
• [15] 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.
• [16] 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.
• [17] 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.
• [18] Kesgin, U., Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission, Fuel Vol. 83, Elsevier, 2004.
• [19] 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
• [20] 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.
• [21] Heywood, J. B., Internal Combustion Engine Fundamentals, McGraw-Hill 1988.
• [22] 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.
• [23] Egolfopoulos, F. N., Validation of nitrogen kinetics in high pressure fames, Energy Conversion & Management No. 42/2001, pp. 21-34, Elsevier Science Inc. 2001.
• [24] 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.
• [25] Kuo, K. K., Principles of combustion, Wiley. New Jersey 2005.
• [26] 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.
• [27] 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.
• [28] 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.
• [29] Masters, T., Practical neural network recipes in C++, Academic Press Inc., 1993.
• [30] Kowalski, J., The NOx emission estimation by artificial neural network: the results, Journal of KONES, Vol. 15, Warsaw 2008.