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The CH4 combustion model

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents results of research on the possibility of approximation of the results of calculations using the GriMech 3 kinetic mechanism by an artificial neural network (ANN). Application of kinetic mechanisms for modeling of combustion process in the finite element method requires considerable computing power which is associated with high costs of modeling. It is therefore necessary to seek alternative solutions in this area. The paper focuses on the possibility of application of ANN to approximate the total heat release from the combustion of methane. We built and trained ANN allows the approximation of the total heat release from the combustion process with a mean square error not exceeding 0.04% and the individual error for one result equal to 1.9%. Inputs for this model are the temperature and pressure of the combustion process and 52 mole fractions of chemical species in combusted mixture taken into account in the GriMech 3 kinetic model. For this reason, we tried to build and train the ANN approximating the mentioned mole fractions of chemical species. During the study we tested different configurations of ANN’s, containing different numbers of hidden layers and different numbers of neurons in the output and the hidden layer. The best results were obtained for the approximation of the ANN with one hidden layer containing 38 neurons. It was built and trained 52 ANN’s, one for each chemical species. Unfortunately, even for obtained small values of mean square errors of approximation, errors of individual results often exceed 100% of the results obtained from the kinetic calculations. For this reason, the application of ANN in the presented form to approximate mole fractions of chemical species is impossible.
Rocznik
Strony
95--102
Opis fizyczny
Bibliogr. 15 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] Bowman, C. T, at all., http://www.me.berkeley.edu/gri_mech/
  • [2] Demirbas, A., Biodiesel – a realistic fuel alternative for diesel engines, Springer-Verlag, 2008.
  • [3] Ghobadian, B., Rahimi, H., Nikbakht, A. M., Najafi, G., Yusaf, T. F., Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network, Renewable Energy, Vol. 34, Elsevier Science Inc, 2009.
  • [4] Heywood, J. B., Internal Combustion Engine Fundamentals, McGraw-Hill, 1988.
  • [5] Chopey, N. P., Handbook of chemical engineering calculations, 3-rd edition, McGraw-Hill, 2004.
  • [6] Kowalski, J., Tarełko, W., NOx emission from a two-stroke ship engine. Part 1: Modeling aspect, Applied Thermal Engineering, Vol. 29, No 11-12, pp. 2153 – 2159, Elsevier Science Inc, 2009.
  • [7] Kowalski, J., The CH4 combustion model, Journal of Polish CIMAC, Vol. 4, Gdańsk 2010.
  • [8] Kowalski, J., The NOx emission estimation by artificial neural network: the analyze, Journal of KONES, Vol. 15, No 2, 2008, pp. 225 – 232.
  • [9] Masters, T., Practical neural network recipes in C++, Academic Press Inc, 1993.
  • [10] Svozil, D., Kvasnicka, V., Pospichal, J., Introduction to multi-layer feed-forward neural networks, Chemometrics and intelligent laboratory systems, Vol. 39, Elsevier Science Inc, 1997.
  • [11] Winterbone, D. E., Advanced Thermodynamics for Engineers, Wiley & Sons, 1997.
  • [12] Zienkiewicz, O. C., Taylor, R. L., Zhu, J. Z., The Finite element method, 6-th edition, McGraw-Hill, 2005.
  • [13] Woodward, J. L, Estimating the flammable mass of a vapor cloud, American Institute of Chemical Engineers, 1998.
  • [14] Kowalski, J., Tarełko, W., NOx emission from a two-stroke ship engine. Part 1: Modeling aspect, Applied Thermal Engineering, Vol. 29 No 11-12, pp. 2153 – 2159, Elsevier Science Inc, 2009.
  • [15] Kuo, K. K., Principles of combustion, Wiley & Sons, 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPG8-0035-0011
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