References
- Interim Guidelines for the Application of the NOx Technical Code, IMO News, No. 1. 2000
- 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
- Heywood J.B., Sher E., The Two-Stroke Cycle Engine. Its Development, Operation, and Design, Taylor&Francis N.Y. 1999
- Kowalski J., Tarełko W.: NOx emission from a two-stroke ship engine. Part 1: Modeling aspect. Applied Thermal Engineering. Vol. 29 No 11-12, Elsevier 2009
- Kowalski J., Tarełko W.: NOx emission from a two-stroke ship engine: Part 2 - Laboratory test. Applied Thermal Engineering. Vol. 29 No 11-12, Elsevier 2009
- 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., Edinburgh 2000
- Werbos P., Beyond regression: New tools for prediction and analysis in the behavioural sciences, Ph.D. Thesis, Harvard University 1974
- 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
- 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
- Hafner M., Schuler M., Nelles O., Isermann R., Fast neural networks for diesel engine control design, Control Engineering Practice, Vol. 8, Pergamon 2000
- 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
- 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
- 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
- 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
- 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
- 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
- Cerri G., Michelassi V., Monacchia S., Pica S., Kinetic combustion neural modelling integrated into computational fluid dynamics, Proc. Inst. Mech. Engrs. Vol. 217 Part A, IMechE, 2003
- Shenvi N., Geremia J.M., Rabitz H., Efficient chemical kinetic modelling through neural network maps, Journal of Chemical Physics Vol. 120, No 21. American Institute of Physics, 2004
- Galindo J., Luja‵n J.M., Serrano J.R., Hernandez L., Combustion simulation of turbocharger HSDI Diesel engines during transient operation using neural networks, Applied Thermal Engineering Vol. 25, Elsevier, 2005
- Sen B.A., Menon S., Turbulent premixed flame modeling using artificial neural networks based chemical kinetics, Proceedings of the Combustion Institute Vol. 32, Elsevier, 2009
- Kesgin U., Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission, Fuel Vol. 83, Elsevier, 2004
- 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
- Al-Hinti I., Samhouri M., Al-Ghandoor A., Sakhrieh A., The effect of boost pressure on the performance characteristics of a diesel engine: A neuro-fuzzy approach, Applied Energy Vol. 86, Elsevier, 2009
- 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.
- Wang S., D.L. Yu., Adaptive RBF network for parameter estimation and stable air-fuel ratio control, Neural Networks Vol. 21. Elsevier, 2008
- 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, 2009
- Atashkari K., Nariman-Zadeh N., Gölcü M., Khalkhali A., Jamali A., Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms, Energy Conversion and Management, Vol. 48, Elsevier, 2007
- Najafi G., Ghobadian B., Tavakoli T., Buttsworth D.R., Yusaf T.F., Faizollahnejad M., Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network, Applied Energy Vol. 86, Elsevier, 2009
- Wu J-D., Liu C-H., An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network, Expert Systems with Applications Vol. 36, Elsevier, 2009
- Heywood J.B., Internal Combustion Engine Fundamentals, McGraw-Hill 1988
- 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, Elsevier Science Inc. 2002
- Reynolds J.P., Jeris J., Theodore L., Handbook of chemical and environmental engineering calculations, Willey-Interscience, New Jersey 2007
- Egolfopoulos F.N., Validation of nitrogen kinetics in high pressure fames, Energy Conversion & Management No. 42/2001, Elsevier Science Inc. 2001
- 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, Elsevier Science Inc. 1999
- Kuo K.K., Principles of combustion, Wiley. New Jersey 2005
- 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
- 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, Elsevier Science Inc. 2001
- 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
- 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, Elsevier Science Inc. 1998
- Masters T., Practical neural network recipes in C++, Academic Press Inc., 1993
- Marine Engine IMO Tier II Programme 2009, MAN Netherland 2009
- Kowalski J., The NOx emission estimation by artificial neural network: the results, Journal of KONES, Vol. 15, Warsaw 2008
- Kowalski J., The NOx emission estimation by artificial neural network: the analyze, Journal of KONES, Vol. 15, Warsaw 2008
- 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
- Svozil D., Kvasnicka V., Pospichal J., Introduction to multilayer feed-forward neural networks, Chemometrics and intelligent laboratory systems Vol. 39, Elsevier 1997