Open Access
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
Article Number 72
Number of page(s) 10
Published online 23 November 2021
  • Jiang J., Li D. (2016) Theoretical analysis and experimental confirmation of exhaust temperature control for diesel vehicle NOx emissions reduction, Appl. Energy 174, 232–244. [CrossRef] [Google Scholar]
  • Mera Z., Fonseca N., López J.-M., Casanova J. (2019) Analysis of the high instantaneous NOx emissions from Euro 6 diesel passenger cars under real driving conditions, Appl. Energy 242, 1074–1089. [CrossRef] [Google Scholar]
  • Ehsani M., Gao Y., Longo S., Ebrahimi K. (2018) Modern electric, hybrid electric, and fuel cell vehicles, CRC Press, Taylor & Francis Group, Boca Raton, FL. [Google Scholar]
  • The California Low-Emission Vehicle Regulations []. [Google Scholar]
  • Regulation (EC) No 715/2007 of the European Parliament and of the Council of 20 June 2007 on type approval of motor vehicles with respect to emissions from light passenger and commercial vehicles (Euro 5 and Euro 6) and on access to vehicle repair and maintenance information []. [Google Scholar]
  • Lešnik L., Kegl B., Torres-Jiménez E., Cruz-Peragón F. (2020) Why we should invest further in the development of internal combustion engines for road applications, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 75, 56. [CrossRef] [Google Scholar]
  • Chérel J., Zaccardi J.M., Bouteiller B., Allimant A. (2020) Experimental assessment of new insulation coatings for lean burn spark-ignited engines, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 75, 11. [CrossRef] [Google Scholar]
  • Plee S., Ahmad T., Myers J.P., Faeth G.M. (1982) Diesel NOx emissions – A simple correlation technique for intake air effects, in: Symposium (International) on Combustion, Elsevier, pp. 1495–1502. [CrossRef] [Google Scholar]
  • Tullis S., Greeves G. (1996) Improving NOx versus BSFC with EUI 200 using EGR and pilot injection for heavy-duty diesel engines, SAE Trans. 1222–1237. [Google Scholar]
  • Lee K.H. (1997) Trends in technologies of exhaust gas in diesel engines, Auto J. 19, 5, 9–19. [Google Scholar]
  • Smokers R., Vermeulen R., van Mieghem R., Gense R., Skinner I., Fergusson M., MacKay E., Brink P., Fontaras G., Samaras Z. (2006) Review and analysis of the reduction potential and costs of technological and other measures to reduce CO2-emissions from passenger cars, TNO Rep. 6, 1. [Google Scholar]
  • Praveena V., Martin M.L.J. (2018) A review on various after treatment techniques to reduce NOx emissions in a CI engine, J. Energy Inst. 91, 5, 704–720. [CrossRef] [Google Scholar]
  • Yoo J.-H., Kim D.-W., Yoo Y.-S., Eum M.-D. (2009) Study on the characteristics of carbon dioxide emissions factors from passenger cars, Trans. Korean Soc. Autom. Eng. 17, 4, 10–15. [Google Scholar]
  • Tsokolis D., Tsiakmakis S., Dimaratos A., Fontaras G., Pistikopoulos P., Ciuffo B., Samaras Z. (2016) Fuel consumption and CO2 emissions of passenger cars over the New Worldwide Harmonized Test Protocol, Appl. Energy 179, 1152–1165. [CrossRef] [Google Scholar]
  • Schluckner C., Gaber C., Landfahrer M., Demuth M., Hochenauer C. (2020) Fast and accurate CFD-model for NOx emission prediction during oxy-fuel combustion of natural gas using detailed chemical kinetics, Fuel 264, 116841. [CrossRef] [Google Scholar]
  • Li T., Skreiberg Ø., Løvås T., Glarborg P. (2019) Skeletal mechanisms for prediction of NOx emission in solid fuel combustion, Fuel 254, 115569. [CrossRef] [Google Scholar]
  • Ji J., Cheng L., Wei Y., Wang J., Gao X., Fangn M., Wang Q. (2019) Predictions of NOx/N2O emissions from an ultra-supercritical CFB boiler using a 2-D comprehensive CFD combustion model, Particuology 2, 49. [Google Scholar]
  • Falcitelli M., Pasini S., Tognotti L. (2002) Modelling practical combustion systems and predicting NOx emissions with an integrated CFD based approach, Comput. Chem. Eng. 26, 9, 1171–1183. [CrossRef] [Google Scholar]
  • Vihar R.Baškovič U.Ž., Katrašnik T. (2018) Real-time capable virtual NOx sensor for diesel engines based on a two-Zone thermodynamic model, Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles 73, 11. [CrossRef] [Google Scholar]
  • Filippone A., Bojdo N. (2018) Statistical model for gas turbine engines exhaust emissions, Transp. Res. Part D: Transp. Environ. 59, 451–463. [CrossRef] [Google Scholar]
  • Chen S.K., Mandal A., Chien L.-C., Ortiz-Soto E. (2018) Machine learning for misfire detection in a dynamic skip fire engine, SAE Int. J. Engines 11, 2018-01-1158, 965–976. [CrossRef] [Google Scholar]
  • Li N., Lu G., Li X., Yan Y. (2016) Prediction of NOx emissions from a biomass fired combustion process based on flame radical imaging and deep learning techniques, Combust. Sci. Technol. 188, 2, 233–246. [CrossRef] [Google Scholar]
  • Li H., Butts K., Zaseck K., Liao-McPherson D., Kolmanovsky I. (2017) Emissions modeling of a light-duty diesel engine for model-based control design using multi-layer perceptron neural networks, SAE Technical Paper. [Google Scholar]
  • Oduro S., Ha Q.P., Duc H. (2016) Vehicular emissions prediction with CART-BMARS hybrid models, Transp. Res. Part D: Transp. Environ. 49, 188–202. [CrossRef] [Google Scholar]
  • Guardiola C., Pla B., Blanco-Rodriguez D., Calendini P.-O. (2015) ECU-oriented models for NOx prediction. Part 1: A mean value engine model for NOx prediction, Proc. Inst. Mech. Eng. Part D: J. Automobile Eng. 229, 8, 992–1015. [CrossRef] [Google Scholar]
  • Bertram A.M., Kong S.-C. (2017) Augmentation of an Artificial Neural Network (ANN) model with expert knowledge of critical combustion features for optimizing a compression ignition engine using multiple injections, SAE Technical Paper. [Google Scholar]
  • Ganesan V., Porai P.T. (2013) Optimization of fuel injection timing of a gasoline engine using artificial neural network, SAE Technical Paper. [Google Scholar]
  • Lucido M., Shibata J. (2018) Learning gasoline direct injector dynamics using artificial neural networks, SAE Technical Paper. [Google Scholar]
  • Wang G., Awad O.I., Liu S., Shuai S., Wang Z. (2020) NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis, Energy 198, 117286. [CrossRef] [Google Scholar]
  • Arsie I., De Cesare M., Lazzarini F., Pianese C., Sorrentino M. (2017) Neural network models for virtual sensing of NOx emissions in automotive diesel engines with least square-based adaptation, Cont. Eng. Prac. 61, 11–20. [CrossRef] [Google Scholar]
  • Wang Y.-Y., He Y., Rajagopalan S. (2011) Design of engine-out virtual NOx sensor using neural networks and dynamic system identification, SAE Int. J. Engines 4, 1, 828–836. [Google Scholar]
  • Yang G., Wang Y., Li X. (2020) Prediction of the NOx emissions from thermal power plant using long-short term memory neural network, Energy 192, 116597. [CrossRef] [Google Scholar]
  • Xie P., Gao M., Zhang H., Niu Y., Wang X. (2020) Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network, Energy 190, 116482. [CrossRef] [Google Scholar]
  • Hoos H., Hutter F., Leyton-Brown K. (2014) Proc. An efficient approach for assessing hyperparameter importance, in: International Conference on Machine Learning, June 21–June 26, 2014 and in Beijing, China, pp. 754–762. [Google Scholar]
  • Ioffe S., Szegedy C. (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: 32nd International Conference on Machine Learning, Lille, France. [Google Scholar]
  • van Laarhoven T. (2017) L2 regularization versus batch and weight normalization, in: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. [Google Scholar]
  • Haykin S. (1994) Neural networks: A comprehensive foundation, Prentice Hall PTR, Upper Saddle River, NJ, United States. [Google Scholar]
  • Bock S., Goppold J., Wei M. (2018) An improvement of the convergence proof of the ADAM-Optimizer, in: Conference Paper At Oth Clusterkonferenz 2018, 13 April, 2018. [Google Scholar]
  • Balles L., Hennig P. (2017) Dissecting Adam: The sign, magnitude and variance of stochastic gradients, in: 35th International Conference on Machine Learning, Stockholm, Sweden. [Google Scholar]
  • Jacobson S., Reichman D., Bjornstad B., Leslie M., Collins L.M., Malof J.M. (2019) Proc. Reliable training of convolutional neural networks for GPR-based buried threat detection using the Adam optimizer and batch normalization, in: Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIV, International Society for Optics and Photonics, 1101206 p. [Google Scholar]
  • Kingma D.P., Ba J. (2014) Adam: A method for stochastic optimization, in: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA. [Google Scholar]
  • Shopova E.G., Vaklieva-Bancheva N.G. (2006) BASIC – A genetic algorithm for engineering problems solution, Comput. Chem. Eng. 30, 8, 1293–1309. [CrossRef] [Google Scholar]
  • Gelman A., Goodrich B., Gabry J., Vehtari A. (2019) R-squared for Bayesian regression models, Am. Stat. 73, 3, 307–309. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.