Skip to main content
SpringerLink
Log in

Modeling and sensitivity analysis of NOx emissions and mechanical efficiency for diesel engine

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

This paper involves discovering effective and better reaction of the diesel engine at various velocities by having ideal values in a short period. Therefore, gene expression programming is used for modeling and presenting governing expression for the related factors. The effective parameters consist of engine speed, intake air temperature, rate of air over fuel, fuel mass, NOx emission, mechanical efficiency, and immediate infusion diesel engine used as a part of demonstrating. Gene expression programming and its values exactly predict output results and present precise formula. Moreover, the sensitivity analysis was performed to recognize the effectiveness of the factors for reducing NOx and increasing mechanical efficiency. In the sensitivity analysis, the methods such as partial correlation coefficient, standard regression coefficient, and the Sobol’-Jansen and distributed evaluation of local sensitivity analysis are all used. The obtained results show that air/fuel rate is more influential factor in both NOx emission and mechanical efficiency. Moreover, the difference between results of standard regression or partial correlation coefficients and Sobol’-Jansen or distributed evaluation methods is in nonlinearity effect or interactions among the factors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Adeyinka AL (2007) Applications of sensitivity analysis in petroleum engineering

  • Alonso JM, José MJM, José MJM, Fernando Alvarruiz JM, José M, Desantes L, Hernandez V, Hernandez GM, Hernández L et al (2007) Combining neural networks and genetic algorithms to predict and reduce diesel engine emissions. IEEE Trans Evol Comput 11(1):46–55. https://doi.org/10.1109/TEVC.2006.876364

    Article  Google Scholar 

  • Arcaklioglu E, Çelıkten İ (2005) A diesel engine’s performance and exhaust emissions. Appl Energy 80:11–22

    Article  CAS  Google Scholar 

  • Arsie I, Cricchio A, 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. Control Eng Pract 61:11–20. https://doi.org/10.1016/j.conengprac.2017.01.005

    Article  Google Scholar 

  • Aster, R. C., Borchers, B., & Thurber, C. H. (2018). Parameter estimation and inverse problems. Elsevier

  • Benajes, J, Molina S, García JM (2001) Influence of pre-and post-injection on the performance and pollutant emissions in a HD diesel engine

  • Burke RD, Baumann W, Akehurst S, Brace CJ (2013) Dynamic modelling of diesel engine emissions using the parametric Volterra series. Proceed Instit Mech Eng D 228(2):164–179. https://doi.org/10.1177/0954407013503629

    Article  CAS  Google Scholar 

  • Chakraborty A, Roy S, Banerjee R (2016) An experimental based ANN approach in mapping performance-emission characteristics of a diesel engine operating in dual-fuel mode with LPG. J Nat Gas Sci Eng 28:15–30. https://doi.org/10.1016/j.jngse.2015.11.024

    Article  CAS  Google Scholar 

  • D’Ambrosio S, Finesso R, Fu L, Mittica A, Spessa E (2014) A control-oriented real-time semi-empirical model for the prediction of NOx emissions in diesel engines. Appl Energy 130:265–279. https://doi.org/10.1016/j.apenergy.2014.05.046

    Article  CAS  Google Scholar 

  • Draper, Smith (1981) Applied regression analysis. 2. https://doi.org/10.1080/03610929808832244

  • Edwards SP, Wynn HP, Grove DM. (2000) Statistics for engine optimization

  • Ferreira C. (2002). “Gene expression programming in problem solving.” In Soft computing and industry, 635–53. Springer

  • Freer J, Beven K, Ambroise B (1996) Bayesian estimation of uncertainty in runoff prediction and the value of data: an application of the GLUE approach. Water Resour Res 32(7):2161–2173. https://doi.org/10.1029/96WR03723

    Article  Google Scholar 

  • Havenith, C., Needham, J. R., Nicol, A. J., & Such, C. H. (1993). Low emission heavy duty diesel engine for Europe(No. 932959). SAE Technical Paper

  • He Y, Rutland CJ (2004) Application of artificial neural networks in engine modelling. Int J Engine Res 5:281–296

    Article  Google Scholar 

  • Herman JD, Reed PM, Wagener T (2013) Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior. Water Resour Res 49(3):1400–1414. https://doi.org/10.1002/wrcr.20124

    Article  Google Scholar 

  • Herzog, P. L., Bürgler, L., Winklhofer, E., Zelenka, P., & Cartellieri, W. (1992). NO x reduction strategies for DI diesel engines. SAE transactions, 820–836

  • Heywood J (1988) Internal combustion engine fundamentals. McGraw-Hill Education

  • Hill MC, Tiedeman CR (2007) Effective groundwater model calibration: with analysis of data, sensitivities, predictions, and uncertainty. Books.Google.Com. 46. https://doi.org/10.1111/j.1745-6584.2007.00398.x

  • Hornberger, G. M., & Spear, R. C. (1981). Approach to the preliminary analysis of environmental systems. J. Environ. Mgmt., 12(1), 7–18.

  • Jansen MJW (1999) Analysis of variance designs for model output. Comput Phys Commun 117(1):35–43. https://doi.org/10.1016/S0010-4655(98)00154-4

    Article  CAS  Google Scholar 

  • Liu B, Hu J, Yan F, Fiifi Turkson R, Lin F (2016) A novel optimal support vector machine ensemble model for NOx emissions prediction of a diesel engine. Measurement 92:183–192. https://doi.org/10.1016/j.measurement.2016.06.015

    Article  Google Scholar 

  • Menke W (1984) “Geophysical data analysis: discrete inverse theory academic.” New York

  • Mohammadhassani J, Dadvand A, Khalilarya S, Solimanpur M (2015) Prediction and reduction of diesel engine emissions using a combined ANN-ACO method. Appl Soft Comput J 34:139–150. https://doi.org/10.1016/j.asoc.2015.04.059

    Article  Google Scholar 

  • Mohammadhassani J, Khalilarya S, Solimanpur M, Dadvand A (2012) Prediction of NOx emissions from a direct injection diesel engine using artificial neural network. Modell Simul Eng 2012:1–8. https://doi.org/10.1155/2012/830365

    Article  Google Scholar 

  • Morey CJ, and Mark J. (2000). SAE technical diesel passenger vehicles – can they meet air quality needs and climate change goals ? no. 724. https://doi.org/10.4271/2000-01-1599

  • Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33(2):161. https://doi.org/10.2307/1269043

    Article  Google Scholar 

  • Niu X, Yang C, Wang H, Wang Y (2017) Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine. Appl Therm Eng 111:1353–1364. https://doi.org/10.1016/j.applthermaleng.2016.10.042

    Article  Google Scholar 

  • Oliver DS, Reynolds AC, and Liu N. (2008). Inverse theory for petroleum reservoir characterization and history matching. https://doi.org/10.1017/CBO9780511535642

  • Pirouzpanah V, Kashani BO (1999) Prediction of major pollutants emission in direct - injection dual - fuel diesel and natural - gas engines. SAE Tech Paper Ser (724): 55. https://doi.org/10.4271/1999-01-0841

  • Quaglietta E, Punzo V (2013) Supporting the design of railway systems by means of a Sobol variance-based sensitivity analysis. Transp Res C 34:38–54. https://doi.org/10.1016/j.trc.2013.05.007

    Article  Google Scholar 

  • Roy S, Ghosh A, Das AK, Banerjee R (2015) Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR. Appl Energy 140:52–64. https://doi.org/10.1016/j.apenergy.2014.11.065

    Article  CAS  Google Scholar 

  • Saltelli A, Annoni P, Azzini I, Campolongo F (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput Phys 181:259–270

    Article  CAS  Google Scholar 

  • Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, and Tarantola S. (2008). Global sensitivity analysis: the primer. Wiley

  • Savva NS, Hountalas DT (2014) Evolution and application of a pseudo-multi-zone model for the prediction of NOx emissions from large-scale diesel Engines at Various Operating Conditions. Energy Convers Manag 85:373–388. https://doi.org/10.1016/j.enconman.2014.05.103

    Article  CAS  Google Scholar 

  • Seber GAF, Wild CJ (1989) Nonlinear regression. Wiley, New York

    Book  Google Scholar 

  • Sobol’ IM, Kucherenko S (2010) A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices. Comput Phys Commun 181(7):1212–1217. https://doi.org/10.1016/j.cpc.2010.03.006

    Article  CAS  Google Scholar 

  • Sobol’, I. M., & Kucherenko, S. (2009). Derivative based global sensitivity measures and their link with global sensitivity indices. Mathematics and Computers in Simulation, 79(10), 3009–3017. doi: https://doi.org/10.1016/j.matcom.2009.01.023

  • Sobol’ IM (2001) Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simul 55:271–280

    Article  Google Scholar 

  • Tarantola A (2005). Inverse Problem Theory and Methods for Model Parameter Estimation. https://doi.org/10.1137/1.9780898717921.bm

  • Tiedeman CR, Matthew Ely D, Hill MC, O’Brien GM (2004) A method for evaluating the importance of system state observations to model predictions, with application to the Death Valley regional groundwater flow system. Water Resour Res 40(12):1–14. https://doi.org/10.1029/2004WR003313

    Article  Google Scholar 

  • Umer, M., Ahmad, R., & Chaudhry, I. (2013). Unsupervised artificial neural networks (ANNs) for intelligent pheromone up gradation. further evolution of neural augmented ant colony optimization (NaACO). Life Science Journal, 10(3), 318–327

  • Werkhoven, K., Wagener, T., Reed, P., & Tang, Y. (2008). Characterization of watershed model behavior across a hydroclimatic gradient. Water Resources Research, 44(1). doi:https://doi.org/10.1029/2007wr006271

  • Yekani M, Saber AS, Ahmadi M, Badfar H (2019) Presentation of new thermal conductivity expression for Al 2 O 3 –water and CuO –water nanofluids using gene expression programming (GEP). J Therm Anal Calorim 135(1):195–206. https://doi.org/10.1007/s10973-018-7305-9

    Article  CAS  Google Scholar 

  • Zhan C-s, Song X-m, Xia J, Tong C (2013) An efficient integrated approach for global sensitivity analysis of hydrological model parameters. Environ Model Softw 41:39–52. https://doi.org/10.1016/j.envsoft.2012.10.009

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Urmia University of Technology (UUT), P.O. Box: 57166-419, Urmia, Iran

    Abbas Sharifi & Homayoun Badfar

  2. Department of Industrial Engineering, Urmia University of Technology (UUT), Urmia, Iran

    Mohsen Ahmadi

  3. Department of Science, Urmia University of Technology (UUT), Urmia, Iran

    Mohsen Hosseini

Authors
  1. Abbas Sharifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohsen Ahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Homayoun Badfar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohsen Hosseini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abbas Sharifi.

Additional information

Responsible editor: Philippe Garrigues

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

In the appendix section, the pseudocode of the presented methods is notated.

figure a
figure b

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharifi, A., Ahmadi, M., Badfar, H. et al. Modeling and sensitivity analysis of NOx emissions and mechanical efficiency for diesel engine. Environ Sci Pollut Res 26, 25190–25207 (2019). https://doi.org/10.1007/s11356-019-05613-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-019-05613-0

Keywords

Search

Navigation