IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v119y2014icp405-416.html
   My bibliography  Save this article

Development of a control-oriented model to optimise fuel consumption and NOX emissions in a DI Diesel engine

Author

Listed:
  • Molina, S.
  • Guardiola, C.
  • Martín, J.
  • García-Sarmiento, D.

Abstract

This paper describes a predictive NOX and consumption model, which is oriented to control and optimisation of DI Diesel engines. The model applies the Response Surface Methodology following a two-step process: firstly, the relationship between engine inputs (intake charge conditions and injection settings) and some combustion parameters (peak pressure, indicated mean effective pressure and burn angles) is determined; secondly, engine outputs (NOX and consumption) are predicted from the combustion parameters using NOX and mechanical losses models. Splitting the model into two parts allows using either experimental or modelled combustion parameters, thus enhancing the model flexibility. If experimental in-cylinder pressure is used to obtain combustion parameters, the mean error of predicted NOX and consumption are 2% and 6% respectively, with a calculation time of 5.5ms. Using modelled parameters reduces the calculation time to 1.5ms, with a penalty in the accuracy. The model performs well in a multi-objective optimisation, reducing NOX and consumption in different amounts depending on the objective of the optimisation.

Suggested Citation

  • Molina, S. & Guardiola, C. & Martín, J. & García-Sarmiento, D., 2014. "Development of a control-oriented model to optimise fuel consumption and NOX emissions in a DI Diesel engine," Applied Energy, Elsevier, vol. 119(C), pages 405-416.
  • Handle: RePEc:eee:appene:v:119:y:2014:i:c:p:405-416
    DOI: 10.1016/j.apenergy.2014.01.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261914000403
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2014.01.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aithal, S.M., 2010. "Modeling of NOx formation in diesel engines using finite-rate chemical kinetics," Applied Energy, Elsevier, vol. 87(7), pages 2256-2265, July.
    2. Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    3. Asprion, Jonas & Chinellato, Oscar & Guzzella, Lino, 2013. "A fast and accurate physics-based model for the NOx emissions of Diesel engines," Applied Energy, Elsevier, vol. 103(C), pages 221-233.
    4. Çelik, Veli & Arcaklioglu, Erol, 2005. "Performance maps of a diesel engine," Applied Energy, Elsevier, vol. 81(3), pages 247-259, July.
    5. Arcaklioglu, Erol & Çelikten, Ismet, 2005. "A diesel engine's performance and exhaust emissions," Applied Energy, Elsevier, vol. 80(1), pages 11-22, January.
    6. Saerens, B. & Vandersteen, J. & Persoons, T. & Swevers, J. & Diehl, M. & Van den Bulck, E., 2009. "Minimization of the fuel consumption of a gasoline engine using dynamic optimization," Applied Energy, Elsevier, vol. 86(9), pages 1582-1588, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Du, Wei & Li, Yanjun & Shi, Jianxin & Sun, Baozhi & Wang, Chunhui & Zhu, Baitong, 2023. "Applying an improved particle swarm optimization algorithm to ship energy saving," Energy, Elsevier, vol. 263(PE).
    2. Zhang, Qiang & Ogren, Ryan M. & Kong, Song-Charng, 2016. "A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA," Applied Energy, Elsevier, vol. 165(C), pages 676-684.
    3. Payri, Francisco & López, José Javier & Martín, Jaime & Carreño, Ricardo, 2018. "Improvement and application of a methodology to perform the Global Energy Balance in internal combustion engines. Part 1: Global Energy Balance tool development and calibration," Energy, Elsevier, vol. 152(C), pages 666-681.
    4. Tauzia, Xavier & Maiboom, Alain & Karaky, Hassan, 2017. "Semi-physical models to assess the influence of CI engine calibration parameters on NOx and soot emissions," Applied Energy, Elsevier, vol. 208(C), pages 1505-1518.
    5. Macián, V. & Serrano, J.R. & Piqueras, P. & Sanchis, E.J., 2019. "Internal pore diffusion and adsorption impact on the soot oxidation in wall-flow particulate filters," Energy, Elsevier, vol. 179(C), pages 407-421.
    6. Rahmani, R. & Rahnejat, H. & Fitzsimons, B. & Dowson, D., 2017. "The effect of cylinder liner operating temperature on frictional loss and engine emissions in piston ring conjunction," Applied Energy, Elsevier, vol. 191(C), pages 568-581.
    7. Gu, Jie & Wang, Yingyuan & Hu, Jiancun & Zhang, Kun & Shi, Lei & Deng, Kangyao, 2024. "Real-time prediction of fuel consumption and emissions based on deep autoencoding support vector regression for cylinder pressure-based feedback control of marine diesel engines," Energy, Elsevier, vol. 300(C).
    8. Bahiuddin, Irfan & Mazlan, Saiful Amri & Imaduddin, Fitrian & Ubaidillah,, 2017. "A new control-oriented transient model of variable geometry turbocharger," Energy, Elsevier, vol. 125(C), pages 297-312.
    9. Guan, Cong & Theotokatos, Gerasimos & Zhou, Peilin & Chen, Hui, 2014. "Computational investigation of a large containership propulsion engine operation at slow steaming conditions," Applied Energy, Elsevier, vol. 130(C), pages 370-383.
    10. Serrano, J.R. & Climent, H. & Piqueras, P. & Angiolini, E., 2014. "Analysis of fluid-dynamic guidelines in diesel particulate filter sizing for fuel consumption reduction in post-turbo and pre-turbo placement," Applied Energy, Elsevier, vol. 132(C), pages 507-523.
    11. Di Battista, D. & Cipollone, R., 2016. "Experimental and numerical assessment of methods to reduce warm up time of engine lubricant oil," Applied Energy, Elsevier, vol. 162(C), pages 570-580.
    12. Mingliang Bai & Wenjiang Yang & Dongbin Song & Marek Kosuda & Stanislav Szabo & Pavol Lipovsky & Afshar Kasaei, 2020. "Research on Energy Management of Hybrid Unmanned Aerial Vehicles to Improve Energy-Saving and Emission Reduction Performance," IJERPH, MDPI, vol. 17(8), pages 1-24, April.
    13. Yongming Feng & Haiyan Wang & Ruifeng Gao & Yuanqing Zhu, 2019. "A Zero-Dimensional Mixing Controlled Combustion Model for Real Time Performance Simulation of Marine Two-Stroke Diesel Engines," Energies, MDPI, vol. 12(10), pages 1-19, May.
    14. Bermúdez, Vicente & Serrano, José Ramón & Piqueras, Pedro & Campos, Daniel, 2015. "Analysis of the influence of pre-DPF water injection technique on pollutants emission," Energy, Elsevier, vol. 89(C), pages 778-792.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Najjar, Yousef S.H., 2011. "Comparison of performance of a Greener direct-injection stratified-charge (DISC) engine with a spark-ignition engine using a simplified model," Energy, Elsevier, vol. 36(7), pages 4136-4143.
    2. Yap, Wai Kean & Karri, Vishy, 2011. "ANN virtual sensors for emissions prediction and control," Applied Energy, Elsevier, vol. 88(12), pages 4505-4516.
    3. Ng, Hoon Kiat & Gan, Suyin & Ng, Jo-Han & Pang, Kar Mun, 2013. "Simulation of biodiesel combustion in a light-duty diesel engine using integrated compact biodiesel–diesel reaction mechanism," Applied Energy, Elsevier, vol. 102(C), pages 1275-1287.
    4. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 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," Applied Energy, Elsevier, vol. 140(C), pages 52-64.
    5. Kurt, Hüseyin & Kayfeci, Muhammet, 2009. "Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks," Applied Energy, Elsevier, vol. 86(10), pages 2244-2248, October.
    6. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    7. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    8. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    9. Togun, Necla & Baysec, Sedat, 2010. "Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine," Applied Energy, Elsevier, vol. 87(11), pages 3401-3408, November.
    10. Najafi, G. & Ghobadian, B. & Tavakoli, T. & Buttsworth, D.R. & Yusaf, T.F. & Faizollahnejad, M., 2009. "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network," Applied Energy, Elsevier, vol. 86(5), pages 630-639, May.
    11. Rafael R. Maes & Geert Potters & Erik Fransen & Rowan Van Schaeren & Silvia Lenaerts, 2022. "Influence of Adding Low Concentration of Oxygenates in Mineral Diesel Oil and Biodiesel on the Concentration of NO, NO 2 and Particulate Matter in the Exhaust Gas of a One-Cylinder Diesel Generator," IJERPH, MDPI, vol. 19(13), pages 1-18, June.
    12. Paúl Andrés Molina Campoverde, 2023. "Estimation of Fuel Consumption through PID Signals Using the Real Emissions Cycle in the City of Quito, Ecuador," Sustainability, MDPI, vol. 15(16), pages 1-20, August.
    13. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    14. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    15. Seungha Lee & Youngbok Lee & Gyujin Kim & Kyoungdoug Min, 2017. "Development of a Real-Time Virtual Nitric Oxide Sensor for Light-Duty Diesel Engines," Energies, MDPI, vol. 10(3), pages 1-21, March.
    16. Ali, Mohamed Kamal Ahmed & Fuming, Peng & Younus, Hussein A. & Abdelkareem, Mohamed A.A. & Essa, F.A. & Elagouz, Ahmed & Xianjun, Hou, 2018. "Fuel economy in gasoline engines using Al2O3/TiO2 nanomaterials as nanolubricant additives," Applied Energy, Elsevier, vol. 211(C), pages 461-478.
    17. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    18. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    19. Shivakumar & Srinivasa Pai, P. & Shrinivasa Rao, B.R., 2011. "Artificial Neural Network based prediction of performance and emission characteristics of a variable compression ratio CI engine using WCO as a biodiesel at different injection timings," Applied Energy, Elsevier, vol. 88(7), pages 2344-2354, July.
    20. Li, Yangtao & Khajepour, Amir & Devaud, Cécile & Liu, Kaimin, 2017. "Power and fuel economy optimizations of gasoline engines using hydraulic variable valve actuation system," Applied Energy, Elsevier, vol. 206(C), pages 577-593.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:119:y:2014:i:c:p:405-416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.