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Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel

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  • Can, Özer
  • Baklacioglu, Tolga
  • Özturk, Erkan
  • Turan, Onder

Abstract

In the present study, numerous artificial neural networks were employed to predict the combustion characteristics of a four-stroke, single-cylinder, naturally aspirated diesel engine, including multilayer perceptron (MLP), adaptive neuro-fuzzy interference system (ANFIS) and radial basis function network (RBFN). The actual data derived from measurements and calculations were applied in model training, cross-validation, and testing. Biodiesel fuel ratio, engine load, air consumption, and fuel flow rate data were considered as model-input parameters, which are related to main engine operating variables and also affect the combustion characteristics. These kinds of model-input data were especially preferred due to being commonly direct measurable with the main engine sensors or found in engine maps/look-up tables managed by the electronic control unit (ECU). The main parameters obtained from the direct analysis of the measured in-cylinder pressure data and the heat release analysis results were also determined as model-output parameters. Equally, to ensure a more accurate, straightforward and practical approach to prediction, in every category of neural networks, three training algorithms were adopted, including Levenberg-Marquardt (LM), back propagation (BP) and conjugate gradient (CG). Moreover, a sensitivity analysis was accomplished by assessing the strengths of the links between input and output parameters for every topology. As such, the researcher revealed that all proposed neural networks could predict maximum heat release rate (HRRmax), ignition delay (ID), maximum cylinder pressure (Pmax), maximum cylinder pressure location (θPmax), maximum in-cylinder pressure rise rate (dPmax), indicated mean effective pressure (IMEP), crank angle of center heat release rate (CA50), and combustion duration (CD), with a high accuracy rate. Results of model-output parameters are also important parameters in the combustion diagnostics which are influential on engine thermal efficiency and pollutant formation process. In addition, it is necessary to note that MLP architectures that incorporated the LM algorithm presented superior results. With regards to the optimal ANN model, the linear coefficient values: 0.999848, 0.999847, 0.999955, 0.999780, 0.999378, 0.999929, 0.999766, and 0.999216, were found for ID, HRRmax, Pmax, θPmax, dPmax, IMEP, CA50 and CD, respectively.

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  • Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003760
    DOI: 10.1016/j.energy.2022.123473
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    6. Zandie, Mohammad & Ng, Hoon Kiat & Gan, Suyin & Muhamad Said, Mohd Farid & Cheng, Xinwei, 2023. "Multi-input multi-output machine learning predictive model for engine performance and stability, emissions, combustion and ignition characteristics of diesel-biodiesel-gasoline blends," Energy, Elsevier, vol. 262(PA).
    7. Li, Ji & Zhou, Quan & He, Xu & Chen, Wan & Xu, Hongming, 2023. "Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives," Energy, Elsevier, vol. 272(C).
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    10. Cao, Jiale & Li, Tie & Huang, Shuai & Chen, Run & Li, Shiyan & Kuang, Min & Yang, Rundai & Huang, Yating, 2023. "Co-optimization of miller degree and geometric compression ratio of a large-bore natural gas generator engine with novel Knock models and machine learning," Applied Energy, Elsevier, vol. 352(C).
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