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Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios

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  • Mehra, Roopesh Kumar
  • Duan, Hao
  • Luo, Sijie
  • Rao, Anas
  • Ma, Fanhua

Abstract

In today's era, the computational capabilities of artificial neural network has endorsed to be a bedrock and inferences in many fields, including internal-combustion engines. The presented research in ANN has been germinated to anticipate the performance and emission characteristics of a turbocharged SI engine fueled with various HCNG mixtures. The experiments were accomplished at various excess air ratios (λ), ignition timings (θi) at MAP of 105 kPa and 140 kPa, while engine speed was kept constant at 1600 rpm to obtain data for testing and training ANN model. The test results show that with the increase in values of λ, MAP, and hydrogen addition, the torque output effectively decreases while BSFC first decreases and after attaining minimum value it further increases. The NOx, CO, THC, and CH4 emissions all declined with the hike of ignition advance angle, and inclined with increase of the load. ANN’s popular backpropagation algorithm is adopted in multilayered feedforward networks. In order to predict the performance and emission characteristics of HCNG engine, the four-input and one-output network structure are used. HCNG0, HCNG20 and HCNG40 blends has been studied in the presented ANN model in which the excess air ratio (λ), engine load, ignition timing, and HCNG blends has been taken as four-input parameters. The bestowed model has been trained using hyperbolic tangent transfer activation function (tansig) and Levenberg-Marquardt learning algorithm (LM) along with numerous neurons.

Suggested Citation

  • Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:736-754
    DOI: 10.1016/j.apenergy.2018.06.085
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