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Prediction of the performance and exhaust emissions of a compression ignition engine using a wavelet neural network with a stochastic gradient algorithm

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  • Rahimi molkdaragh, R.
  • Jafarmadar, S.
  • Khalilaria, Sh
  • Soukht Saraee, H.

Abstract

The purpose of this research is to use a wavelet neural network (WNN) and stochastic gradient algorithm (SGA) to predict the performance and exhaust emissions of a compression ignition engine with nanoparticles-diesel fuel. The percentage of the additive of nanoparticles to the fuel ranges between 20 and 80 ppm. A model of WNN has been applied in order to predict the relationship between the power, fuel consumption (FC), specific fuel consumption (SFC), CO, NOx, and HC with the amount of nanoparticles at different speeds. The input variables are of two parameters (the percentage of nanoparticles and engine speed), while the output variables are of six parameters (power, FC, SFC, CO, NOx, and HC). In this work, considering the characteristics of the utilized wavelet function and application of the SGA method, satisfactory results were obtained in prediction of exhaust emissions and performance of the target engine. In addition, two common artificial neural networks (ANNs) (back propagation (BP) and non-linear autoregressive with exogenous input (NARX)) were used in predicting the performance of internal combustion engines compared with WNN results. Therefore, evaluation results of these three networks showed that the WNN with the SGA are very accurate and useful method to perform the prediction and model nonlinear phenomena of internal combustion engines.

Suggested Citation

  • Rahimi molkdaragh, R. & Jafarmadar, S. & Khalilaria, Sh & Soukht Saraee, H., 2018. "Prediction of the performance and exhaust emissions of a compression ignition engine using a wavelet neural network with a stochastic gradient algorithm," Energy, Elsevier, vol. 142(C), pages 1128-1138.
    • Handle: RePEc:eee:energy:v:142:y:2018:i:c:p:1128-1138
    DOI: 10.1016/j.energy.2017.09.006
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    References listed on IDEAS

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    1. Han, Yongming & Geng, Zhiqiang & Zhu, Qunxiong & Qu, Yixin, 2015. "Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry," Energy, Elsevier, vol. 83(C), pages 685-695.
    2. Çay, Yusuf & Korkmaz, Ibrahim & Çiçek, Adem & Kara, Fuat, 2013. "Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network," Energy, Elsevier, vol. 50(C), pages 177-186.
    3. Sen, Asok K. & Wang, Jinhua & Huang, Zuohua, 2011. "Investigating the effect of hydrogen addition on cyclic variability in a natural gas spark ignition engine: Wavelet multiresolution analysis," Applied Energy, Elsevier, vol. 88(12), pages 4860-4866.
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    Cited by:

    1. Dengfeng Zhao & Haiyang Li & Junjian Hou & Pengliang Gong & Yudong Zhong & Wenbin He & Zhijun Fu, 2023. "A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption," Energies, MDPI, vol. 16(14), pages 1-20, July.
    2. Ağbulut, Ümit & Gürel, Ali Etem & Sarıdemir, Suat, 2021. "Experimental investigation and prediction of performance and emission responses of a CI engine fuelled with different metal-oxide based nanoparticles–diesel blends using different machine learning alg," Energy, Elsevier, vol. 215(PA).
    3. Kwak, Sanghyeok & Choi, Jaehong & Lee, Min Chul & Yoon, Youngbin, 2021. "Predicting instability frequency and amplitude using artificial neural network in a partially premixed combustor," Energy, Elsevier, vol. 230(C).
    4. Muhammed A. Hassan & Hindawi Salem & Nadjem Bailek & Ozgur Kisi, 2023. "Random Forest Ensemble-Based Predictions of On-Road Vehicular Emissions and Fuel Consumption in Developing Urban Areas," Sustainability, MDPI, vol. 15(2), pages 1-22, January.

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