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

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@Article{Roy:2015:AE,

• author = "Sumit Roy and Ashmita Ghosh and Ajoy Kumar Das and Rahul Banerjee",
• title = "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}",
• journal = "Applied Energy",
• volume = "140",
• pages = "52--64",
• year = "2015",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming, Artificial Neural Network, CRDI, EGR, Engine performance, Exhaust emissions",
• ISSN = "0306-2619",
• DOI = "doi:10.1016/j.apenergy.2014.11.065",
• URL = "http://www.sciencedirect.com/science/article/pii/S0306261914012343",
• abstract = "Gene Expression Programming was employed to express the relationship between the inputs and the outputs of a single cylinder four-stroke CRDI engine coupled with EGR. The performance and emission parameters (BSFC, BTE, CO2, NOx and PM) have been modelled by Gene Expression Programming where load, fuel injection pressure, EGR and fuel injected per cycle were chosen as input parameters. From the results it was found that the GEP can consistently emulate actual engine performance and emission characteristics proficiently even under different modes of CRDI operation with EGR with significant accuracy. Moreover, the GEP obtained results were also compared with an ANN model, developed on the same parametric ranges. The comparison of the obtained results showed that the GEP model outperforms the ANN model in predicting the desired response variables.",

}

Genetic Programming entries for Sumit Roy Ashmita Ghosh Ajoy Kumar Das Shri Rahul Banerjee

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