The use of ELM-WT (extreme learning machine with wavelet transform algorithm) to predict exergetic performance of a DI diesel engine running on diesel/biodiesel blends containing polymer waste

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@Article{Aghbashlo:2016:Energy,
  author =       "Mortaza Aghbashlo and Shahaboddin Shamshirband and 
                 Meisam Tabatabaei and Por Lip Yee and 
                 Yaser Nabavi Larimi",
  title =        "The use of ELM-WT (extreme learning machine with
                 wavelet transform algorithm) to predict exergetic
                 performance of a {DI} diesel engine running on
                 diesel/biodiesel blends containing polymer waste",
  journal =      "Energy",
  volume =       "94",
  pages =        "443--456",
  year =         "2016",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2015.11.008",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0360544215015327",
  abstract =     "In this study, a novel method based on Extreme
                 Learning Machine with wavelet transform algorithm
                 (ELM-WT) was designed and adapted to estimate the
                 exergetic performance of a DI diesel engine. The
                 exergetic information was obtained by calculating mass,
                 energy, and exergy balance equations for the
                 experimental trials conducted at various engine speeds
                 and loads as well as different biodiesel and expanded
                 polystyrene contents. Furthermore, estimation
                 capability of the ELM-WT model was compared with that
                 of the ELM, GP (genetic programming) and ANN
                 (artificial neural network) models. The experimental
                 results showed that an improvement in the exergetic
                 performance modelling of the DI diesel engine could be
                 achieved by the ELM-WT approach in comparison with the
                 ELM, GP, and ANN methods. Furthermore, the results
                 showed that the applied algorithm could learn thousands
                 of times faster than the conventional popular learning
                 algorithms. Obviously, the developed ELM-WT model could
                 be used with a high degree of confidence for further
                 work on formulating novel model predictive strategy for
                 investigating exergetic performance of DI diesel
                 engines running on various renewable and non-renewable
                 fuels.",
  keywords =     "genetic algorithms, genetic programming, Biodiesel, DI
                 diesel engine, Exergetic performance parameters,
                 Expanded polystyrene, Cost sensitivity analysis,
                 Extreme learning machine-wavelet (ELM-WT)",
}

Genetic Programming entries for Mortaza Aghbashlo Shahaboddin Shamshirband Meisam Tabatabaei Por Lip Yee Yaser Nabavi Larimi

Citations