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,
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author = "Mortaza Aghbashlo and Shahaboddin Shamshirband and
Meisam Tabatabaei and Por Lip Yee and
Yaser Nabavi Larimi",
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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",
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journal = "Energy",
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volume = "94",
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pages = "443--456",
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year = "2016",
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ISSN = "0360-5442",
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DOI = "doi:10.1016/j.energy.2015.11.008",
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URL = "http://www.sciencedirect.com/science/article/pii/S0360544215015327",
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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.",
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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