Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach
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- @Article{Pan:2016:FPT,
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author = "Indranil Pan and Daya Shankar Pandey",
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title = "Incorporating uncertainty in data driven regression
models of fluidized bed gasification: A {Bayesian}
approach",
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journal = "Fuel Processing Technology",
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volume = "142",
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pages = "305--314",
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year = "2016",
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ISSN = "0378-3820",
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DOI = "doi:10.1016/j.fuproc.2015.10.027",
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URL = "http://www.sciencedirect.com/science/article/pii/S0378382015302149",
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abstract = "In recent years, different non-linear regression
techniques using neural networks and genetic
programming have been applied for data-driven modelling
of fluidized bed gasification processes. However, none
of these methods explicitly take into account the
uncertainty of the measurements and predictions. In
this paper, a Bayesian approach based on Gaussian
processes is used to address this issue. This method is
used to predict the syngas yield production and the
lower heating value (LHV) for municipal solid waste
(MSW) gasification in a fluidized bed gasifier. The
model parameters are calculated using the maximum
a-posteriori (MAP) estimate and compared with the
Markov Chain Monte Carlo (MCMC) method. The simulations
demonstrate that the Bayesian methodology is a powerful
technique for handling the uncertainties in the model
and making probabilistic predictions based on
experimental data. The method is generic in nature and
can be extended to other types of fuels as well.",
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keywords = "genetic algorithms, genetic programming, Municipal
solid waste, Bayesian statistics, Gaussian processes,
Gasification, Fluidized bed gasifier",
- }
Genetic Programming entries for
Indranil Pan
Daya Shankar Pandey
Citations