Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach

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@Article{Pan:2016:FPT,

• author = "Indranil Pan and Daya Shankar Pandey",
• title = "Incorporating uncertainty in data driven regression models of fluidized bed gasification: A {Bayesian} approach",
• journal = "Fuel Processing Technology",
• volume = "142",
• pages = "305--314",
• year = "2016",
• ISSN = "0378-3820",
• DOI = "doi:10.1016/j.fuproc.2015.10.027",
• URL = "http://www.sciencedirect.com/science/article/pii/S0378382015302149",
• 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.",
• 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

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