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

Pan, Indranil and Pandey, Daya S. (2016) Incorporating uncertainty in data driven regression models of fluidized bed gasification: A Bayesian approach. Fuel Processing Technology, 142. pp. 305-314. ISSN 0378-3820

[img]
Preview
Text
Accepted Version
Available under the following license: Creative Commons Attribution Non-commercial No Derivatives.

Download (968kB) | Preview
Official URL: http://dx.doi.org/10.1016/j.fuproc.2015.10.027

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.

Item Type: Journal Article
Keywords: Municipal solid waste, Bayesian statistics, Gaussian processes, Gasification, Fluidized bed gasifier
Faculty: ARCHIVED Faculty of Science & Technology (until September 2018)
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 02 Apr 2019 10:07
Last Modified: 09 Sep 2021 19:00
URI: https://arro.anglia.ac.uk/id/eprint/704226

Actions (login required)

Edit Item Edit Item