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Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

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journal contribution
posted on 2023-08-30, 16:09 authored by Daya S. Pandey, Indranil Pan, Saptarshi Das, James J. Leahy, Witold Kwapinski
A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well.

History

Refereed

  • Yes

Volume

179

Page range

524-533

Publication title

Bioresource Technology

ISSN

1873-2976

Publisher

Elsevier

File version

  • Accepted version

Language

  • eng

Legacy posted date

2019-04-02

Legacy creation date

2019-03-29

Legacy Faculty/School/Department

ARCHIVED Faculty of Science & Technology (until September 2018)

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