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

Daya Shankar Pandey, Indranil Pan, Saptarshi Das, James J. Leahy, Witold Kwapinski

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)524-533
Number of pages10
JournalBioresource Technology
Volume179
Early online date19 Dec 2014
DOIs
Publication statusPublished - 1 Mar 2015
Externally publishedYes

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