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Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier
Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J.; Kwapinski, Witold
This article corresponds to chapter 6 of Ph.D: Experimental and mathematical modelling of biowaste gasification in a bubbling fluidised bed reactor Pandey, Daya Shankar URI: http://hdl.handle.net/10344/7116 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.
Keyword(s): municipal solid waste; genetic programming; gasification; fluidized bed gasifier
Publication Date:
2015
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: University of Limerick
Citation(s): info:eu-repo/grantAgreement/EC/FP7/289887
Bioresource Technology;179, pp. 524-533
http://dx.doi.org/10.1016/j.biortech.2014.12.048
289887
Publisher(s): Elsevier
First Indexed: 2018-09-01 06:26:36 Last Updated: 2018-09-02 06:26:35