Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.
Copyright © 2014 Elsevier Ltd. All rights reserved.
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.
The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework ProgrammeFP7/2007-2013/under REA grant agreement n° .
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.
Vol. 179, pp. 524 - 533
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