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

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@Article{Pandey:2015:BT,

• author = "Daya Shankar Pandey and Indranil Pan and Saptarshi Das and James J. Leahy and Witold Kwapinski",
• title = "Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier",
• journal = "Bioresource Technology",
• volume = "179",
• pages = "524--533",
• year = "2015",
• keywords = "genetic algorithms, genetic programming, Municipal solid waste, Gasification, Fluidised bed gasifier",
• ISSN = "0960-8524",
• DOI = "doi:10.1016/j.biortech.2014.12.048",
• URL = "http://www.sciencedirect.com/science/article/pii/S0960852414017933",
• 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 fluidised 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.",

}

Genetic Programming entries for Daya Shankar Pandey Indranil Pan Saptarshi Das James J Leahy Witold Kwapinski

Citations