A heating value estimation of refuse derived fuel using the genetic programming model
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gp-bibliography.bib Revision:1.8081
- @Article{OZKAN:2019:WM,
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author = "Kemal Ozkan and Sahin Isik and Zerrin Gunkaya and
Aysun Ozkan and Mufide Banar",
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title = "A heating value estimation of refuse derived fuel
using the genetic programming model",
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journal = "Waste Management",
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volume = "100",
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pages = "327--335",
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year = "2019",
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ISSN = "0956-053X",
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DOI = "doi:10.1016/j.wasman.2019.09.035",
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URL = "http://www.sciencedirect.com/science/article/pii/S0956053X19306142",
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keywords = "genetic algorithms, genetic programming, Correlation
analysis, Higher heating value, Refused derived fuel",
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abstract = "Refuse Derived Fuel (RDF) makes an increasingly
important contribution to sustainable waste management
as an energy source in cement kilns. The most important
parameter of RDF in an evaluation of its performance as
a fuel is Higher Heating Value (HHV). The two methods
of HHV determination are the direct method and the
indirect method. The direct method requires the use of
a calorimetric bomb and the indirect method requires
ultimate or proximate analysis. As in the direct
method, the ultimate analysis based indirect method
requires the use of specific equipment and a skilled
analyst. Most cement plants do not have special
equipment. From this point of view, this study aims to
predict the HHVs of RDF samples using the results of
proximate analysis. Two Genetic Programming (GP)
Models, namely GP Model #1 and GP Model #2 are used for
the prediction. GP Model #1 denotes a modest nonlinear
mapping function used for the prediction of HHVs,
whereas GP Model #2 is a more inclusive nonlinear
correlation analysis model as an improved version of GP
Model #1. To assess the developed models, the test data
is simulated and statistical results to the estimation
of HHVs are reported as R2 equal to 0.9951 and 0.9988,
Root Mean Square Error (RMSE) equal to 1.4126 and
0.6971 and Average Absolute Error (AAE) equal to 0.0543
and 0.0251, for GP Model #1 and GP Model #2,
respectively. It can be seen that GP Model #2 may be
confidently used for HHV estimation",
- }
Genetic Programming entries for
Kemal Ozkan
Sahin Isik
Zerrin Gunkaya
Aysun Ozkan
Mufide Banar
Citations