Genetic programming based high performing correlations for prediction of higher heating value of coals of different ranks and from diverse geographies
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- @Article{Ghugare:2016:JEI,
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author = "Suhas B. Ghugare and Sanjeev S. Tambe",
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title = "Genetic programming based high performing correlations
for prediction of higher heating value of coals of
different ranks and from diverse geographies",
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journal = "Journal of the Energy Institute",
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year = "2017",
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volume = "90",
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number = "3",
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pages = "476--484",
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month = jun,
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keywords = "genetic algorithms, genetic programming, Coal, Higher
heating value, Proximate analysis, Ultimate analysis",
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ISSN = "1743-9671",
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DOI = "doi:10.1016/j.joei.2016.03.002",
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URL = "http://www.sciencedirect.com/science/article/pii/S1743967115304578",
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abstract = "The higher heating value (HHV) is the most important
indicator of a coal's potential energy yield. It is
commonly used in the efficiency and optimal design
calculations pertaining to the coal combustion and
gasification processes. Since the experimental
determination of coal's HHV is tedious and
time-consuming, a number of proximate and/or ultimate
analyses based correlations-which are mostly
linear-have been proposed for its estimation. Owing to
the fact that relationships between some of the
constituents of the proximate/ultimate analyses and the
HHV are nonlinear, the linear models make suboptimal
predictions. Also, a majority of the currently
available HHV models are restricted to the coals of
specific ranks or particular geographical regions.
Accordingly, in this study three proximate and ultimate
analysis based nonlinear correlations have been
developed for the prediction of HHV of coals by using
the computational intelligence (CI) based genetic
programming (GP) formalism. Each of these correlations
possesses following noteworthy characteristics: (i) the
highest HHV prediction accuracy and generalization
capability as compared to the existing models, (ii)
wider applicability for coals of different ranks and
from diverse geographies, and (iii) structurally lower
complex than the other CI-based existing HHV models. It
may also be noted that in this study, the GP technique
has been used for the first time for developing
coal-specific HHV models. Owing to the stated
attractive features, the GP-based models proposed here
possess a significant potential to replace the existing
models for predicting the HHV of coals.",
- }
Genetic Programming entries for
Suhas B Ghugare
Sanjeev S Tambe
Citations