Linear Genetic Programming for Prediction of Nickel Recovery from Spent Nickel Catalyst
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Ossman:2010:AJEAS,
-
author = "Mona E. Ossman and Walaa Sheta and Y. Eltaweel",
-
title = "Linear Genetic Programming for Prediction of Nickel
Recovery from Spent Nickel Catalyst",
-
journal = "American Journal of Engineering and Applied Sciences",
-
year = "2010",
-
volume = "3",
-
number = "2",
-
pages = "482--488",
-
keywords = "genetic algorithms, genetic programming",
-
URL = "http://thescipub.com/pdf/10.3844/ajeassp.2010.482.488",
-
DOI = "doi:10.3844/ajeassp.2010.482.488",
-
size = "7 pages",
-
ISSN = "19417020",
-
bibsource = "OAI-PMH server at www.doaj.org",
-
language = "eng",
-
oai = "oai:doaj-articles:ceb69159d4d8f9ef5c936c95f426a554",
-
abstract = "Problem statement: In this study Linear Genetic
Programming (LGP) and statistical regression are used
in predicting Current Efficiency (CE) of Electro
deposition cell used for recovery of nickel from spent
nickel catalyst. Approach: The Nickel electro
deposition from spent catalyst leachate solutions was
studied to determine the effect of the operative
conditions such as nickel concentration, temperature,
current density and time on the CE of the unit cell.
Results: For this purpose, LGP and regression models
were calibrated with training sets and validated by
testing sets. Additionally, the robustness of the
proposed LGP and regression models were evaluated by
experimental data, which are used neither in training
nor at testing stage. The results showed that both
techniques predicted the CE data in quite good
agreement with the observed ones and the predictions of
LGP are challenging. Conclusion/Recommendations: The
performance of LGP, which was moderately better than
statistical regression, is very promising and hence
supports the use of LGP in simulating the electro
deposition of Nickel from spent Nickel catalyst.",
- }
Genetic Programming entries for
Mona E Ossman
Walaa Sheta
Y Eltaweel
Citations