Identification of the Tennessee Eastman Chemical Process Reactor Using Genetic Programming
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- @Article{Faris2013b,
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author = "Hossam Faris and Alaa Sheta",
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title = "Identification of the Tennessee Eastman Chemical
Process Reactor Using Genetic Programming",
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journal = "International Journal of Advanced Science and
Technology",
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year = "2013",
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volume = "50",
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pages = "121--140",
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month = jan,
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keywords = "genetic algorithms, genetic programming, Tennessee
Eastman chemical process, Artificial Neural Networks
(ANN), fuzzy Logic (FL) and Neuro-Gas and Neuro-PSO",
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ISSN = "2005-4238",
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broken = "http://www.sersc.org/journals/IJAST/vol50.php",
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URL = "http://www.sersc.org/journals/IJAST/vol50/11.pdf",
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size = "18 pages",
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abstract = "The Tennessee Eastman chemical process is a
well-defined simulation of a chemical process that has
been commonly used in process control research. As
chemical process plants are getting more complex, the
pressure on chemical engineers to develop accurate
models for monitoring and control purposes is
increased. In this paper, we explore the idea of using
Genetic Programming (GP) technique to model the
Tennessee Eastman (TE) Chemical Process Reactor. The
process is decomposed to four subsystems. They are
reactor level, reactor pressure, reactor cooling water
temperature, and reactor temperature subsystems. GP
found to have many advantages over other techniques in
developing an automated process for industrial system
modelling. A comparison between the applications of GP
in modeling the TE chemical reactors subsystems with
respect to other soft computing techniques such as
Artificial Neural Networks (ANN), fuzzy Logic (FL) and
Neuro-Gas and Neuro-PSO is provided.",
- }
Genetic Programming entries for
Hossam Faris
Alaa Sheta
Citations