Model development and surface analysis of a bio-chemical process
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Jiang:2016:CILS,
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author = "Dazhi Jiang and Wan-Huan Zhou and Ankit Garg and
Akhil Garg",
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title = "Model development and surface analysis of a
bio-chemical process",
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journal = "Chemometrics and Intelligent Laboratory Systems",
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volume = "157",
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pages = "133--139",
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year = "2016",
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ISSN = "0169-7439",
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DOI = "doi:10.1016/j.chemolab.2016.07.010",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169743916301721",
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abstract = "Phytoremediation, is a promising biochemical process
which has gained wide acceptance in remediating the
contaminants from the soil. Phytoremediation process
comprises of biochemical mechanisms such as adsorption,
transport, accumulation and translocation.
State-of-the-art modelling methods used for studying
this process in soil are limited to the traditional
ones. These methods rely on the assumptions of the
model structure and induce ambiguity in its predictive
ability. In this context, the Artificial Intelligence
approach of Genetic programming (GP) can be applied.
However, its performance depends heavily on the
architect (objective functions, parameter settings and
complexity measures) chosen. Therefore, this present
work proposes a comprehensive study comprising of the
experimental and numerical one. Firstly, the lead
removal efficiency (percent) from the phytoremediation
process based on the number of planted spinach,
sampling time, root and shoot accumulation of the soil
is measured. The numerical modelling procedure
comprising of the two architects of GP investigates the
role of the two objective functions (SRM and AIC)
having two complexity measures: number of nodes and
order of polynomial in modelling this process. The
performance comparison analysis of the proposed models
is conducted based on the three error metrics (RMSE,
MAPE and R) and cross-validation. The findings reported
that the models formed from GP architect using SRM
objective function and order of polynomial as
complexity measure performs better with lower size and
higher generalization ability than those of AIC based
GP models. 2-D and 3-D surface analysis on the selected
GP architect suggests that the shoot accumulation
influences (non-linearly) the lead removal efficiency
the most followed by the number of planted spinach, the
root accumulation and the sampling time. The present
work will be useful for the experts to accurately
determine lead removal efficiency based on the explicit
GP model, thus saving the waste of input resources.",
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keywords = "genetic algorithms, genetic programming,
Phytoremediation, Lead removal, Statistical modelling,
Biochemical, Cross-validation",
- }
Genetic Programming entries for
Dazhi Jiang
Wan-Huan (Hanna) Zhou
Ankit Garg
Akhil Garg
Citations