Effect of varied fiber alkali treatments on the tensile strength of Ampelocissus cavicaulis reinforced polyester composites: Prediction, optimization, uncertainty and sensitivity analysis
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- @Article{ADEYI:2021:AIEPR,
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author = "Abiola John Adeyi and Oladayo Adeyi and
Emmanuel Olusola Oke and Olusegun Abayomi Olalere and
Seun Oyelami and Akinola David Ogunsola",
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title = "Effect of varied fiber alkali treatments on the
tensile strength of Ampelocissus cavicaulis reinforced
polyester composites: Prediction, optimization,
uncertainty and sensitivity analysis",
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journal = "Advanced Industrial and Engineering Polymer Research",
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volume = "4",
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number = "1",
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pages = "29--40",
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year = "2021",
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ISSN = "2542-5048",
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DOI = "doi:10.1016/j.aiepr.2020.12.002",
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URL = "https://www.sciencedirect.com/science/article/pii/S2542504820300580",
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keywords = "genetic algorithms, genetic programming, Response
surface methodology, Multigene genetic programming,
Oracle crystal ball, Uncertainty and sensitivity
analysis",
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abstract = "Studies on modeling and optimization of alkali
treatment, investigation of experimental uncertainty
and sensitivity analysis of alkali treatment factors of
natural fibers are important to effective natural fiber
reinforced polymer composite development. In this
contribution, response surface methodology (RSM) was
employed to investigate and optimize the effect of
varied treatment factors (sodium hydroxide
concentration (NaOH) and soaking time (ST)) of the
alkali treatment of Ampelocissus cavicaulis natural
fiber (ACNF) on the tensile strength (TS) of alkali
treated ACNF reinforced polyester composite. RSM and
multi gene genetic programming (MGGP) were
comparatively employed to model the alkali treatment.
The best model was applied in Oracle Crystal Ball (OCB)
to investigate the uncertainty of the treatment results
and sensitivity of the treatment factors. Results
showed that increased NaOH and ST increased the TS of
the alkali treated ACNF reinforced polyester composite
up to 28.3500 MPa before TS decreased. The coefficient
of determination (R2) and root mean square error (RMSE)
of RSM model were 0.8920 and 0.6528 while that of MGGP
were 0.9144 and 0.5812. The optimum alkali treatment
established by RSM was 6.23percent of NaOH at 41.99 h
of ST to give a TS of 28.1800 MPa with a desirability
of 0.9700. The TS of the validated optimum alkali
treatment condition was 28.2200 MPa. The certainty of
the experimental results was 71.2580percent. TS was
13.8000percent sensitive to NaOH and 86.2000percent
sensitive to ST. This work is useful for effective
polymer composite materials production to reduce the
enormous material and energy losses that usually
accompany the process",
- }
Genetic Programming entries for
Abiola John Adeyi
Oladayo Adeyi
Emmanuel Olusola Oke
Olusegun Abayomi Olalere
Seun Oyelami
Akinola David Ogunsola
Citations