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,

• author = "Abiola John Adeyi and Oladayo Adeyi and Emmanuel Olusola Oke and Olusegun Abayomi Olalere and Seun Oyelami and Akinola David Ogunsola",
• title = "Effect of varied fiber alkali treatments on the tensile strength of Ampelocissus cavicaulis reinforced polyester composites: Prediction, optimization, uncertainty and sensitivity analysis",
• journal = "Advanced Industrial and Engineering Polymer Research",
• volume = "4",
• number = "1",
• pages = "29--40",
• year = "2021",
• ISSN = "2542-5048",
• DOI = "doi:10.1016/j.aiepr.2020.12.002",
• URL = "https://www.sciencedirect.com/science/article/pii/S2542504820300580",
• keywords = "genetic algorithms, genetic programming, Response surface methodology, Multigene genetic programming, Oracle crystal ball, Uncertainty and sensitivity analysis",
• 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

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