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• W4311700818 endingPage "116094" @default.
• W4311700818 startingPage "116094" @default.
• W4311700818 abstract "The calcium alginate (CA) hydrogels of the raw and reinforced with cellulose nanocrystals (CNC) were synthesized using the ion-exchange method. FT-IR, FESEM, EDX, XRD, and TGA were used to characterize the samples. Swelling as a crucial property of hydrogels was investigated. CNCs-reinforced hydrogels (CA/CNC) with a higher swelling ratio were selected for the subsequent tests. The results of the effect of independent variables, including concentrations of sodium alginate, calcium chloride, and CNC on CA/CNC swelling were analyzed using response surface method based on central composite design (RSM-CCD) and compared with the artificial neural network-salp swarm algorithm (ANN-SSA) hybrid. ANN-SSA codes were developed using the Python programming language. The best optimal topology for the ANN was 3:7:1, and the trained ANN-SSA was converted to matrix form to have a predictive equation based on the weights and biases. The validation of the models was performed using the correlation coefficient (R2), root mean square error (RMSE), and normal standard deviation (Δq (%)). The results of R2, RMSE, and Δq (%) for ANN-SSA are 1.000, 2.657, and 0.095, respectively, and for RSM are 0.984, 202.598, and 7.384, respectively, confirming that ANN-SSA was more precise than RSM." @default.
• W4311700818 created "2022-12-28" @default.
• W4311700818 creator A5002790324 @default.
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• W4311700818 date "2023-02-01" @default.
• W4311700818 modified "2023-09-30" @default.
• W4311700818 title "Swelling prediction of calcium alginate/cellulose nanocrystal hydrogels using response surface methodology and artificial neural network" @default.
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• W4311700818 doi "https://doi.org/10.1016/j.indcrop.2022.116094" @default.
• W4311700818 hasPublicationYear "2023" @default.
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