Abstract
To research the mechanisms of diffusion and water uptake kinetics biocomposites, various HDPE materials reinforced by varying quantities of Washingtonia filifera (WF) fibres (10 to 30 wt%) were submerged at room temperature in distilled water. To optimise the immersion time and WF fibre content in HDPE/WF biocomposite water uptake, an artificial neural network, the response surface methodology and the genetic algorithm were employed. In this research, the CCD model of RSM was utilised to carry out test design, modelling, and optimisation. It was found that the way water absorbs liquids follows the Fickian diffusion mode. The outcomes demonstrate that the incorporation of WF fibres into the HDPE matrix decreased the diffusivity. The ANOVA determined the relative significance of each variable and demonstrated the model's validity by demonstrating a high correlation between the observed data. Moreover, the results showed that the ANN models have training, test and validation correlation coefficients for water absorption prediction of 0.9955, 0.9915 and 0.9999, respectively. RSM-GA revealed that a 10% fibre content and a one-hour immersion duration produced the lowest levels of absorption. Additionally, a model that is very suitable for predicting biocomposites water uptake and is applicable to a variety of industrial applications is developed.