Experimental Investigation of the Absorption Behavior of Date Palm Fiber Reinforced Iso-Polyester Composites: Artificial Neuron Network (ANN) Modeling

The present article attempts to study absorption properties of bio-composites reinforced with date palm fibers. The effect of fiber loading on water absorption at room temperature 25 degrees C was investigated. The weight gain was measured of bio-composites immersed in distilled water, seawater and rainwater, for more than 670 hours, until reaching the saturation with a measurement interval between 24 and 48 hours. To understand absorption phenomenon, scanning electron microscopy was used. Porosity rate was determined using image J software. It was noted the water absorption rate of the bio composites reached 16.20%, 16.33%, 21.94%, 41.99% for seawater, 16.41%, 16.52%, 20.84%, 30.08% for distilled water, and 14.00%, 14.04%, 19.30%, 36.94% for rainwater, respectively. The absorption increases when increasing fiber content. The diffusion coefficient of bio-composites has minimum and maximum values of about 1.94 x 10(-6)mm(2)/s and 3.99 x 10(-6)mm(2)/s, respectively. Palm fibers are highly porous. The porosity value was higher than 51%. To predict the absorption rate, artificial neural network method was used. The ANN models obtained are very well correlated with the experimental data where the values of the correlation coefficient of the datasets are all beyond 0.99 and the average error value was estimated at 3 x 10(-5)

Water Absorption Behavior of Date Palm Fruit Branches Fiber (DPF) Composites: Experimental and Statistical Analyses

The environmental effects on the response of Date Palm Fiber (DPF) composites are explored through an in-depth analysis of water absorption behavior. This study comprehensively investigates the influence of fiber content, immersion time, and water type on mass gain. Rigorous experimentation and advanced statistical analyses quantify the percent contribution of each factor, emphasizing the dominant role of fiber content at 96.61%. While immersion time and water type contribute relatively smaller percentages (2.11% and 1.28%, respectively), their insights are invaluable for tailored composite design. The study extends to interaction effects, showcasing the combined influence of factors. Regression models, progressing from linear and reciprocal formulations to comprehensive global models, are developed. Meticulous examination of prediction accuracy, using diverse statistical metrics, highlights the superior performance of the global reciprocal model across different water types. This work provides essential insights for optimizing DPF composite design, fabrication, and application, empowering engineers and researchers to make informed decisions in industries demanding tailored water absorption behavior