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

ABSTRACT

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°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 × 10⁻⁶mm²/s and 3.99 × 10⁻⁶mm²/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 × 10⁻⁵.

摘要

本文试图研究椰枣纤维增强生物复合材料的吸附性能. 研究了室温25°C下纤维负载对吸水率的影响. 将生物复合材料浸泡在蒸馏水、海水和雨水中670小时以上，直到达到饱和，测量间隔为24至48小时. 为了了解吸收现象，使用了扫描电子显微镜. 利用image J软件测定孔隙率. 生物复合材料的吸水率分别为海水16.20%、16.33%、21.94%、41.99%，蒸馏水16.41%、16.52%、20.84%、30.08%，雨水14.00%、14.04%、19.30%、36.94%. 随着纤维含量的增加，吸收率增加. 生物复合材料的扩散系数最小值约为1.94 × 10-6 mm2/s，最大值约为3.99 × 10-6mmm2/s. 棕榈纤维具有高度的多孔性. 孔隙度值高于51%. 为了预测吸收率，采用了ANN方法. 获得的ANN模型与实验数据有很好的相关性，其中数据集的相关系数均超过0.99，平均误差估计为3 × 10-5.

KEYWORDS:

• Bio composite
• water absorption
• diffusion
• ANN; date palm fiber
• 生物复合材料

关键词:

• 生物复合材料
• 吸水率
• 扩散
• 椰枣纤维
• 多孔性

Highlights

• Test the feasibility of using the ANN model with three parameters.

• Porosity rate was determined using image J software.

• Predict the absorption rate, artificial neural network method was used.

• Test the feasibility of using the ANN model with three parameters.

Acknowledgements

This research is supported by PRFU Project-N° A11N01UN280120220001 organized by the Algerian Ministry of Higher Education and Scientific Research (MESRS). The authors would like to thank Mr. Belkacem Aoufi Assistant Engineer University of M’sila, Faculty of Technology. LGP2 is part of the LabEx Tec 21 (Investissements d’Avenir - grant agreement n°ANR-11-LABX-0030) and of the PolyNat Carnot Institut (Investissements d’Avenir - grant agreement n°ANR-11-CARN-030-01).

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All authors contribute and participate in the work carried out in this paper.

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The authors of this paper agree to publish this work in the Journal of natural fibers.

Additional information

Funding

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.