Artificial Neural Network Modeling of the Water Absorption Behavior of Plantain Peel and Bamboo Fibers Reinforced Polystyrene Composites

The research described here aimed to model the water absorption behavior of reinforced polystyrene (PS) composites developed from powders of plantain peel (PPC) and bamboo fiber (BFC) using the Artificial Neural Network (ANN) model. The composites were developed by manual mixing and hand layup at room temperature (25 +/- 2 degrees C) and cured by open molding at room temperature for 7 days. Water absorption tests were performed according to the ASTM standard method (D1037-99, ASTM, 1999). The water absorption was observed to increase with both filler loading and immersion time for both PPC and BFC. The coefficient of determination (R (2)) values >0.98 were achieved for training, validation, and testing for both composite types. The model results showed low root mean squared error values (<1 wt%), revealing that in the utilization of the model a high accuracy threshold was expected for the ANN predictions. Parity plots revealed that the models gave a good balance between over-predictions and under-predictions and the accuracy could be substantiated both at low and high water absorption prediction values. ANOVA revealed that the results were statistically significant at a significance level of p < 0.05.

Automated summary

The study aimed to model the water absorption behavior of reinforced polystyrene composites developed from plantain peel powder and bamboo fiber using an Artificial Neural Network model. The results indicate high accuracy in the model predictions, with water absorption increasing with filler loading and immersion time, showing statistical significance.