Optimization of safflower oil-based polyester biocomposite reinforced with diatomite: An response surface methodology approach and assessment of artificial neural network findings.

This article investigates the characterization and optimization of diatomite-enhanced modified safflower oil (MSO)-derived polyester biocomposites as sustainable alternatives to petrochemical-based unsaturated polyester (UP) materials. Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) analyses, the study identifies an optimal composition of 6.7 wt.% MSO and 4.5 wt.% diatomite, balancing improved thermophysical properties such as density, Shore D hardness, and thermal conductivity. The addition of diatomite contributes to enhanced thermal stability, reduced density, and a linear increase in thermal conductivity, suggesting potential applications in insulation, while excessive MSO or diatomite content negatively affects hardness, curing time, and composite homogeneity. Overall, the research highlights the promise of MSO and diatomite as bio-based, eco-friendly components for developing cost-effective polyester biocomposites with tailored properties.