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

In this investigation, the examination revolves around the characterization of diatomite-enhanced modified safflower oil (MSO)-derived polyester biocomposites. The primary objective is to explore the feasibility of these biocomposites as a substitute for petrochemical-based unsaturated polyester (UP) materials, with the overarching goal of enhancing their economic sustainability. Experimental data analysis employed Response Surface Methodology (RSM) and Artificial Neural Network (ANN), uncovering the optimal composition for the polyester biocomposite to be 6.7 wt.% MSO and 4.5 wt.% diatomite. During the RSM analysis, it was noted that the response parameters exhibited quadratic p-values, specifically, for density ( p < .0001), thermal conductivity ( p < .0001), and Shore D hardness ( p < .0003). However, higher ratios of MSO lead to decreased hardness and increased curing time. SEM images reveal a detrimental impact on the surface morphology of the polyester biocomposite when the diatomite content reaches 8 wt.%. Additionally, Fourier Transform Infrared Spectroscopy (FTIR) and Thermogravimetric Analysis (TGA) offer valuable insights into the chemical bond structure and thermal behavior of the biocomposite, respectively. The Cure Index (CI) value for the diatomite-enhanced composite was determined to be 0.925, indicating a favorable contribution to the polyester curing process. The study finds that diatomite contributes to a linear change in the thermal conductivity coefficient, making the biocomposite suitable for use in the insulation industry. Overall, the study suggests that diatomite reinforced MSO-based polyester biocomposites have the potential as an alternative to petrochemical unsaturated polyester.