Diatomite reinforced modified safflower oil-based epoxy biocomposite production: Optimization with RSM and assessment of outcomes by ANN

In this study, a biocomposite based on modified safflower oil (MSO) has been created and its structure has been reinforced with diatomite. The weight of the petrochemical raw material utilized is decreased with the usage of MSO, and diatomite is employed to create a novel, environmentally friendly biocomposite. Response surface methodology (RSM) is used to optimize experimental research while taking the resulting biocomposite's thermophysical properties into consideration. The chemical bond structure of the biocomposite is examined using Fourier transform infrared (FTIR) spectroscopy. For thermal decomposition behavior, thermogravimetric analysis (TGA) is performed, and scanning electron microscopy (SEM) is used for surface morphology. Additionally, research has been done on the diatomite-reinforced MSO-based biocomposite's Shore D hardness, density, and thermal conductivity coefficient. It has been found that MSO interacts well with diatomite to improve some of the biocomposite's features and to help it create new ones. The employment of an artificial neural network (ANN) and RSM has been shown to facilitate the effective and efficient execution of experimental research and the more accurate evaluation of results. According to RSM, biocomposite production with 65 wt% epoxy A, 34 wt% epoxy B, 8 wt% MSO, and 5 wt% diatomite is optimum. [Display omitted]