Investigation of corrosion and water absorption of biomass natural coir fiber/hBN reinforced epoxy hybrid composites using different optimisation approaches.

Agricultural waste or agro-waste, including natural fibers and particles from various crop parts, is increasingly recognized as a significant contributor to environmental issues. However, from a circular economy perspective, these materials present an opportunity to be repurposed into new, eco-friendly products. The present study, specifically focuses on understanding the effect of different factors, such as the particulate loading and the size (coir and hBN - 1 to 5 wt%; Coir Powder size (100-200 μm) of the particles on composite's corrosion rates and water absorption properties. These hybrid particulate composites (HPC) are fabricated using the hand layup process. The study uses a Box-Behnken Design (BBD-L15), a statistical experimental design tool that facilitates the effective investigation of many input parameters and their interactions, to comprehensively investigate these impacts. In addition, the study utilizes four metaheuristic algorithms-the Dragonfly Algorithm (DFO), the Salp Swarm Algorithm (SSA), Teaching Learning Optimization (TLO) and Particle Swarm Optimization (PSO)-alongside regression equations to predict the optimal characteristics of the composite material. To determine the best-performing algorithm, a comparison is made using Deng's method. The findings indicate that the composite with a higher weight% of hBN particulates exhibits reduced water absorption and corrosion rates. A larger Deng's Value often indicates better performance. Based on its higher Deng's Value, the SSO algorithm outperforms other algorithms in minimizing both corrosion resistance (CR) and water absorption (WA). The Deng's Value for SSO reached a maximum of 0.68, while the other algorithms show comparable but lower performance.