Optimization of thermal conductivity in coir fibre-reinforced PVC composites using advanced computational techniques

This research focuses on enhancing the thermal conductivity of coir fibre-reinforced polyvinyl chloride (PVC) composites using advanced optimization techniques. While coir fibre adds sustainability and biodegradability, it poses challenges in achieving optimal thermal performance when integrated into PVC. To address these challenges, the study uses Response Surface Methodology (RSM) and three nature-inspired optimization methods viz. Particle Swarm Optimization (PSO), Dragonfly Optimization (DFO) and Cuckoo Search Algorithm (CSA) to improve factors like fibre content, particle size and chemical treatment. A Box-Behnken experimental design helps to create composite samples using hydraulic injection moulding and thermal conductivity is measured with a two-slab guarded hot plate device. Among the optimization methods, CSA emerges as the most effective, achieving a maximum thermal conductivity of 0.801 W/mK with minimal error deviation (0.01-5.5%) by the process parameters such as potassium hydroxide treatment, coir content of 2 wt% and powder diameter of 75 (µm). DFO delivers consistent results with slightly higher error rates, while PSO demonstrates rapid convergence but greater variability. The comparison shows that CSA performs better, providing a dependable and long-lasting way to create high-quality coir-reinforced PVC composites that are good for industrial use. This work is among the first to compare multiple bio-inspired optimization algorithms for enhancing the thermal properties of coir-reinforced PVC composites, offering a new pathway for developing high-performance, eco-friendly materials for industrial applications.