Modelling and Optimization of Corncob-Reinforced Polymer Composite using Response Surface Methodology

Abstract

This study focuses on the development and optimization of corncob-reinforced polymer composites as a sustainable substitute for conventional synthetic materials. Growing environmental concerns and the need to utilize agricultural residues inspired the use of corncob, a lignocellulosic by-product, as a reinforcing filler in an unsaturated polyester matrix. The corncob was processed and characterized for its chemical composition before being incorporated into the polymer at varying fibre loadings and lengths. Response Surface Methodology (RSM) based on a Box–Behnken Design (BBD) was applied to analyze and optimize the influence of fibre loading and fibre length on key mechanical properties, including tensile strength, flexural strength, and impact energy. The results indicated that both variables significantly affected composite performance, with notable interaction and quadratic effects. Analysis of variance (ANOVA) confirmed the validity of the developed quadratic models, with high coefficients of determination (R² > 0.96), demonstrating strong agreement between predicted and experimental results. Optimization revealed that the best mechanical properties—tensile strength (11.74 MPa), flexural strength (20.90 MPa), and impact energy (0.815 J)—were obtained at a fibre loading of 23.54% and fibre length of 1.04 mm. BET analysis showed a high surface area of 266.971 m²/g with a strong correlation coefficient (r = 0.99297), indicating considerable porosity. SEM results confirmed uniform dispersion with localized pores, while EDX analysis revealed dominant silicon content alongside carbon and oxygen. FTIR spectra identified key functional groups, confirming the presence of silicate, carbonyl, alkyl, and hydroxyl functionalities. Overall, the results highlight the potential of corncob as an effective reinforcement material, promoting sustainable material development and efficient agricultural waste utilization.