Statistical Optimization for Maximized Cellulose Yield from Carludovica palmata Fibers: Structural and Thermal Characterization for Sustainable Biopolymer Applications

This study investigates the statistical optimization of cellulose extraction from Carludovica palmata fibers, an underexplored lignocellulosic resource with potential for sustainable polymer applications. Response Surface Methodology (RSM) based on a Central Composite Design (CCD) was applied to optimize acid and alkaline hydrolysis parameters, including reagent concentration, temperature, and reaction time, with the aim of maximizing cellulose yield while minimizing process severity. The multivariate approach enabled the identification of nonlinear effects and optimal operational windows that cannot be resolved using conventional single‐variable methods. Under optimized conditions, cellulose yields of 42.7% for the acid stage and 57.7% for the alkaline stage were obtained, and the statistical models showed good predictive reliability. Structural and thermal characterization confirmed that optimization influenced polymer‐relevant properties: Fourier transform infrared spectroscopy evidenced effective removal of hemicellulose and lignin, X‐ray diffraction revealed an increase in crystallinity from 41% in untreated fibers to 64% after alkaline treatment, and thermogravimetric analysis showed enhanced thermal stability, with the main degradation temperature increasing from 328 °C to 352 °C. These results demonstrate that statistical optimization is an effective strategy to improve both yield and physicochemical properties of cellulose, supporting the valorization of C. palmata fibers for biopolymer‐based materials.