Strain-rate-dependent optimization of alkali treatment in sugarcane bagasse fibers using ensemble learning and genetic algorithms

• Bagasse fiber diameter follows a log-normal distribution and varies widely (128.26–1255.1 μm). • Machine learning effectively predicts alkali treatment outcomes (R² = 86.3%). • Strain rate was the most influential factor (62.37%), followed by NaOH concentration (24.72%) and soaking time (12.90%). • Genetic algorithm optimizes strain-rate-specific alkali treatments for improved performance. • Optimal parameters maximizing breaking load were: 3% NaOH/4 h at 3.3 × 10⁻⁴ s⁻¹, 1%/8 h at 1.0 × 10⁻³ s⁻¹, and 2%/1 h at 1.67 × 10⁻³ s⁻¹. Natural fibers, particularly sugarcane bagasse, have gained increasing attention as sustainable reinforcements in polymer composites due to their environmental and economic benefits. However, the mechanical properties of bagasse fiber-reinforced composites is highly sensitive to alkali treatment parameters (NaOH concentration and soaking time) and tensile testing conditions (strain rate). This study investigates the effects of NaOH concentration and soaking time on bagasse fiber tensile strength. A full factorial experimental design involving 48 runs was conducted, varying NaOH concentrations (1%, 3%, 5%, 10%) and soaking durations (2, 4, 6, 10 h) to cover low to high treatment severities based on literature, capturing both main effects and interactions (16 unique combinations). Three specimens per combination (48 tensile tests) were evaluated. Strain rate sensitivity was assessed independently at three levels (3.3 × 10⁻⁴ s⁻¹, 1.0 × 10⁻³ s⁻¹, 1.67 × 10⁻³ s⁻¹). FTIR spectroscopy confirmed partial removal of hemicellulose and lignin, enhancing cellulose accessibility. A blending ensemble model combining Random Forest, XGBoost, and regression models achieved high predictive accuracy (R² = 86.3%, RMSE = 25.7). Feature importance analysis revealed that strain rate (62.37%) had the greatest influence, followed by NaOH concentration (24.72%) and soaking time (12.90%). Genetic algorithm-based optimization identified optimal treatment conditions at each strain rate.The proposed data-driven framework enables the efficient optimization of alkali treatment parameters of bagasse fibers, contributing to the development of high-performance, eco-friendly fiber-reinforced composites.