実験的石油樹脂製造と応答曲面モデリングによる最適化

This study establishes an integrated experimental and modeling framework for optimizing petroleum resin production via cationic polymerization. Using Response Surface Methodology with a Central Composite Design, we systematically investigated the effects of reaction temperature (20-100 °C), AlCl₃ catalyst dosage (0.1-3 wt%), and reaction time (60-180 min) on resin yield, molecular weight, softening point, and color. The developed empirical models demonstrated exceptional predictive capability, with coefficients of determination (R²) exceeding 0.94 for all responses. Optimization results revealed that maximum yield (22.5%) and softening point (152 °C) with minimum color (Gardner 3.7) were achieved at 20 °C with 1.13 wt% catalyst over 86 min. Experimental validation confirmed the model's accuracy, with average prediction errors below 3%. The hybrid aliphatic-aromatic nature of the synthesized resin was confirmed through comprehensive characterization (FTIR, NMR, DSC), while mechanistic insights into parameter effects provided fundamental understanding of the polymerization behavior. This research provides a robust framework for the multi-objective optimization of petroleum resin production, with direct implications for industrial application.