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<p>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&#xa0;°C), AlCl<sub>3</sub> catalyst dosage (0.1–3&#xa0;wt%), and reaction time (60–180&#xa0;min) on resin yield, molecular weight, softening point, and color. The developed empirical models demonstrated exceptional predictive capability, with coefficients of determination (R<sup>2</sup>) exceeding 0.94 for all responses. Optimization results revealed that maximum yield (22.5%) and softening point (152&#xa0;°C) with minimum color (Gardner 3.7) were achieved at 20&#xa0;°C with 1.13 wt% catalyst over 86&#xa0;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.</p>

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Experimental petroleum resin production and optimization using response surface modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月28日

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  • Mohamad-Taghi Rostami,
  • Hamidreza Shahverdi,
  • Vahid Javanbakht,
  • Alireza Najafi Chermahini,
  • Afsaneh Fakhar

摘要

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.