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<p>Eco-friendly dyes provide a sustainable alternative by reducing environmental pollution, enhancing biodegradability, and ensuring safer textile processing. This study optimized the dyeing process for wool, a renewable and biodegradable fiber, and nylon, a durable synthetic fiber, using <i>Prangos ferulacea</i> (L.) Lindl., an environmentally friendly natural dye. Response surface methodology (RSM) was employed to design experiments based on dye concentration, time, pH, and temperature, while artificial neural networks were used for modeling, demonstrating superior predictive accuracy over RSM (minimum R<sup>2</sup> = 0.96). Sensitivity analysis identified pH as the most influential factor for wool (50% contribution) and dyeing duration for nylon (46% contribution). A genetic algorithm optimized the dyeing conditions, yielding ideal parameters for wool (80.8 wt.% dye concentration, 110.3&#xa0;min, pH 6.1, and 80.6&#xa0;°C) and nylon (68.6 wt.% dye concentration, 102.5&#xa0;min, pH 5.0, and 95.0&#xa0;°C). Model validation confirmed a complex nonlinear relationship between dyeing parameters and the K/S value, while the incorporation of natural mordants significantly influenced color strength, modifying the absorption spectrum and colorimetric properties. The integration of artificial intelligence techniques provides deeper insights into dyeing mechanisms, advancing optimized, eco-friendly, and efficient textile dyeing methodologies.</p>

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Optimizing the eco-friendly dyeing of wool and nylon fabrics with Prangos ferulacea (L.) Lindl using artificial intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月17日

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  • Shiva Shokrzadeh,
  • Milad Razbin,
  • Fatemeh Shahmoradi Ghaheh,
  • Majid Tehrani,
  • Mehdi Sadrjahani

摘要

Eco-friendly dyes provide a sustainable alternative by reducing environmental pollution, enhancing biodegradability, and ensuring safer textile processing. This study optimized the dyeing process for wool, a renewable and biodegradable fiber, and nylon, a durable synthetic fiber, using Prangos ferulacea (L.) Lindl., an environmentally friendly natural dye. Response surface methodology (RSM) was employed to design experiments based on dye concentration, time, pH, and temperature, while artificial neural networks were used for modeling, demonstrating superior predictive accuracy over RSM (minimum R2 = 0.96). Sensitivity analysis identified pH as the most influential factor for wool (50% contribution) and dyeing duration for nylon (46% contribution). A genetic algorithm optimized the dyeing conditions, yielding ideal parameters for wool (80.8 wt.% dye concentration, 110.3 min, pH 6.1, and 80.6 °C) and nylon (68.6 wt.% dye concentration, 102.5 min, pH 5.0, and 95.0 °C). Model validation confirmed a complex nonlinear relationship between dyeing parameters and the K/S value, while the incorporation of natural mordants significantly influenced color strength, modifying the absorption spectrum and colorimetric properties. The integration of artificial intelligence techniques provides deeper insights into dyeing mechanisms, advancing optimized, eco-friendly, and efficient textile dyeing methodologies.