Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence
Abstract The dyeing process of textile materials is inherently intricate, influenced by a myriad of factors, including dye concentration, dyeing time, pH level, temperature, type of dye, fiber composition, mechanical agitation, salt concentration, mordants, fixatives, water quality, dyeing method, a...
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Nature Portfolio
2024-07-01
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| Online Access: | https://doi.org/10.1038/s41598-024-64761-7 |
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| author | Fatemeh Shahmoradi Ghaheh Milad Razbin Majid Tehrani Leila Zolfipour Aghdam Vayghan Mehdi Sadrjahani |
| author_facet | Fatemeh Shahmoradi Ghaheh Milad Razbin Majid Tehrani Leila Zolfipour Aghdam Vayghan Mehdi Sadrjahani |
| author_sort | Fatemeh Shahmoradi Ghaheh |
| collection | DOAJ |
| description | Abstract The dyeing process of textile materials is inherently intricate, influenced by a myriad of factors, including dye concentration, dyeing time, pH level, temperature, type of dye, fiber composition, mechanical agitation, salt concentration, mordants, fixatives, water quality, dyeing method, and pre-treatment processes. The intricacy of achieving optimal settings during dyeing poses a significant challenge. In response, this study introduces a novel algorithmic approach that integrates response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA) techniques for the precise fine-tuning of concentration, time, pH, and temperature. The primary focus is on quantifying color strength, represented as K/S, as the response variable in the dyeing process of polyamide 6 and woolen fabric, utilizing plum-tree leaves as a sustainable dye source. Results indicate that ANN (R2 ~ 1) performs much better than RSM (R2 > 0.92). The optimization results, employing ANN-GA integration, indicate that a concentration of 100 wt.%, time of 86.06 min, pH level of 8.28, and a temperature of 100 °C yield a K/S value of 10.21 for polyamide 6 fabric. Similarly, a concentration of 55.85 wt.%, time of 120 min, pH level of 5, and temperature of 100 °C yield a K/S value of 7.65 for woolen fabric. This proposed methodology not only paves the way for sustainable textile dyeing but also facilitates the optimization of diverse dyeing processes for textile materials. |
| format | Article |
| id | doaj-art-29068bdfc88f4c53b8deef750f3e346b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-29068bdfc88f4c53b8deef750f3e346b2025-02-02T12:25:04ZengNature PortfolioScientific Reports2045-23222024-07-0114111710.1038/s41598-024-64761-7Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligenceFatemeh Shahmoradi Ghaheh0Milad Razbin1Majid Tehrani2Leila Zolfipour Aghdam Vayghan3Mehdi Sadrjahani4Department of Textile Engineering, Faculty of Environmental Science, Urmia University of TechnologySchool of Engineering, Macquarie UniversityDepartment of Art, Shahrekord UniversityDepartment of Textile Engineering, Faculty of Environmental Science, Urmia University of TechnologyDepartment of Textile Engineering, Faculty of Environmental Science, Urmia University of TechnologyAbstract The dyeing process of textile materials is inherently intricate, influenced by a myriad of factors, including dye concentration, dyeing time, pH level, temperature, type of dye, fiber composition, mechanical agitation, salt concentration, mordants, fixatives, water quality, dyeing method, and pre-treatment processes. The intricacy of achieving optimal settings during dyeing poses a significant challenge. In response, this study introduces a novel algorithmic approach that integrates response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA) techniques for the precise fine-tuning of concentration, time, pH, and temperature. The primary focus is on quantifying color strength, represented as K/S, as the response variable in the dyeing process of polyamide 6 and woolen fabric, utilizing plum-tree leaves as a sustainable dye source. Results indicate that ANN (R2 ~ 1) performs much better than RSM (R2 > 0.92). The optimization results, employing ANN-GA integration, indicate that a concentration of 100 wt.%, time of 86.06 min, pH level of 8.28, and a temperature of 100 °C yield a K/S value of 10.21 for polyamide 6 fabric. Similarly, a concentration of 55.85 wt.%, time of 120 min, pH level of 5, and temperature of 100 °C yield a K/S value of 7.65 for woolen fabric. This proposed methodology not only paves the way for sustainable textile dyeing but also facilitates the optimization of diverse dyeing processes for textile materials.https://doi.org/10.1038/s41598-024-64761-7Dyeing processPlum-tree leavesArtificial neural networkResponse surface methodologyGenetic algorithm |
| spellingShingle | Fatemeh Shahmoradi Ghaheh Milad Razbin Majid Tehrani Leila Zolfipour Aghdam Vayghan Mehdi Sadrjahani Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence Scientific Reports Dyeing process Plum-tree leaves Artificial neural network Response surface methodology Genetic algorithm |
| title | Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence |
| title_full | Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence |
| title_fullStr | Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence |
| title_full_unstemmed | Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence |
| title_short | Modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum-tree leaves using artificial intelligence |
| title_sort | modeling and optimization of dyeing process of polyamide 6 and woolen fabrics with plum tree leaves using artificial intelligence |
| topic | Dyeing process Plum-tree leaves Artificial neural network Response surface methodology Genetic algorithm |
| url | https://doi.org/10.1038/s41598-024-64761-7 |
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