使用人工智能对聚胺6和带有梅叶的羊毛织物染色过程的建模和优化
Fatemeh Shahmoradi Ghaheh1, Milad Razbin2,3, Majid Tehrani4
1Department of Textile Engineering, Faculty of Environmental Science, Urmia University of Technology, Urmia, Iran. f.shahmoradi@uut.ac.ir.
Scientific reports
|July 2, 2024
概括
这项研究优化了织品染色,使用了结合人工神经网络 (ANN) 和遗传算法 (GA) 的新算法. 该方法精确调整了聚胺6和用梅叶染色羊毛织物的可持续染色参数.
科学领域:
- 织品化学 织品化学
- 可持续材料科学科学 可持续材料科学
- 计算机建模 计算建模
背景情况:
- 织品染色是复杂的,有很多因素影响结果.
- 实现最佳染色参数是一个重大挑战.
- 可持续的染色实践越来越重要.
研究的目的:
- 开发和验证一种新的算法方法,以优化织品染色参数.
- 微调度,时间,pH值和温度以提高颜色强度 (K/S).
- 利用梅叶作为聚胺6和羊毛织物的可持续染料来源.
主要方法:
- 响应表面方法 (RSM),人工神经网络 (ANN) 和遗传算法 (GA) 的整合.
- 颜色强度 (K/S) 的量化作为主要响应变量.
- 将算法应用于聚胺6和羊毛织物染色.
主要成果:
- 与RSM (R2 > 0.92) 相比,ANN表现出更高的性能 (R2 ≈ 1).
- 聚胺的优化参数6:100重量%度,86.06分钟,pH值8.28,100°C,K/S值为10.21.
- 羊毛面料的优化参数:55.85重量%度,120分钟,pH值5,100°C,K/S值为7.65.
结论:
- 拟议的ANN-GA综合方法有效优化织品染色参数.
- 这种方法促进了使用天然染料来源的可持续织染料.
- 该方法可适应优化各种织染工艺.
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