使用人工神经网络来识别回收颗粒:聚烯基复合材料
Maya T Gómez-Bacab1, Aldo L Quezada-Campos1, Carlos D Patiño-Arévalo2
1Laboratorio de Ingeniería Química, Departamento de Biotecnológicas y Ambientales, Universidad Autónoma de Guadalajara, Av. Patria 1201, Zapopan CP. 45129, Jalisco, Mexico.
Polymers
|September 13, 2025
概括
福利埃变换红外光谱学与人工神经网络相结合,可以准确量化聚烯复合材料中的矿物填充物. 这种快速,非破坏性的方法通过改善分类和质量控制,有助于聚合物回收利用.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 聚合物科学 聚合物科学
背景情况:
- 聚合物回收利用面临挑战,因为复合材料的分类很困难.
- 精确识别矿物填料含量对于有效的回收和质量控制至关重要.
研究的目的:
- 开发一种定量方法,用于预测聚烯复合材料中的矿物填料含量.
- 为此目的,利用里埃变换红外光谱学 (ATR-FTIR) 和人工神经网络 (ANN).
主要方法:
- 采用ATR-FTIR光谱技术收集了聚烯复合材料与粉,碳酸和玻璃纤维填充剂的光谱数据.
- 通过使用光谱特征 (600-1700 cm-1) 来训练ANN模型,以与填充剂度 (wt.%) 相对应.
- 开发的模型使用X射线光 (XRF) 和能量分散式X射线光谱 (EDX) 进行了验证.
主要成果:
- 对于所有测试的矿物填充剂,ANN模型实现了高准确性,预测误差低于7.5%,R值高于0.98.
- 该方法成功量化了商业回收聚烯颗粒中的填充剂含量.
- 验证证实了ATR-FTIR和ANN方法的可靠性.
结论:
- 综合ATR-FTIR和ANN方法提供了一个简单,快速和非破坏性的工具,用于识别再生聚合物中的矿物填充剂类型和含量.
- 这种方法可以显著减少聚合物商业化中的错误分类问题,并加强工业质量控制.
- 该技术适用于非专业用户,促进在回收行业更广泛的应用.
相关概念视频
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