Tsai Pei-Fen, Yuan Shyan-Ming
Department of Computer Science, National Yang Ming Chiao Tung University, ChiaoTung Campus, Hsinchu 300093, Taiwan.
Sensors (Basel). 2024 Dec 25;25(1):57. doi: 10.3390/s25010057.
With the fast-fashion trend, an increasing number of discarded clothing items are being eliminated at the stages of both pre-consumer and post-consumer each year. The linear economy produces large volumes of waste, which harm environmental sustainability. This study addresses the pressing need for efficient textile recycling in the circular economy (CE). We developed a highly accurate Raman-spectroscopy-based textile-sorting technology, which overcomes the challenge of diverse fiber combinations in waste textiles. By categorizing textiles into six groups based on their fiber compositions, the sorter improves the quality of recycled fibers. Our study demonstrates the potential of Raman spectroscopy in providing detailed molecular compositional information, which is crucial for effective textile sorting. Furthermore, AI technologies, including PCA, KNN, SVM, RF, ANN, and CNN, are integrated into the sorting process, further enhancing the efficiency to 1 piece per second with a precision of over 95% in grouping textiles based on the fiber compositional analysis. This interdisciplinary approach offers a promising solution for sustainable textile recycling, contributing to the objectives of the CE.
随着快时尚潮流的兴起，每年在消费前和消费后阶段都有越来越多的废弃衣物被淘汰。线性经济产生大量废物，损害环境可持续性。本研究满足了循环经济（CE）中高效纺织品回收的迫切需求。我们开发了一种基于拉曼光谱的高精度纺织品分类技术，该技术克服了废纺织品中纤维组合多样的挑战。通过根据纤维成分将纺织品分为六组，该分类器提高了回收纤维的质量。我们的研究证明了拉曼光谱在提供详细分子组成信息方面的潜力，这对于有效的纺织品分类至关重要。此外，包括主成分分析（PCA）、K近邻算法（KNN）、支持向量机（SVM）、随机森林（RF）、人工神经网络（ANN）和卷积神经网络（CNN）在内的人工智能技术被集成到分类过程中，进一步将效率提高到每秒1件，在基于纤维成分分析对纺织品进行分组时精度超过95%。这种跨学科方法为可持续纺织品回收提供了一个有前景的解决方案，有助于实现循环经济的目标。
