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Development of intelligent sorting system realized with the aid of laser-induced breakdown spectroscopy and hybrid preprocessing algorithm-based radial basis function neural networks for recycling black plastic wastes

机译:借助激光诱导击穿光谱技术和基于混合预处理算法的径向基函数神经网络实现智能分类系统的开发,以回收黑色塑料废料

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摘要

Plastic recycling has been the key issue for reducing environmental problems and resolving resource depletion. To improve the recovery rate of plastics, the plastic wastes are correctly identified according to their resin type. However, the identification system, which is able to identify black plastics according to not only the type of black plastics but also the grade of resins correctly, has not been introduced. In this paper, laser-induced breakdown spectroscopy, intelligent algorithms and preprocessing algorithms are used to improve the identification of black plastics such as polypropylene, polystyrene (PS), and acrylonitrile butadiene styrene (ABS). The laser-induced breakdown spectroscopy is capable of obtaining the characteristic spectrum regardless of material's physical state. To extract the new features which are very valuable to improving learning performance, increasing computational efficiency, and building better generalization models from the obtained spectra through laser-induced breakdown spectroscopy, the hybrid preprocessing algorithm, composed of principal component analysis and independent component analysis, is used. In addition, the intelligent algorithm named the extended radial basis function neural networks inheriting the advantages of fuzzy theory and neural networks is used to identify black plastic samples into several categories with respect to their resins. The proposed identification system, composed of three parts such as laser induced breakdown spectroscopy, hybrid preprocessing algorithms, and an efficient intelligent classification algorithm, is able to show the synergy effect on the black plastic identification problem. From several experimental results, it can be seen that the identification system based on laser-induced breakdown spectroscopy and the intelligent algorithm is used for identification of black plastics by resin type.
机译:塑料回收一直是减少环境问题和解决资源枯竭的关键问题。为了提高塑料的回收率,可根据其树脂类型正确识别塑料废物。然而,尚未引入能够不仅根据黑色塑料的类型而且能够正确地根据树脂的等级识别黑色塑料的识别系统。在本文中,使用激光诱导击穿光谱,智能算法和预处理算法来改进对黑色塑料的识别,例如聚丙烯,聚苯乙烯(PS)和丙烯腈丁二烯苯乙烯(ABS)。激光诱导击穿光谱仪能够获得特征光谱,而与材料的物理状态无关。为了提取新特征,这些特征对于提高学习性能,提高计算效率以及通过激光诱导击穿光谱从获得的光谱中建立更好的泛化模型非常有价值,是由主成分分析和独立成分分析组成的混合预处理算法。用过的。此外,继承了模糊理论和神经网络优势的智能算法(称为扩展径向基函数神经网络)用于将黑色塑料样品的树脂识别为几类。所提出的识别系统由激光诱导击穿光谱,混合预处理算法和高效的智能分类算法三部分组成,能够显示出对黑色塑料识别问题的协同效应。从几个实验结果可以看出,基于激光诱导击穿光谱学和智能算法的识别系统可用于按树脂类型识别黑色塑料。

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