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Supervision of ethylene propylene diene M-class (EPDM) rubber vulcanization and recovery processes using attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy and multivariate analysis

机译:使用衰减全反射傅里叶变换红外(aTR FT-IR)光谱和多变量分析监测乙烯丙烯二烯m级(EpDm)橡胶硫化和回收工艺

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摘要

EPDM rubber is widely used in a diverse type of applications, such as the automotive, industrial and construction sectors among others. Due to its appealing features, the consumption of vulcanized EPDM rubber is growing significantly. However, environmental issues are forcing the application of devulcanization processes to facilitate the recovery, which has led rubber manufacturers to implement strict quality controls. Consequently, it is important to develop methods for supervising the vulcanizing and recovering processes of such products. This paper deals with the supervision process of EPDM compounds by means of Fourier transform mid-infrared (FTIR) spectroscopy and suitable multivariate statistical methods. A nondestructive and expeditive classification approach was applied to a sufficient number of EPDM samples with different applied processes, that is, with and without application of vulcanizing agents, vulcanized samples and microwave treated samples. First the FTIR spectra of the samples is acquired and next it is processed by applying suitable feature extraction methods, i.e., principal component analysis (PCA) and canonical variate analysis (CVA) to obtain the latent variables to be used for classifying test EPDM samples. Finally, the k-NN (k nearest neighbor) algorithm was used in the classification stage. Experimental results prove the accuracy of the proposed method and the potential of FTIR spectroscopy in this area, since the classification accuracy can be as high as 100%.
机译:EPDM橡胶被广泛用于各种应用,例如汽车,工业和建筑行业。由于其吸引人的特性,硫化三元乙丙橡胶的消耗量显着增长。然而,环境问题迫使脱硫工艺的应用以促进回收,这导致橡胶制造商实施严格的质量控制。因此,重要的是开发监督此类产品的硫化和回收过程的方法。本文通过傅立叶变换中红外(FTIR)光谱法和合适的多元统计方法来研究EPDM化合物的监督过程。非破坏性和快速分类方法已应用到足够数量的具有不同应用过程的EPDM样品中,即使用和不使用硫化剂,硫化样品和微波处理过的样品。首先获取样品的FTIR光谱,然后通过应用合适的特征提取方法(即主成分分析(PCA)和规范变量分析(CVA))对其进行处理,以获得用于对测试EPDM样品进行分类的潜在变量。最后,在分类阶段使用k-NN(k最近邻)算法。实验结果证明了该方法的准确性以及该领域的FTIR光谱技术的潜力,因为分类精度可以高达100%。

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