Laboratory for Circular Process Engineering (LCPE), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Graaf Karel de Goedelaan 5, B-8500 Kortrijk, Belgium.
Research Group Sustainable Systems Engineering (STEN), Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000, Gent, Belgium.
Chemosphere. 2024 Feb;350:141069. doi: 10.1016/j.chemosphere.2023.141069. Epub 2023 Dec 29.
Deodorization and, in a broader sense, the removal of volatile organic compounds (VOCs) from plastic waste have become increasingly important in the field of plastic recycling, and various new decontamination techniques have been developed. Both in research and industrial practice, the selection of VOCs has been random or unsubstantiated, making it difficult to compare studies and assess decontamination processes objectively. Thus, this study proposes the use of Statistical Molecular Design (SMD) and Quantitative Structure - Activity Relationship (QSAR) as chemometric tools for the selection of representative VOCs, based on physicochemical properties. Various algorithms are used for SMD; hence, several frequently used D-Optimal Onion Design (DOOD) and Space-Filling (SF) algorithms were assessed. Hereby, it was validated that DOOD, by dividing the layers based on the equal-distance approach without so-called 'Adjacent Layer Bias', results in the most representative selection of VOCs. QSAR models that describe VOC removal by water-based washing of plastic waste as a function of molecular weight, polarizability, dipole moment and Hansen Solubility Parameters Distance were successfully established. An adjusted-R value of 0.77 ± 0.09 and a mean absolute error of 24.5 ± 4 % was obtained. Consequently, by measuring a representative selection of VOCs compiled using SMD, the removal of other unanalyzed VOCs was predicted on the basis of the QSAR. Another advantage of the proposed chemometric selection procedure is its flexibility. SMD allows to extend or modify the considered dataset according to the available analytical techniques, and to adjust the considered physicochemical properties according to the intended process.
脱臭，更广泛地说，从塑料废物中去除挥发性有机化合物（VOC），在塑料回收领域变得越来越重要，并且已经开发出各种新的净化技术。在研究和工业实践中，VOC 的选择都是随机的或没有根据的，这使得很难比较研究并客观评估净化过程。因此，本研究提出使用统计分子设计（SMD）和定量构效关系（QSAR）作为基于物理化学性质选择代表性 VOC 的化学计量工具。SMD 使用了各种算法；因此，评估了几种常用的 D-Optimal Onion Design（DOOD）和空间填充（SF）算法。通过在此处验证，基于等距方法而不是所谓的“相邻层偏差”对层进行划分的 DOOD 导致 VOC 最具代表性的选择。成功建立了描述塑料废物通过水基洗涤去除 VOC 的 QSAR 模型，该模型作为分子量、极化率、偶极矩和 Hansen 溶解度参数距离的函数。调整后的 R 值为 0.77±0.09，平均绝对误差为 24.5±4%。因此，通过使用 SMD 编译具有代表性的 VOC 选择，可以根据 QSAR 预测其他未分析 VOC 的去除。所提出的化学计量选择程序的另一个优点是其灵活性。SMD 允许根据可用的分析技术扩展或修改考虑的数据集，并根据预期的过程调整考虑的物理化学性质。
