Braşoveanu Mirela, Sabbaghi Hassan, Nemţanu Monica R
National Institute for Laser, Plasma and Radiation Physics, 409 Atomiştilor St., P.O. Box MG-36, 077125 Bucharest-Măgurele, Romania.
Department of Food Science and Technology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948978, Razavi Khorasan Province, Iran.
Materials (Basel). 2023 Mar 28;16(7):2686. doi: 10.3390/ma16072686.
The present study is focused on assessing the interrelation of variables involved in the synthesis of natural-inspired copolymers by electron beam grafting while taking the functionality of the resulting materials into account. In this respect, copolymers of starch-graft-polyacrylamide (St--PAM) were synthesized by irradiation, and their flocculation efficiency regarding the total suspended solids (), chemical oxygen demand (), and fatty matters () was tested in coagulation-flocculation experiments at laboratory scale on wastewater from the oil industry. Data mining involved approaches related to the association (correlation and dimensionality reduction with principal component analysis (PCA)), clustering by agglomerative hierarchical clustering (AHC), classifying by classification and regression tree (CART), and prediction (decision tree prediction, multiple linear regression (MLR), and principal component regression (PCR)) of treatments applied with the variation of the monomer concentration, irradiation dose, and dose rate. The relationship mining proved that the level of was significantly affected by the irradiation dose and monomer concentration, and was mainly affected by the dose rate (significance level = 0.05). showed the highest negative correlation with the tested variables. Moreover, the consequences of MLR demonstrated an acceptable accuracy (mean absolute percentage error < 5%) for and ; meanwhile, linear modeling together with the consequences of PCA in the structure of PCR could help to simplify and improve the prediction accuracy of equations.
本研究聚焦于评估通过电子束接枝合成天然灵感共聚物时所涉及变量的相互关系，同时考虑所得材料的功能。在这方面，通过辐照合成了淀粉接枝聚丙烯酰胺（St-PAM）共聚物，并在实验室规模下对石油工业废水进行的混凝-絮凝实验中测试了它们对总悬浮固体（）、化学需氧量（）和脂肪物质（）的絮凝效率。数据挖掘涉及与关联（通过主成分分析（PCA）进行相关性和降维）、通过凝聚层次聚类（AHC）进行聚类、通过分类与回归树（CART）进行分类以及对随着单体浓度、辐照剂量和剂量率变化而应用的处理进行预测（决策树预测、多元线性回归（MLR）和主成分回归（PCR））等方法。关系挖掘证明，的水平受辐照剂量和单体浓度的显著影响，而主要受剂量率影响（显著性水平 = 0.05）。与测试变量显示出最高的负相关性。此外，MLR的结果表明对于和具有可接受的准确性（平均绝对百分比误差 < 5%）；同时，线性建模以及PCR结构中PCA的结果有助于简化并提高方程的预测准确性。
