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. 2023 Mar 28;16(7):2686.
doi: 10.3390/ma16072686.

Synthesis of Natural-Inspired Materials by Irradiation: Data Mining from the Perspective of Their Functional Properties in Wastewater Treatment

Mirela Braşoveanu  1 , Hassan Sabbaghi  2 , Monica R Nemţanu  1
Affiliations

Synthesis of Natural-Inspired Materials by Irradiation: Data Mining from the Perspective of Their Functional Properties in Wastewater Treatment

Mirela Braşoveanu et al. Materials (Basel). .

Abstract

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-g-PAM) were synthesized by irradiation, and their flocculation efficiency regarding the total suspended solids (TSS), chemical oxygen demand (COD), and fatty matters (FM) 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 COD was significantly affected by the irradiation dose and monomer concentration, and FM was mainly affected by the dose rate (significance level = 0.05). TSS showed the highest negative correlation with the tested variables. Moreover, the consequences of MLR demonstrated an acceptable accuracy (mean absolute percentage error < 5%) for COD and FM; 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.

Keywords: biopolymer; copolymerization; correlation; feature selection; flocculant; functionality; prediction; principal components; statistical techniques.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Scree plot for explained eigenvalues and cumulative variability (%) from PCA analysis.
Figure 2
Figure 2
Correlations circle between study variables and factors.
Figure 3
Figure 3
Factor scores in PCA Biplot (treatments T1…T18).
Figure 4
Figure 4
AHC Dendrogram of treatments.
Figure 5
Figure 5
Regression tree: decision making for TSS.
Figure 6
Figure 6
Regression tree: decision making for COD.
Figure 7
Figure 7
Regression tree: decision making for FM.
Figure 8
Figure 8
MLR training set in prediction of study variables versus empirical data (Case = COD).
See this image and copyright information in PMC

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