Experimental Analysis and Neural Network Modeling of the Rheological Behavior of Xanthan Gum and Its Derivatives

Abstract

The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722–1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives’ viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R2 = 0.99998 and MSE = 5.95 × 10−3) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.

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“…These measures included the correlation coefficient (R), coefficient of determination (R 2 ), adjusted coefficient of determination (R 2 adj), root mean square error (RMSE), and Error Prediction of Model (EPM). The formulas used to calculate these criteria were as follows [26][27][28][29][30][31][32]: In order to assess the performance of the models and select the optimal one, statistical measures were used. These measures included the correlation coefficient (R), coefficient of determination (R 2 ), adjusted coefficient of determination (R 2 adj ), root mean square error (RMSE), and Error Prediction of Model (EPM).…”

“…These measures included the correlation coefficient (R), coefficient of determination (R 2 ), adjusted coefficient of determination (R 2 adj ), root mean square error (RMSE), and Error Prediction of Model (EPM). The formulas used to calculate these criteria were as follows [26][27][28][29][30][31][32]:…”

Modeling and Optimization of Hybrid Fenton and Ultrasound Process for Crystal Violet Degradation Using AI Techniques

“…The accuracy of each model is evaluated using the following essential performance measures: the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). These individual metrics are computed by applying the following mathematical expressions [40][41][42][43][44]:…”

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

“…The quality of the developed models was examined using statistical analysis and ANOVA at a 95% confidence level. Various model quality measures, such as the p-value, F-value, degree of freedom (DF), coefficient of determination (R 2 ), adjusted determination of coefficient (R adj 2 ), and Root Mean Square Error (RMSE), were used to evaluate the statistical adequacy of the models [15,25,[27][28][29][30][31][32][33][34][35]. The F-value describes the variation in the responses, which can be evaluated using a regression equation, whereas the p-value indicates the statistical adequacy of the developed model.…”