Mana Abazari; Habib Allah Tayebi; Khadijeh Aghajani
Abstract
Background and Purpose: the investigation of the adsorption of pollutantsfrom aquatic environments with the least number of experiments, is one ofthe concerns of researchers. In the present study, the aim is to model theadsorption process of acid dye 62 by a metal-organic framework containingaluminum ...
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Background and Purpose: the investigation of the adsorption of pollutantsfrom aquatic environments with the least number of experiments, is one ofthe concerns of researchers. In the present study, the aim is to model theadsorption process of acid dye 62 by a metal-organic framework containingaluminum (MIL-53(Al)-NH2).Materials and Methods: In this study, MIL-53(Al)-NH2 was synthesized fromthe raw material of 2-amino terephthalic acid and aluminum nitrate. Afterexamining the effective parameters on dye adsorption, artificial neuralnetwork (ANN), multiple linear regression (MLR) and multiple nonlinearregression (MNLR) have been used to predict the amount of dye adsorption.Results: The results of XRD, FE-SEM and FTIR analyzes indicated theappropriate synthesis of MIL-53(Al)-NH2. The optimal conditions are: pH=2,time 60 minutes, adsorbent dosage 0.02g and temperature 25°C. Accordingto the results, in the comparison between the three used methods, the neuralnetwork model has the highest prediction accuracy. The output of this modelhas the lowest root mean square error (RMSE) and the highest correlationcoefficient (CC) with true data in comparison with multiple linear and nonlinearregression models.Conclusion: According to the results, it can be seen that the MIL-53(Al)-NH2is an efficient compound and in addition, due to the high efficiency of theartificial neural network model, this model can be used to ensure the resultsof dye removal and reduce costs by reducing the number of experiments.