Journal of Research in Environmental Health

Journal of Research in Environmental Health

Modeling of Adsorption Process of Acid Blue 62 Dye on Metal-Organic Framework Containing Aluminum using Artificial Neural Network, Multiple Linear and Nonlinear Regression Methods

Document Type : Research article

Authors
Mana Abazari 1
Habib Allah Tayebi 1
Khadijeh Aghajani 2
1 Department of Textile Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran.
2 Department of Computer Engineering, University of Mazandaran, Babolsar, Iran.
10.22038/jreh.2022.63313.1480
Abstract
Background and Purpose: the investigation of the adsorption of pollutants
from aquatic environments with the least number of experiments, is one of
the concerns of researchers. In the present study, the aim is to model the
adsorption process of acid dye 62 by a metal-organic framework containing
aluminum (MIL-53(Al)-NH2).
Materials and Methods: In this study, MIL-53(Al)-NH2 was synthesized from
the raw material of 2-amino terephthalic acid and aluminum nitrate. After
examining the effective parameters on dye adsorption, artificial neural
network (ANN), multiple linear regression (MLR) and multiple nonlinear
regression (MNLR) have been used to predict the amount of dye adsorption.
Results: The results of XRD, FE-SEM and FTIR analyzes indicated the
appropriate synthesis of MIL-53(Al)-NH2. The optimal conditions are: pH=2,
time 60 minutes, adsorbent dosage 0.02g and temperature 25°C. According
to the results, in the comparison between the three used methods, the neural
network model has the highest prediction accuracy. The output of this model
has the lowest root mean square error (RMSE) and the highest correlation
coefficient (CC) with true data in comparison with multiple linear and nonlinear
regression models.
Conclusion: According to the results, it can be seen that the MIL-53(Al)-NH2
is an efficient compound and in addition, due to the high efficiency of the
artificial neural network model, this model can be used to ensure the results
of dye removal and reduce costs by reducing the number of experiments.
 
Keywords
Adsorption Process
MIL-53(Al)-NH2
Artificial Neural Network (ANN)
Multiple Linear Regression (MLR)
Multiple Nonlinear Regression (MNLR)
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Journal of Research in Environmental Health
Volume 8, Issue 3 - Serial Number 31
Autumn 2022
Pages 267-279