نوع مقاله : مقالات پژوهشی
نویسندگان
1 گروه مهندسی نساجی، واحد قائمشهر ، دانشگاه آزاد اسلامی ، قائمشهر، ایران
2 گروه مهندسی کامپیوتر، دانشگاه مازندران ، بابلسر، ایران
چکیده
زمینه و هدف: مدل سازی جذب ترکیبات آلاینده از محیط های آبی با کمترین تعداد آزمایشات، یکی از دغدعه های محققین می باشد. در پژوهش حاضر هدف مدل سازی فرایند جذب رنگزای اسیدی آبی 62 با ترکیب آلی-فلزی حاوی آلومینیوم (MIL-53(Al)-NH2) می باشد.
مواد و روش ها: در این پژوهش، MIL-53(Al)-NH2 از ماده اولیه 2- آمینو ترفتالیک اسید و نیترات آلومینیوم سنتز شد. پس از بررسی پارامترهای موثر بر جذب رنگزا، از روش های شبکه عصبی مصنوعی(ANN)، رگرسیون خطی چندگانه (MLR) و رگرسیون غیرخطی چندگانه (MNLR) برای پیش بینی میزان جذب رنگزا استفاده شده است.
یافته ها: نتایج به دست آمده از آنالیز پراش اشعه ایکس (XRD)، میکروسکوپ الکترونی روبرشی گسیل میدانی (FE-SEM) و طیف سنج مادون قرمز تبدیل فوریه سنتز مناسب MIL-53(Al)-NH2 را نشان داد. شرایط بهینه بصورت 2=pH، زمان60 دقیقه، میزان جاذب 02/0 گرم و دمای 25 درجه سانتیگراد می باشد. بر اساس نتایج ، در مقایسه بین سه روش استفاده شده، مدل شبکه عصبی از بالاترین دقت پیش بینى برخوردار است. خروجی ایجاد شده با استفاده از این مدل در قیاس با مدل های رگرسیون خطی و غیر خطی چندگانه، کمترین جذر میانگین مربعات خطا (RMSE)و بیشترین مقدار ضریب همبستگی(CC) با داده های واقعی را دارا می باشد.
نتیجه گیری: با توجه به نتایج می توان دریافت که MIL-53(Al)-NH2 یک جاذب کارامد بوده و در ضمن با توجه به کارایى بالاى مدل شبکه عصبى مصنوعى مى توان از این مدل جهت حصول اطمینان از نتایج حذف رنگزا و کاهش هزینه بواسطه کاهش تعداد آزمایشات استفاده کرد.
کلیدواژه‌ها
عنوان مقاله [English]
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
نویسندگان [English]
- 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.
چکیده [English]
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.
کلیدواژه‌ها [English]
- Adsorption Process
- MIL-53(Al)-NH2
- Artificial Neural Network (ANN)
- Multiple Linear Regression (MLR)
- Multiple Nonlinear Regression (MNLR)
- Shabandokht M. Binaeian E. Tayebi H A. Adsorption of food dye Acid red 18 onto polyaniline-modified rice husk composite: isotherm and kinetic analysis. Desalination and Water Treatment 2016; 57: 27638-27650.
- Torabinejad A. Nasirizadeh N. Tayebi H A. Synthesize and characterization of Aminosilane functionalized MCM-41 for removal of anionic dye: Kinetic and thermodynamic stud. International Journal of Nano Dimension 2005 7; 4: 295-307.
- Akbartabar I. Yazdanshenas M. Tayebi H A. Physical chemistry studie of Acid Dye Removal from aqueous Media by Mesoporous Nano Composite Adsorption Isotherm, kinetic and thermodynamic studies. Physical Chemistry Research 2017; 5: 659-679.
- Golshan A. Rashidi A. Tayebi H A. Yazdanshenas M E. Comparison of different kinetic models for adsorption of acid blue 62 as an environmental pollutant from aqueous solution onto mesoporous Silicate SBA-15 modified by Tannic acid. International Journal of Nano Dimension 2018; 9 : 79-88.
- Mirzaie M. Rashidi A.Tayebi H A. Optimized removal of acid blue 62 from textile waste water by SBA-15/PAMAM dendrimer hybrid using response surface methodology. J0urnal of Polymer and the Environment 2018; 26: 1831-1843.
- Tayebi H A. Dalirandeh Z. Shokuhi Rad A. Synthesis of polyaniline/Fe3O4 magnetic nanoparticles for removal of reactive red 198 from textile waste water: kinetic, isotherm, and thermodynamic studies. Desalination and Water Treatment 2016; 57: 22551-22563.
- Zareyee D. Tayebi H A. Javadi S H. Preparation of polyaniline/activated carbon composite for removal of reactive red 198 from aqueous solution. Iranian Journal of Organic Chemistry 2012; 4(1): 799-802. (Persian)
- Shafiabadi M. Dashti A. Tayebi H A. Removal of Hg (II) from aqueous solution using polypyrrole/SBA-15 nanocomposite experimental and modeling. Synthetic Metals 2016; 212: 154-160.
- Tayebi H A. Ghanei M. Aghajani K. Modeling of reactive orange 16 dye removal from aqueous media by mesoporous silica/crosslinked polymer hybrid using RBF, MLP and GMDH neural network models. Journal of Molecular Structure. 2019; 1178: 523-514.
- Binaeian E. Seghatoleslami N. Tayebi H A. Preparation of titanium dioxide nanoparticles supported on hexagonal mesoporous silicate (HMS) modified by oak gall tannin and its photocatalytic performance in degradation of azo dye. Advanced Powder Technology 2016; 27(4): 1047-1055.
- Torabinejad A. Yazdanshenas M E. Tayebi H A. Synthesis of conductive polymer-coated mesoporous MCM-41 for textile dye removal from aqueous media. Journal of Nanostructure in Chemistry 2017; 7: 217-229.
- Eren M. Arslanoglu H. Production of microporous Cu-doped BTC (Cu-BTC) metal-organic framework composite materials, superior adsorbents for the removal of methylene blue (Basic Blue 9). Journal of Environmental Chemical Engineering 2020; 8(5): 104247.
- Tayebi H A. Sadeghi M. Moradian M. Synthesis of SBA-15/ZIF-8 nanocomposite for removal of reactive red 198 from textile waste water. Journal of Textile Science and Technology In Press 2021. (Persian)
- catalyst for organic dye removal Separation and Purification Technology. 2021; 267: 118620.
- Binaeian E. Li Y. Tayebi H A. et al. Enhancing toxic gas uptake performance of Zr-based MOF through uncoordinated carboxylate and copper insertion ammonia adsorption. Journal of Hazardous Materials 2021; 416: 125933.
- Aghajani K. Tayebi H A. Synthesis of SBA-15/PAni mesoporous composite for adsorption of reactive dye from aqueous media: RBF and MLP networks predicting models. Fibers and Polymers 2017; 18 3: 465-475.
- Aghajani K. Tayebi H A. Adaptive neuro-fuzzy inference system analysis on adsorption studies of reactive red 198 from aqueous solution by SBA-15/CTAB composite. Spectrochimica Acta part: A Molecular and Biomolecular Spectroscopy 2017; 171: 439-448.
- Tayebi H A. Using RBF and MLP models in predicting Reactive Red 198 removal from aqueous solution by SBA-15/CTAB composite. Desalination and Water Treatment 2017; 66: 263-273.
- Onu C E. Nwabanne J T. Ohale P E. Comparative analysis of RSM, ANN and ANFIS and the mechanistic modeling in eriochrome black-T dye adsorption using modified clay. South African Journal of Chemical Engineering 2021; 36: 24.
- Ghosh I. Kar S. Chatterjee T. et al. Adsorptive removal of Safranin-O dye from aqueous medium using coconut coir and its acid-treated forms Adsorption study scale-up design MPR and GA-ANN modeling. Sustainable Chemistry and Pharmacy 2021; 19: 100374.
- Taoufik N. Elmchaouri A. El Mahmoudi S. et al. Comparative analysis study by response surface methodology and artificial neural network on salicylic acid adsorption optimization using activated carbon. Environmental Nanotechnology Monitoring & Management 2021; 15: 100448.
- Schio R. Salau N. Mallmann E. Dotto G L. Modeling of fixed-bed dye adsorption using response surface methodology and artificial neural network. Chemical Engineering Communications 2021; 208 8: 1081.
- Ghorbani M A. Ahmad Zadeh H. Isazadeh M. A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environmental Earth Sciences 2016; 75(6): 476.
- Chakraborty A. Diganta G. Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences 2017; 10 17: 385.
- Ghaedi A. Ghaedi M M. Karami P. Comparison of ultrasonic with stirrer performance for removal of sunset yellow (SY) by activated carbon prepared from wood of orange tree artificial neural network modeling. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy 2015; 138: 789-799.
- Karimiyan A. Egdernezhad A. Simulation of Groundwater Level and Salinity in Ramhormoz Plain Using Artificial Neural Network Model and Optimized Artificial Neural Network Model. Iranian Journal of Research in Environmental Health 2021; 7 1: 17-26.
- Samy M. Ibrahim M G. Alalm M G. et al. MIL-53 (Al)/ZnO coated plates with high photocatalytic activity for extended degradation of trimethoprim via novel photocatalytic reactor. Separation and Purification Technologyn 2020; 249: 117173.
- Marill KA. Advanced Statistics Linear Regression Part II. Multiple Linear Regression. 2004; 111: 94-102.
- Garson GD. Interpreting neural-network connection weights. Artificial Intelligence Expert. 1991; 6 4: 46-51.
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