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مجله پژوهش در بهداشت محیط

مجله پژوهش در بهداشت محیط

مدل سازی فرایند جذب رنگزای اسیدی آبی 62 از محیط های آبی با ترکیب آلی-فلزی حاوی آلومینیوم با استفاده از روش های شبکه عصبی مصنوعی، رگرسیون خطی و غیر خطی چندگانه

نوع مقاله : مقالات پژوهشی

نویسندگان
مانا اباذری 1
حبیب اله طیبی 1
خدیجه آقاجانی 2
1 گروه مهندسی نساجی، واحد قائمشهر ، دانشگاه آزاد اسلامی ، قائمشهر، ایران
2 گروه مهندسی کامپیوتر، دانشگاه مازندران ، بابلسر، ایران
10.22038/jreh.2022.63313.1480
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
فرایند جذب
MIL-53(Al)-NH2
مدل شبکه عصبى مصنوعى
رگرسیون خطی چندگانه
رگرسیون غیرخطی چندگانه

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)
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