پیش‌بینی میزان جذب آلاینده‌های رنگی آزو از پساب با استفاده از جاذب‌های متخلخل چارچوب‌های فلز-آلی

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار، گروه مهندسی شیمی، دانشکده مهندسی شیمی و مواد، دانشگاه صنعتی شاهرود، شاهرود، ایران، صندوق پستی: 3619995161

چکیده

در پژوهش حاضر، توانایی بالقوه روش‌های یادگیری ماشین هوشمند از قبیل LS-SVM، RBFNN، MLPNN و ANFIS برای پیش‌بینی بازده حذف رنگزاهای آزو از پساب مورد بررسی قرار گرفت. به این منظور، بانک بزرگی از داده‌های مربوط به جذب سطحی رنگزاهای آزو بوسیله چارچوب­های فلز-آلی گوناگون به عنوان جاذب­های متخلخل تحت شرایط مختلف از قبیل مقدار جاذب، غلظت اولیه رنگزاها، pH محلول، مساحت سطح ویژه جاذب، دما و زمان تماس جمع‌آوری شدند. بررسی متغیرهای مختلف آماری و مقایسه مدل‌های مختلف نشان داد که مدل LS-SVM کمترین خطا و در نتیجه دقیق‌ترین پیش‌بینی را برای میزان جذب آلاینده‌های رنگی در بین سایر مدل‌ها ارائه می‌دهد که در آن مقادیر AARE (%)، R2، STD، و RMSE به ترتیب برابر با 1.844، 0.9899، 0.0213 و 18.511 درصد به دست آمدند. همچنین، این الگوریتم سازگاری دقیق­تری با روند فرآیند جذب سطحی رنگزاهای آزو با تغییرات غلظت اولیه رنگزا، pH محلول و دما نشان داد. آنالیز حساسیت نشان داد که سطح ویژه و مقدار جاذب متخلخل تاثیر مثبت و عواملی مانند غلظت اولیه و pH اثر منفی بر روی بازده جذب دارند.

کلیدواژه‌ها

عنوان مقاله [English]

Prediction of the Adsorption Amount of Azo Dyes Pollutants from Wastewater Using Porous Metal-organic Framework Adsorbents

نویسنده [English]

  • Jafar Abdi
Faculty of Chemical and Material Engineering, Shahrood University of Technology, P.O. Box: 3619995161, Shahrood, Iran
چکیده [English]

In this project, the potential capability of intelligent machine learning methods such as LS-SVM, RBFNN, MLPNN, and ANFIS was investigated for estimating the efficiency of azo dyes removal from wastewater. To this aim, a huge data bank of azo dyes adsorption by metal-organic frameworks as porous adsorbents were collected under different conditions, including adsorbent dosage, initial dye concentration, solution pH, specific surface area, temperature, and contact time. Assessing different statistical parameters and comparing the models showed that LS-SVM approach had the minimum error and, therefore, it conferred the most accurate prediction for the efficiency of azo dyes removal by MOFs among other models. The values of AARE (%), R2, STD, and RMSE were calculated 1.844 %, 0.9899, 0.0213, and 18.511, respectively for LS-SVM. Also, this model illustrated precise compatibility with adsorption trends by variation of initial dye concentration, pH, and temperature. The sensitivity analysis presented that adsorbent surface area and adsorbent dosage had a positive impact and initial concentration and pH negatively influenced estimating the removal of dyes.

کلیدواژه‌ها [English]

  • Metal
  • organic framework Wastewater treatment Azo dyes Intelligent models Sensitivity analysis

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