مقایسۀ مدل‌سازی روش‌های شبکۀ عصبی و RSM فرایند استخراج از صفحات مدارچاپی تلفن همراه به‌وسیلۀ افشرۀ لیمو

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناس ارشد مهندسی شیمی، دانشگاه کردستان

2 استادیار مهندسی شیمی، دانشگاه کردستان

چکیده

در این پژوهش، مقایسۀ میان روش‌های شبکۀ عصبی و سطح پاسخ در فرایند بیواسید لیچینگ بر پایۀ استفاده از افشرۀ لیمو برای استخراج فلزات مس و روی از صفحات مدارچاپی تلفن همراه بررسی شده است. سه شاخصۀ مهم چگالی پسماند، غلظت هیدروژن پراکسید و غلظت افشرۀ لیمو بررسی شد. برای بهینه‌سازی شاخصه‌های مؤثر از روش سطح پاسخ (RSM) استفاده شد. نتایج نشان داد که برای ذراتی با اندازۀ 150 تا μm 180 در دمای ثابت20 درجه سلسیوس و زمان  4h در شرایط بهینه شامل چگالی پسماند(w/v) 4/1% غلظت هیدروژن پراکسید(v/v) 2/12% و غلظت افشرۀ لیمو  (v/v) 74%، بازده بازیابی فلزات مس و روی به‌ترتیب 89% و 73% است. هم‌چنین از شبکۀ عصبی مصنوعی برای پیش‌بینی میزان استخراج فلزات مس و روی به‌عنوان تابعی از شاخصه‌های موردبررسی استفاده شد. برای اعتبارسنجی مدل، یک چهارم داده‌های آزمایشگاهی به‌عنوان داده‌های ارزیابی در نظر گرفته شد. نتایج مدل‌سازی شبکۀ عصبی دقت بالایی را به‌منظور پیش‌بینی متغیر هدف نشان داد؛ به‌طوری‌که مقادیر خطای MRE، MSE و R2 به‌ترتیب 485/9%، 254/15 و 9356/0 برای مدل پیش‌بینی کنندۀ استخراج مس و 854/1%، 094/1 و 9963/0 برای مدل پیش‌بینی کنندۀ استخراج روی به‌دست آمد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Neural Network Modeling of the Process of Extraction from Mobile Printed Circuit Boards by Lemon Juice Organic Acids

نویسندگان [English]

  • R. Ozairy 1
  • R. Beigzadeh 2
  • S. O. Rastegar 2
1 M. Sc. in Chemical Engineering, University of Kurdistan
2 Assistant Professor of Chemical Engineering, University of Kurdistan
چکیده [English]

In this study, the application of bio-acid leaching method based on the use of lemon juice to extract copper and zinc metals from mobile printed circuit boards has been investigated. Three important factors were investigated include lemon juice concentration, Solid / Liquid (S/L) ratio, and hydrogen peroxide (H2O2) concentration. Response surface methodology (RSM) was used to optimize the effective factors. The results showed that for particles with a size of 150 to 180 μm at a constant temperature of 20 ° C and time 4 h under optimal conditions including 1.41% (w/v) S/L ratio, 12.2% (v/v) H2O2 and 74% (v/v) lemon juice, copper and zinc recovery efficiencies are 89% and 73%, respectively. Moreover, the artificial neural network was used to predict the extraction of copper and zinc metals as a function of the studied factors. To validate the model, laboratory results were considered as evaluation data. The results of neural network modeling showed high accuracy to predict the target variable. The values of MRE, MSE, and R2 were 9.485, 15.254, and 0.9356% for the copper extraction model and 1.854%, 1.094, and 0.9963% for the zinc extraction model, respectively.
 

کلیدواژه‌ها [English]

  • Bio-Acid Leaching
  • Mobile Printed Circuit Boards (PCBs)
  • Response Surface Methodology
  • Artificial Neural Network

مراجع

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مهندسی شیمی ایران
دوره 21، شماره 121 - شماره پیاپی 121
خرداد و تیر 1401
صفحه 21-32

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