پیش‌بینی حلالیت تعادل دی‌اکسیدکربن در محلول تری‌اتانول آمین + پیپرازین + آب با استفاده از مدل‌سازی شبکۀ عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 پژوهشگر پسا دکتری دانشکدۀ مهندسی شیمی، نفت و گاز، دانشگاه علم و صنعت ایران

2 استاد دانشکدۀ مهندسی شیمی، نفت و گاز، دانشگاه علم و صنعت ایران

چکیده

در این مطالعه، مدل‌ مبتنی بر هوش مصنوعی برای پیش‌بینی حلالیت تعادلی دی‌اکسیدکربن در سامانۀ حلال آمین (تری‌اتانول آمین + پیپرازین + آب) با هدف جذب
دی‌اکسیدکربن ایجاد شده است. در مدل پرسپترون چندلایه، داده‌های حلالیت (بارگذاری دی‌اکسیدکربن در محلول آمین) به‌عنوان تابعی از فشار جزئی دی‌اکسیدکربن، دمای سامانه و ترکیب آمین بررسی شد. الگوریتم لونبرگ- مارکوارت پس‌انتشار برای پیش‌بینی فشار جزئی دی‌اکسیدکربن استفاده شد. نسبت نهایی آموزش، اعتبارسنجی و مجموعۀ داده‌های آزمایشی تقریباً 70:15:15 بود. ساختار بهینۀ پرسپترون چندلایه (MLP) در الگوریتم لونبرگ- مارکوارت برای فشار جزئی دی‌اکسیدکربن با 20 نورون در اولین لایۀ پنهان و 10 نورون در لایۀ پنهان دوم ایجاد شده است. ضریب همبستگی 995/0 بین نتایج تجربی و محاسبات شبکۀ عصبی مصنوعی وجود دارد که سازگاری عالی بین آن‌ها را نشان می‌دهد. بهترین عملکرد اعتبارسنجی 0043497/0 از دورۀ 13 بود. به‌طور کلی، نتایج نشان می‌دهد که مدل اعمال‌شده می‌تواند پیش‌بینی دقیقی از فشار جزئی و یا حلالیت برای شرایط مختلف عملیاتی ارائه دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Prediction of Carbon Dioxide Equilibrium Solubility in Solution of Triethanolamine + Piperazine + Water using Artificial Neural Network Modeling

نویسندگان [English]

  • Z. Khoshraftar 1
  • A. Ghaemi 2
1 Postdoctoral Researcher of Chemical Engineering, Iran University of Science and Technology
2 Professor of Chemical Engineering, Iran University of Science and Technology
چکیده [English]

In this study, we developed artificial neural network-based model for prediction of equilibrium solubility of carbon dioxide in the amine solvent system of (triethanolamine + piperazine + water) for the purpose of carbon dioxide uptake. In the MLP model, the solubility data (CO2 loading in the amine solution) were investigated as functions of CO2 partial pressure, system temperature, and amine composition. The Levenberg–Marquardt back-propagation (LMP) algorithm was used to predict the partial pressure of carbon dioxide. The final ratio of training, validation, and test datasets was approximately 70:15:15. The optimum multilayer perceptron (MLP) structure in Levenberg-Marquardt algorithm for CO2 partial pressure is created with 20 neurons in the first hidden layer and 10 neurons in the second hidden layer. There was a 0.99546 correlation coefficient between the experimental results and the artificial neural network (ANN) calculations, demonstrating excellent compatibility between them. The best validation performance was 0.0043497 from epoch 13. In general, the results show that the applied model can provide an accurate prediction of partial pressure or solubility for different operating conditions.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • Solubility of Carbon Dioxide
  • Blended Amines
  • Triethanolamine
  • Piperazine

مراجع

 

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مهندسی شیمی ایران
دوره 22، شماره 129
مهر و آبان 1402
صفحه 78-97

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