مدل‌سازی استخراج فنل از فاضلاب با استفاده از روش‌های هوشمند

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی شیمی، دانشگاه علم و صنعت ایران، تهران، ایران

2 استادیار، دانشکده مهندسی شیمی، دانشگاه علم وصنعت ایران، تهران، ایران

3 دانشیار، دانشکده مهندسی شیمی، دانشگاه علم وصنعت ایران، تهران، ایران

چکیده

در این پژوهش فرایند استخراج فنل از فاضلاب با استفاده از روشهای هوشمند مدل‌سازی شد. از روش‌های هوشمند شامل شبکه پرسپترون چندلایه، شبکه بر پایه توابع شعاعی و ماشین بردار رگرسیونی برای مدل‌سازی استفاده‌ شد. طراحی ساختار بهینه شبکه‌ها از 184 مجموعه داده تجربی انجام شد. ورودی‌های شبکه شامل حجمی آلی به آبی، سرعت روتور، دما، pH و زمان و خروجی شبکه‌ بازده استخراج فنل بود. برای ارزیابی عملکرد و توقف شبکه‌ها از ضریب تعیین و میانگین مربع خطا برای هر سه مدل استفاده ‌شد. مقایسه نتایج کلیه مدل‌ها نشان داد که مدل ماشین بردار رگرسیونی با میانگین مربع خطا برابر 684/0 و ضریب بهترین مدل است. پارامترهای بهینه فرایند شامل نسبت حجمی آلی به آبی 22/0، سرعت روتور 350 دور در دقیقه، دما 86/22 درجه سلسیوس،pH  برابر 7/5، زمان 86/15 دقیقه و بازده استخراج متناظر 35/96 به دست آمد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Modeling of Phenol Extraction from Wastewater Using Intelligent Techniques

نویسندگان [English]

  • Mohsen Keshavarz Tork 1
  • Ahad Ghaemi 2
  • Mansour Shirvani 3
1 MSc Student of Chemical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
2 Ass. Prof. of Chemical Engineering Iran University of Science and Technology (IUST)
3 Assoc. Prof. of Chemical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran
چکیده [English]

In this study, the extraction of phenol from wastewater was simulated using intelligent methods which include multi-layer perceptron, radial basis functions network, and support vector regression. To design the network structure and to train and test it, 184 experimental data sets were used. Inputs to the network consisted of organic–aqueous volume ratio, rotor speed, temperature, pH, and time while extraction efficiency was the output. Root mean square error and correlation coefficient were used in all the three models as network performance and network stop criteria. Comparison of the results obtained from the three models revealed that the support vector regression was the best model with a correlation coefficient of 0.684 and a root mean square error of 0.99. Moreover, model results showed good agreement with experimental data. Optimal process operational parameters included an organic to aqueous volume ratio of 0.22, a rotor speed of 350 rpm, a temperature of 22.86 °C, a pH equal to 7.5, and an agitation time of 15.86 minutes; the corresponding extraction efficiency was obtained to be 96.35.

کلیدواژه‌ها [English]

  • Modeling Phenol Removal
  • Multilayer perceptron network
  • Radial Basis Function Network
  • Support vector regression
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