پیش بینی جذب سطحی فنل از فاضلاب با خاک اره به کمک روش‌های هوش-مند

محورهای موضوعی : مدیریت محیط زیست

محسن کشاورز ترک ¹ , احد قائمی ² , منصور شیروانی ³

1 - کارشناسی ارشد مهندسی شیمی، دانشگاه علم و صنعت، تهران، ایران.
2 - استادیار، دانشکده مهندسی شیمی، دانشگاه علم و صنعت، تهران، ایران. *(مسوول مکاتبات)
3 - دانشیار، دانشکده مهندسی شیمی، دانشگاه علم و صنعت، تهران، ایران.

تاریخ دریافت : 1394/04/04 تاریخ پذیرش : 1395/05/02 تاریخ انتشار : 1398/02/01

کلید واژه: درصد جذب فنل, خاک اره, شبیه سازی, مدل‌های هوش‌مند, شرایط بهینه,

چکیده مقاله :

زمینه و هدف:حضور فنل و مشتقات آن در آب و فاضلاب به دلیل خطراتی که بر روی سلامت انسان و محیط زیست دارد، به عنوان یک نگرانی عمده محسوب می شود. به دلیل سمی بودن فنل حتی در غلظت های کم و هم چنین به دلیل این که حضور آن در منابع طبیعی آب می تواند سبب شکل گیری ترکیبات جانبی فرآیندهای گندزدایی و اکسیداسیون شود، این ماده یکی از شایع ترین مواد آلی آلاینده آب می باشد. در مطالعه حاضر فرآیند جذب سطحی فنل از فاضلاب توسط جاذب خاک اره با استفاده از روش های هوش مند شبیه سازی شده است. روش بررسی:شیوه های هوش مند شبکه پرسپترون چندلایه، شبکه برپایه توابع شعاعی و ماشین بردار رگرسیونی جهت شبیه سازی استفاده شده است. جهت طراحی ساختار شبکه ها از 125 مجموعه داده تجربی استفاده شده است. معیارهای ارزیابی و توقف شبکه شامل %AARE و R2 می باشند که برای هر سه مدل محاسبه شده است. یافته ها:نتایج نشان داد که مدل ماشین بردار رگرسیونی با داشتن 5132/0 و 979/0 به ترتیب برای %AARE و R2 بهترین مدل می باشد. کلیه مدل ها نتیجه بهتری نسبت به مدل چند جمله ای درجه دوم از خود نشان دادند. مدل ها تطبیق خوبی با داده های تجربی داشتند. بحث و نتیجه گیری: نتایج مدل ها نشان داد که این مدل ها مقدار جذب فنل را با دقت بالا پیش بینی می نماید. هم چنین براساس نتاج مدل ها، پارامترهای بهینه فرآیند شامل، غلظت اولیه فنل 6/127 میلی گرم بر لیتر، مقدار جاذب 84/0 گرم بر لیتر، pH محلول 62/3، زمان جذب 9/146 دقیقه و درصد جذب فنل متناظر 23/91 % به دست آمد.

چکیده انگلیسی:

  Background and Objective: Phenol presence and its derivatives in water and waste water on human health and the environment is one the major concerns. Because of the toxicity of phenol and also because of the presence of even low concentrations in natural resources, water disinfection and oxidation processes can lead to the formation of additional components. This material is one of the most common organic pollutants in water. In this research, adsorption of phenol from wastewater by sawdust was simulated using intelligent techniques. Method: Intelligent techniques including multi-layer Perceptron, radial basis functions network and support vector regression were used. To design the network structure as well as the training and testing of 125 sets of experimental data is used. Performance evaluation criteria and stop network consists of % AARE and, which is used for all three models. Findings: All models compared results showed that the support vector regression with 0.5132 and 0.979, respectively, for %AARE and  is the best model. All models are better results than the quadratic polynomial model showed. Discussion and Conclusion: Models showed good agreement with experimental data. The optimum conditions for the removal of phenol were 127.6 mg/l of initial phenol concentration, 0.84 g/l of adsorbent dose, natural pH value of 3.62 and 146.9 min of contact time, under these conditions the maximum removal efficiency was 91.23%.

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