کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
639843 1456950 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparison between artificial neural networks and Hermia’s models to assess ultrafiltration performance
ترجمه فارسی عنوان
مقایسه شبکه های عصبی مصنوعی و مدل های هرمی به منظور بررسی عملکرد اولترافیلتراسیون
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی تصفیه و جداسازی
چکیده انگلیسی


• Feed-forward artificial neural network was employed to model crossflow UF.
• The pretreatment and internal architecture of the ANNs were studied.
• The ANNs achieved very accurate fitting results to experimental data.
• ANNs achieve fittings that were comparable to Hermia’s models.

In this work, flux decline during crossflow ultrafiltration of macromolecules with ceramic membranes has been modeled using artificial neural networks. The artificial neural network tested was the multilayer perceptron. Operating parameters (transmembrane pressure, crossflow velocity and time) and dynamic fouling were used as inputs to predict the permeate flux. Several pretreatments of the experimental data and the optimal selection of the parameters of the neural networks were studied to improve the fitting accuracy.The fitting accuracy obtained with artificial neural networks was compared with Hermia pore blocking models adapted to crossflow ultrafiltration. The artificial neural networks generate simulations whose performance was comparable to that of Hermia’s models adapted to crossflow ultrafiltration. Considering the computational speed, high accuracy and the ease of the artificial neural networks methodology, they are a competitive, powerful and fast alternative for dynamic crossflow ultrafiltration modeling.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Separation and Purification Technology - Volume 170, 1 October 2016, Pages 434–444
نویسندگان
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