کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
153037 456518 2008 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Artificial neural network simulation of combined humic substance coagulation and membrane filtration
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
پیش نمایش صفحه اول مقاله
Artificial neural network simulation of combined humic substance coagulation and membrane filtration
چکیده انگلیسی

Backpropagation artificial neural network (BPNN) was utilized to predict membrane performance. The network was used to predict and compare humic substance (HS) retention and membrane fouling with previously obtained experimental data. BPNN simulation results show high network reliability, if the network is implemented correctly. The difference between the predicted and experimental data was lower than 5%. Low number of training data input has been shown to hinder the learning process. A high number of training data input has lead to over-fitting or memorization of the training data set, reducing the networks predictability. The number of neurons in the hidden layers needs to be chosen carefully to obtain a reliable network. This paper shows that a lower number of neurons result in low reliability, while a higher number of neurons leads to data over-fitting. The best performance was obtained with 2-10 neurons for HS and heavy metals agglomeration and 5-15 neurons for HS coagulation with and without heavy metals.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemical Engineering Journal - Volume 141, Issues 1–3, 15 July 2008, Pages 27–34
نویسندگان
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