کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
588351 1453342 2015 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm
ترجمه فارسی عنوان
ارزیابی و پیش بینی ضایعات غشایی در یک بیوراکتور غشایی غوطه ور با استفاده همزمان از هوادهی بالا و پایین با استفاده از الگوریتم ژنتیک شبکه عصبی مصنوعی
کلمات کلیدی
هوادهی همزمان، غشای فیبر توخالی، فوران غشاء، فشار مایع غشایی، نفوذ پذیری غشاء، شبکه های عصبی مصنوعی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی بهداشت و امنیت شیمی
چکیده انگلیسی


• Submerged membrane bioreactor with simultaneous upward and downward aeration.
• Simulation of trans-membrane pressure and membrane permeability.
• Radial basis function and multi-layer perceptron were used to perform models.
• Genetic algorithm was utilized to optimize the neural networks.

This paper describes the effect of simultaneous upward and downward aeration on the membrane fouling and process performances of a submerged membrane bioreactor. Trans-membrane pressure (TMP) and membrane permeability (Perm) were simulated using multi-layer perceptron and radial basis function artificial neural networks (MLPANN and RBFANN). Genetic algorithm (GA) was utilized in order to optimize the weights and thresholds of the models. The results indicated that the simultaneous aeration does not significantly improve the removal efficiency of contaminants. The removal efficiencies of BOD, COD, total nitrogen, NH4+−N and TSS were 97.5%, 97%, 94.6%, 96% and 98%, respectively. It was observed that the TMP increases and the Perm decreases as operational time increases. The TMP increasing rate (dTMP/dt) and the Perm decreasing rate (dPerm/dt) for the upward aeration were 2.13 and 2.66 times higher than that of simultaneous aeration, respectively. The training procedures of TMP and Perm models were successful for both RBFANN and MLPANN. The train and test models by MLPANN and RBFANN showed an almost perfect match between the experimental and the simulated values of TMP and Perm. It was illustrated that the GA-optimized ANN predicts TMP and permeability more accurately than a network with a trial-and-error approach calibration.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Process Safety and Environmental Protection - Volume 96, July 2015, Pages 111–124
نویسندگان
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