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Modeling and Experimental Prediction of Wastewater Treatment Efficiency in Oil Refineries Using Activated Sludge Process | ||
| Journal of Chemical and Petroleum Engineering | ||
| مقاله 6، دوره 48، شماره 1، شهریور 2014، صفحه 69-79 اصل مقاله (147.74 K) | ||
| نوع مقاله: Original Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jchpe.2014.5587 | ||
| نویسندگان | ||
| Yasser Vasseghian* 1؛ Mojtaba Ahmadi1؛ Fazel Dolati2؛ Aliakbar Heydari2 | ||
| 1Chemical Engineering Department, Faculty of Engineering, Razi University, Kermanshah, Iran | ||
| 2Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran | ||
| چکیده | ||
| In this study, activated sludge process for wastewater treatment in a refinery was investigated. For such purpose, a laboratory scale rig was built. The effect of several parameters such as temperature, residence time, effect of Leca (filling-in percentage of the reactor by Leca) and UV radiation on COD removal efficiency were experimentally examined. Maximum COD removal efficiency was obtained to be 94% after final testing. An artificial neural network (ANN) was applied to evaluate the effect of operational parameters on the efficiency as the next step. A two-layered ANN provided the best results, using Levenberg–Marquardt back propagation learning algorithm (trainLM) in which tansig and purelin used as transfer functions in the hidden and output layers. Furthermore, the application of three neurons in the hidden layer caused to gratify network training while overfitting was hindered. ANN model, provided a good estimation for correlation coefficient and the mean square error (MSE) which calculated 0.997 and 0.5 × 10-3 respectively. | ||
| کلیدواژه‌ها | ||
| Wastewater treatment؛ COD Removal؛ Activated Sludge؛ Artificial Neural Network | ||
| مراجع | ||
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