Volume 20 Issue 5
Oct.  2007
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QAISAR MAHMOOD, PING ZHENG, DONG-LEI WU, XU-SHENG WANG, HAYAT YOUSAF, EJAZ UL-ISLAM, MUHAMMAD JAFFAR HASSAN, GHULAM JILANI, MUHAMMAD RASHID AZIM. Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks[J]. Biomedical and Environmental Sciences, 2007, 20(5): 398-403.
Citation: QAISAR MAHMOOD, PING ZHENG, DONG-LEI WU, XU-SHENG WANG, HAYAT YOUSAF, EJAZ UL-ISLAM, MUHAMMAD JAFFAR HASSAN, GHULAM JILANI, MUHAMMAD RASHID AZIM. Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks[J]. Biomedical and Environmental Sciences, 2007, 20(5): 398-403.

Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks

  • During present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance.Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network.Results Within the range of experimental conditions tested,it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of suffate to an acceptable manner.Conclusion Apart from experimentation,ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies,better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and,as a consequence,better effluent quality.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks

Abstract: During present investigation the data of a laboratory-scale anoxic sulfide oxidizing (ASO) reactor were used in a neural network system to predict its performance.Methods Five uncorrelated components of the influent wastewater were used as the artificial neural network model input to predict the output of the effluent using back-propagation and general regression algorithms. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA) before they are fed to a back propagated neural network.Results Within the range of experimental conditions tested,it was concluded that the ANN model gave predictable results for nitrite removal from wastewater through ASO process. The model did not predict the formation of suffate to an acceptable manner.Conclusion Apart from experimentation,ANN model can help to simulate the results of such experiments in finding the best optimal choice for ASO based denitrification. Together with wastewater collection and the use of improved treatment systems and new technologies,better control of wastewater treatment plant (WTP) can lead to more effective maneuvers by its operators and,as a consequence,better effluent quality.

QAISAR MAHMOOD, PING ZHENG, DONG-LEI WU, XU-SHENG WANG, HAYAT YOUSAF, EJAZ UL-ISLAM, MUHAMMAD JAFFAR HASSAN, GHULAM JILANI, MUHAMMAD RASHID AZIM. Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks[J]. Biomedical and Environmental Sciences, 2007, 20(5): 398-403.
Citation: QAISAR MAHMOOD, PING ZHENG, DONG-LEI WU, XU-SHENG WANG, HAYAT YOUSAF, EJAZ UL-ISLAM, MUHAMMAD JAFFAR HASSAN, GHULAM JILANI, MUHAMMAD RASHID AZIM. Prediction of Anoxic Sulfide Biooxidation Under Various HRTs Using Artificial Neural Networks[J]. Biomedical and Environmental Sciences, 2007, 20(5): 398-403.

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