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

作　　者：QAISAR MAHMOOD PING ZHENG DONG-LEI WU XU-SHENG WANG HAYAT YOUSAF EJAZ UL-ISLAM MUHAMMAD JAFFAR HASSAN GHULAM JILANI MUHAMMAD RASHID AZIM 

机构地区：[1]Department of Environmental Engineering, College of Environment and Resource Science, Zhejiang University, Hangzhou 310029, Zhejiang, China [2]Institute of Statistical Genetics, Zhejiang University, Hangzhou 310029, Zhejiang, China tUniversity of Arid Agriculture, Rawalpindi, Pakistan [3]University of Arid Agriculture, Rawalpindi, Pakistan

出　　处：《Biomedical and Environmental Sciences》2007年第5期398-403,共6页生物医学与环境科学（英文版）

摘　　要：Objective 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 sulfate 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.Objective 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 sulfate 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.

关 键 词：Artificial neural networks Effluent sulfide prediction Effluent nitrite prediction Principal components analysis Wastewater treatment ASO reactor 

分 类 号：X703[环境科学与工程—环境工程]