An on-line wastewater quality predication system based on a time-delay neural network |
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| Authors: | Jiabao Zhu Jim Zurcher Ming Rao Max Q-H Meng[Author vitae] |
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| Affiliation: | aDepartment of Electrical and Computer Engineering, University of Alberta, Canada;bWeyerhaeuser Canada, Grande Prairie Operations, Canada;cIntelligence Engineering Laboratory, Department of Chemical and Materials Engineering, Univeristy of Alberta, Edmonton, Alberta, Canada |
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| Abstract: | The biological treatment process in a wastewater treatment system is a very complex process. The efficiency of the treatment is usually measured by laboratory tests, which typically take five days. In this paper, a time-delay neural network (TDNN) modeling method is proposed for predicting the treatment results. As the first step, a sensitivity analysis performed on a multi-layer perceptron (MLP) network model is used to reduce the input dimensions of the model. Then a TDNN model is further used to improve the performance of the original MLP network model. Subsequently, an on-line prediction and model-updating strategy is proposed and implemented. Simulations using industrial process data show that the prediction accuracy can be improved by the on-line model updating. |
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| Keywords: | Wastewater treatment processes time-delay neural networks reduction of input dimensions modeling on-line prediction model updating |
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