Prediction of Influent Flow Rate: Data-Mining Approach
Publication: Journal of Energy Engineering
Volume 139, Issue 2
Abstract
In this paper, models for short-term prediction of influent flow rate in a wastewater-treatment plant are discussed. The prediction horizon of the model is up to 180 min. The influent flow rate, rainfall rate, and radar reflectivity data are used to build the prediction model by different data-mining algorithms. The multilayer perceptron neural network algorithm has been selected to build the prediction models for different time horizons. The computational results show that the prediction model performs well for horizons up to 150 min. Both the peak values and the trends are accurately predicted by the model. There is a small lag between the predicted and observed influent flow rate for horizons exceeding 30 min. The lag becomes larger with the increase of the prediction horizon.
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Acknowledgments
This research was supported by funding from the Iowa Energy Center (Grant No. 10-1).
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Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 139 • Issue 2 • June 2013
Pages: 118 - 123
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jan 17, 2012
Accepted: Sep 27, 2012
Published online: Sep 29, 2012
Published in print: Jun 1, 2013
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