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A data-mining approach to predict influent quality

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Abstract

In wastewater treatment plants, predicting influent water quality is important for energy management. The influent water quality is measured by metrics such as carbonaceous biochemical oxygen demand (CBOD), potential of hydrogen, and total suspended solid. In this paper, a data-driven approach for time-ahead prediction of CBOD is presented. Due to limitations in the industrial data acquisition system, CBOD is not recorded at regular time intervals, which causes gaps in the time–series data. Numerous experiments have been performed to approximate the functional relationship between the input and output parameters and thereby fill in the missing CBOD data. Models incorporating seasonality effects are investigated. Four data-mining algorithms—multilayered perceptron, classification and regression tree, multivariate adaptive regression spline, and random forest—are employed to construct prediction models with the maximum prediction horizon of 5 days.

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Acknowledgments

This research has been supported by the Iowa Energy Center, grant no. 08-01

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, The University of Iowa, 3131 Seamans Center, Iowa City, IA, 52242, USA

    Andrew Kusiak, Anoop Verma & Xiupeng Wei

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  1. Andrew Kusiak
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  2. Anoop Verma
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  3. Xiupeng Wei
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Correspondence to Andrew Kusiak.

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Kusiak, A., Verma, A. & Wei, X. A data-mining approach to predict influent quality. Environ Monit Assess 185, 2197–2210 (2013). https://doi.org/10.1007/s10661-012-2701-2

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