University of Oulu

Tomperi, J., Koivuranta, E., Leiviskä, K. (2017) Predicting the effluent quality of an industrial wastewater treatment plant by way of optical monitoring. Journal of Water Process Engineering, 16, 283-289. doi:10.1016/j.jwpe.2017.02.004

Predicting the effluent quality of an industrial wastewater treatment plant by way of optical monitoring

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Author: Tomperi, Jani1; Koivuranta, Elisa2; Leiviskä, Kauko1
Organizations: 1Control Engineering, University of Oulu. Oulu, Finland
2Fibre and Particle Engineering, University of Oulu. Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Elsevier, 2017
Publish Date: 2019-03-03


Wastewater samples taken from the aeration tank of a full-scale activated sludge plant were analyzed using an automatic optical monitoring device. Five variable selection methods were utilized to find the optimal subsets of input variables to develop predictive models for the important parameters of the wastewater treatment process efficiency and the quality of the effluent, including suspended solids, biochemical oxygen demand, chemical oxygen demand, total nitrogen and total phosphorus. The dependencies between the selected variables were also inspected. The study showed that the models based solely on the optical monitoring variables can be used to predict the level of the effluent quality parameters hours before the traditional sampling and analyses. Thus, predictive modelling based on the optical monitoring variables is a potential tool to be used assistance in a process control, keeping the process in a stable operating condition and avoiding environmental risks and economic losses.

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Series: Journal of water process engineering
ISSN: 2214-7144
ISSN-E: 2214-7144
ISSN-L: 2214-7144
Volume: 16
Pages: 283 - 289
DOI: 10.1016/j.jwpe.2017.02.004
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
218 Environmental engineering
222 Other engineering and technologies
Funding: This research was carried out as part of the Measurement, Monitoring and Environmental Efficiency Assessment (MMEA), the research programme of CLEEN Ltd. – Cluster for Energy and Environment.
Copyright information: © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license