University of Oulu

Tomperi, J., Leiviskä, K. (2017) Comparison of modelling accuracy with and without exploiting automated optical monitoring information in predicting the treated wastewater quality. Environmental Technology, 39 (11), 1442-1449. doi:10.1080/09593330.2017.1331267

Comparison of modelling accuracy with and without exploiting automated optical monitoring information in predicting the treated wastewater quality

Saved in:
Author: Tomperi, Jani1; Leiviskä, Kauko1
Organizations: 1Control Engineering, University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201901031214
Language: English
Published: Informa, 2017
Publish Date: 2018-05-27
Description:

Abstract

Traditionally the modelling in an activated sludge process has been based on solely the process measurements, but as the interest to optically monitor wastewater samples to characterize the floc morphology has increased, in the recent years the results of image analyses have been more frequently utilized to predict the characteristics of wastewater. This study shows that the traditional process measurements or the automated optical monitoring variables by themselves are not capable of developing the best predictive models for the treated wastewater quality in a full-scale wastewater treatment plant, but utilizing these variables together the optimal models, which show the level and changes in the treated wastewater quality, are achieved. By this early warning, process operation can be optimized to avoid environmental damages and economic losses. The study also shows that specific optical monitoring variables are important in modelling a certain quality parameter, regardless of the other input variables available.

see all

Series: Environmental technology
ISSN: 0959-3330
ISSN-E: 1479-487X
ISSN-L: 0959-3330
Volume: 39
Issue: 11
Pages: 1442 - 1449
DOI: 10.1080/09593330.2017.1331267
OADOI: https://oadoi.org/10.1080/09593330.2017.1331267
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
218 Environmental engineering
Subjects:
Copyright information: © 2017 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis in Environmental Technology on 27 May 2017, available online: https://doi.org/10.1080/09593330.2017.1331267