ORIGINAL RESEARCH
Uncertainty of Forecast and Control of Activated Sludge Sedimentation Capacity
 
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1
Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, Kielce, Poland
2
Systems Research Institute PAN, Warsaw, Poland
3
University of Agriculture in Cracow, Department of Sanitary Engineering and Water Management, Krakow, Poland
4
Warsaw University of Life Sciences, University Faculty of Civil and Environmental Engineering, Warsaw, Poland
Online publication date: 2020-01-08
Publication date: 2020-02-13
Submission date: 2019-01-11
Final revision date: 2019-04-11
Acceptance date: 2019-04-24
 
Pol. J. Environ. Stud. 2020;29(2):1879–1887
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ABSTRACT
Our paper presents an analysis of the effect of data selection uncertainty for the teaching and testing sets in the black box model (multilayer perceptron type of artificial neural networks) using the bootstrap method on the accuracy of forecast and control of activated sludge sedimentation (SE) and the sludge volume index (SVI). The calculations show that sludge sedimentation, and hence also the sludge volume index, can be predicted based on the wastewater quality indicators and biological reactor operating parameters. The presented analyses also confirmed the significant influence of the neural network model structure on the uncertainty of estimating biological reactor operating parameters (mixed liquor suspended solids, concentration of oxygen) which, in practical considerations, leads to problems of continuous control of the sludge sedimentation capacity.
eISSN:2083-5906
ISSN:1230-1485