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. 2020 Mar 30;20(7):1941.
doi: 10.3390/s20071941.

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning

Bartosz Szeląg  1 , Jakub Drewnowski  2 , Grzegorz Łagód  3 , Dariusz Majerek  4 , Ewa Dacewicz  5 , Francesco Fatone  6
Affiliations

Soft Sensor Application in Identification of the Activated Sludge Bulking Considering the Technological and Economical Aspects of Smart Systems Functioning

Bartosz Szeląg et al. Sensors (Basel). .

Abstract

The paper presented the methodology for the construction of a soft sensor used for activated sludge bulking identification. Devising such solutions fits within the current trends and development of a smart system and infrastructure within smart cities. In order to optimize the selection of the data-mining method depending on the data collected within a wastewater treatment plant (WWTP), a number of methods were considered, including: artificial neural networks, support vector machines, random forests, boosted trees, and logistic regression. The analysis conducted sought the combinations of independent variables for which the devised soft sensor is characterized with high accuracy and at a relatively low cost of determination. With the measurement results pertaining to the quantity and quality of wastewater as well as the temperature in the activated sludge chambers, a good fit can be achieved with the boosted trees method. In order to simplify the selection of an optimal method for the identification of activated sludge bulking depending on the model requirements and the data collected within the WWTP, an original system of weight estimation was proposed, enabling a reduction in the number of independent variables in a model-quantity and quality of wastewater, operational parameters, and the cost of conducting measurements.

Keywords: activated sludge bulking.; classification model; data mining; smart systems and infrastructure; soft sensor; wastewater treatment plant.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A schematic of the soft sensor system for selecting a data mining method for simulating sludge bulking.
Figure 2
Figure 2
Matrix of the cases of interest and the weight factor values Wgq. where the following relationships are satisfied: λ = f(f1, f2, f3 …, fg) = f(λ), δ = f(z1, z2, z3 …, zq) = f(δ).
Figure 3
Figure 3
Matrices for the selection of methods for identification of sludge bulking.
Figure 4
Figure 4
The effect of variation of the MLSS and DO values on the sensitivity coefficient SMLSS,DO.
Figure 5
Figure 5
The effect of variation of the T and DO values on the sensitivity coefficient ST,DO.
Figure 6
Figure 6
The effect of variation of the MLSS and TN values on the sensitivity coefficient SMLSS,TN.
Figure 7
Figure 7
The effect of variation of the τ and TN values on the sensitivity coefficient Sτ, TN.
See this image and copyright information in PMC

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