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Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge †
by Magdalena Piłat-Rożek
1 [ORCID] , Marcin Dziadosz
1 [ORCID] , Dariusz Majerek
1 [ORCID] , Katarzyna Jaromin-Gleń
2 [ORCID] , Bartosz Szeląg
3 [ORCID] , Łukasz Guz
4, Adam Piotrowicz
4 [ORCID] and Grzegorz Łagód
4,* [ORCID]
1
Faculty of Mathematics and Information Technology, Lublin University of Technology, 20-618 Lublin, Poland
2
Institute of Agrophysics, Polish Academy of Sciences, 20-290 Lublin, Poland
3
Institute of Environmental Engineering, Warsaw University of Life Sciences—SGGW, 02-797 Warsaw, Poland
4
Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in “Rapid on-line method of wastewater parameters estimation by electronic nose for control and operating wastewater treatment plants toward Green Deal implementation” presented at the 2nd International Conference on Strategies toward Green Deal Implementation—Water, Raw Materials and Energy (ICGreenDeal2021), Cracow, Poland, 8–10 December 2021.
Sensors 2023, 23(20), 8578; https://doi.org/10.3390/s23208578
Received: 31 August 2023 / Revised: 6 October 2023 / Accepted: 16 October 2023 / Published: 19 October 2023
(This article belongs to the Section Chemical Sensors)
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Abstract
Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a “gas fingerprint”, which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.
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