at Google ScholarTitle data
Meiler, Valentin ; Pfeiffer, Jens ; Bifano, Luca ; Kandlbinder-Paret, Christoph ; Fischerauer, Gerhard:
Approaches to detect microplastics in water using electrical impedance measurements and support vector machines.
In: IEEE Sensors Journal.
Vol. 23
(2023)
Issue 5
.
- pp. 4863-4872.
ISSN 1530-437X
DOI: https://doi.org/10.1109/JSEN.2023.3236375
Abstract in another language
We propose electrical impedance spectroscopy enhanced by machine learning, in particular support vector machines, as a non-invasive, in-situ method for detecting microplastics in water and as an alternative to slow, expensive laboratory measurements. The primary measurand is the complex-valued electrical impedance of a water-filled measuring cell. We carried out stationary measurements on numerous water samples contaminated with different plastic concentrations in a cylindrical measuring cell and in the frequency range from 20 Hz to 2 MHz. The effects of various influence quantities, such as the concentration of organic material (1.0 %, 3.0 %, 5.0 %) or the salinity of water (0.5 %, 1.0 %, 3.5 %), were also investigated. Measurements at 2 MHz with water flows carrying microplastic particles of different materials and sizes served to investigate the dynamic capabilities of the measurement method. The impedance spectra (stationary measurements) or the measured impedances (dynamic measurements) were then evaluated by SVM. The classification task consisted of distinguishing different plastic materials and particle sizes. In the stationary case, the application of the SVM resulted in assignment accuracies of over 98 %. In the dynamic case, the classification accuracies exceeded 91 % for the mere classification of particle sizes and 85 % for the classification of plastic particles by size and material.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | Electrical impedance spectroscopy (EIS); machine learning; support vector machines (SVM); microplastics; online monitoring; water flow |
| Institutions of the University: | Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology > Chair Measurement and Control Technology - Univ.-Prof. Dr.-Ing. Gerhard Fischerauer Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Chair Measurement and Control Technology |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 02 Mar 2023 11:30 |
| Last Modified: | 09 Aug 2023 09:05 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/73549 |