1.4.3 Concepts for accurate electrical conductivity measurement of liquids in industrial process analytics
- 20. GMA/ITG-Fachtagung Sensoren und Messsysteme 2019
2019-06-25 - 2019-06-26
- 1.4 Prozessautomatisierung und Industrie 4.0
- M. Vogt - KROHNE Messtechnik GmbH, Duisburg (Deutschland), S. Hidalgo, T. Musch - Ruhr Universität Bochum (Deutschland), M. Mallach, T. Lange, J. Förster - KROHNE Innovation GmbH, Duisburg (Deutschland)
- 105 - 112
The electrical conductivity of liquids is of interest in many industrial processes, for example in water desalination, cleaning, mixing of different liquids, analysis of acids and bases, etc. In this contribution, specifically two-electrode conductivity sensors are discussed. A problem with this type of sensor is that a double layer (DL) is given at the interfaces between each of the electrodes and the liquid, resulting in varying and unpredictable diffusion capacitances. For this reason, the electrical resistance of the liquid is not directly accessible from measurements of the sensor input impedance at low frequencies. For accurate determination of the conductivity, the parasitic and disturbing diffusion capacitances, as well as the unknown capacitance of the liquid and the parasitic sensor capacitance have to be eliminated from the measured impedance. The approach in electrical impedance spectroscopy (EIS) is to measure over a very large frequency range for explicitly determining and eliminating the unknown network elements. The motivation behind the work presented here is to allow for precise conductivity measurement at only low frequencies in the range of some kHz. Very advantageously with this, low-cost and low-demanding sensor electronics can be utilized for the realization of according industrial sensors. An equivalent circuit network model of the sensor has been analyzed in detail for this purpose. Approaches for estimating the resistance of the liquid from low frequency measurements of the sensor input impedance are presented and discussed. Finally, the resulting systematic measurement error has been evaluated based on the network model and experimentally.