Numerical analysis and experimental verification of the electrical impedance tomography method for monitoring of the packed-bed drying process

This paper presents a method for online determination the spatial distribution of the moisture content in material being dried. It might be essential for monitoring and optimal control of the drying processes. The proposed method utilizes electrical impedance tomography. As the exemplary material for experimental research the black chokeberry (Aronia melanocarpa) was used. The relationship between electrical impedance of chokeberry and its moisture content was determined for wide range of frequency (0.02200 kHz). Experimental studies of the spatial distribution of the moisture content was performed in the cylindrical vessel equipped with 8-electrode circumferentially arranged. Voltage signal from the electrodes was acquired simultaneously using high-speed data acquisition module. Due to high impedance of the chokeberries, exceeding 109 Ω for dried matter, extraordinary instrumentation for stimulus sourcing and current measurement was necessary to be applied. On the other hand, raw chokeberries are characterized by several orders of magnitude lower impedance (103104 Ω), especially for high frequency. Wide range of observed impedance was able to be measured owing to use of the voltage stimulation instead of the current stimulation. The image reconstruction problem was solved using iterative Gauss-Newton algorithm in the electrical impedance and diuse optical tomography software package. The obtained results has shown satisfactory ability to localize insuciently dried part of the material. It was proved, that strong dependence between moisture content in chokeberries and its impedance in whole frequency range provides more meaningful information than simple resistance or capacitance measurement. Prospect for the improvement of the spatial resolution of humidity imaging was also discussed.

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Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)