August 21, 2024 Dataset Restricted Access

de Assis Dias, Felipe; Kryk, Holger; Schleicher, Eckhard; Nurjahan, Tanzila; Pelz, Philipp; Bart, Hans-Jörg; von Harbou, Erik; Scholl, Stephan; Jasch, Katharina; Hampel, Uwe

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    <subfield code="a">Data publication: An approach for in situ fouling monitoring in heat exchangers using electrical impedance spectroscopy</subfield>
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    <subfield code="a">&lt;p&gt;The dataset presented in this study is focused on the in situ monitoring of organic fouling in a plate heat exchanger using electrical impedance spectroscopy (EIS). The primary objective was to accurately determine the thickness of fouling layers that develop over time during heat exchanger operation. The experiments were conducted using an impedance analyzer (Sciospec ISX-3) configured in a four-terminal setup with two electrodes. The counter and reference electrodes were connected to a probe positioned at the top of the chamber, while the working and sensing electrodes were connected to the heating plate of the heat exchanger. This configuration allowed for the precise measurement of impedance across the fouling layer. The amplitude of the excitation signal was set to 1 V (rms). This value was chosen to maintain linearity at high frequencies and to ensure a high signal-to-noise ratio (SNR). The impedance spectra were recorded across a frequency range of 10 Hz to 2 MHz. The spectrum included 50 measurement points that were logarithmically spaced within this range to capture detailed impedance characteristics across different frequencies. Impedance data were sampled at a rate of 45 mHz, and measurements were collected over a duration of approximately 600 minutes, allowing for continuous monitoring of the fouling development process. The raw data consists of impedance measurements, reflecting changes in the electrical properties of the fouling layer as it accumulates on the heat exchanger surfaces. The data points in the spectrum provide insights into the relationship between fouling thickness and impedance across various frequencies, which is critical for developing models to predict fouling behavior in heat exchangers.This dataset is valuable for researchers and engineers interested in non-invasive fouling monitoring techniques, offering a foundation for improving heat exchanger efficiency through real-time fouling detection and characterization.&lt;/p&gt;</subfield>
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