Abstract
Microwave tomography (MWT) based control is a novel idea in industrial heating systems for drying polymer foam. In this work, an X-band MWT module is designed and developed using a fixed antenna array configuration and integrated with the HEPHAISTOS industrial heating system. A decomposition of the time-reversal operator (DORT) algorithm with a proper Green’s function of multilayered media is utilized to localize the moisture location. The derived Green’s function can be applied to the media with low or high contrast layers. It is shown that the time-reversal imaging (TRI) with the proposed Green’s function can be applied to the multilayered media with a moderately rough surface. Moreover, a single frequency TRI is proposed to decrease the measurement time. Numerical results for different moisture scenarios are presented to demonstrate the efficacy of the proposed method. The developed method is then tested on the experimental data for different moisture scenarios from our developed MWT experimental prototype. Image reconstruction results show promising capabilities of the TRI algorithm in estimating the moisture location in the polymer foam.
Highlights
Drying by microwaves has been widely used, especially in industry, for different applications and purposes
For testing and validating the proposed algorithm, we have focused first on a specific static case where it is applied to obtain the location of moisture in polymer foam
The developed time-reversal imaging (TRI) was applied to both low- and high-contrast media when a low-dielectric layer is situated above a perfect electric conductor (PEC) layer
Summary
Drying by microwaves has been widely used, especially in industry, for different applications and purposes. In microwave drying applications, providing sufficient uniformity of heating distribution is an imperative task especially in industrial-scale production Not meeting this condition can lead to hot-spot formation and thermal runaway [1]. For image reconstruction which can support little data from a small number of antennas, neural networks [17,18,19] have shown promising capabilities that may estimate the moisture in real time (
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