Abstract
The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from the MWT sensors and real-time image reconstruction of the process. Thus, a limited number of sensors are chosen for the MWT and are placed only on top of the polymer foam to enable fast data acquisition. For real-time estimation, we present a neural network-based reconstruction scheme to estimate moisture distribution in a polymer foam. Training data for the neural network is generated using a physics-based electromagnetic scattering model and a parametric model for moisture sample generation. Numerical data for different moisture scenarios are considered to validate and test the performance of the network. Further, the trained network performance is evaluated with data from our developed prototype of the MWT sensor array. The experimental results show that the network has good accuracy and generalization capabilities.
Highlights
Controlled/localised heating in industrial microwave oven [1,2] is paramount to address hot-spot formation and thermal runaway issues [3]
Integration of microwave tomography (MWT) imaging modality operating in X-band range [7] with the drying system is proposed to estimate the moisture content distribution in a polymer foam
The true test samples and estimated outputs from the convolutional neural network (CNN) for the low moisture and for the moderate moisture are shown in Figure 5 and Figure 5, respectively
Summary
During drying of a porous polymer foam, thermal runaway and hot-spot formation may occur [5,6] Such situations may lead to low-quality processing and may even damage the industrial unit in case a fire is kindled in the foam. Automatic online control of power sources (magnetrons) to obtain a selective heating rate at each stage of the drying process is one option to eliminate these problems. To apply such precise control of power sources, non-invasive in situ measurement of the unknown distribution of moisture, especially dominant wet-spots, inside the material is required. Preliminary work in this direction is reported in [8] by the authors
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