Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System
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 acquisiti...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/20/6919 |
| _version_ | 1797513156446126080 |
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| author | Rahul Yadav Adel Omrani Guido Link Marko Vauhkonen Timo Lähivaara |
| author_facet | Rahul Yadav Adel Omrani Guido Link Marko Vauhkonen Timo Lähivaara |
| author_sort | Rahul Yadav |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:12:40Z |
| format | Article |
| id | doaj.art-b907d87c1f344fa58063953e200a59e9 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:12:40Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b907d87c1f344fa58063953e200a59e92023-11-22T19:59:34ZengMDPI AGSensors1424-82202021-10-012120691910.3390/s21206919Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying SystemRahul Yadav0Adel Omrani1Guido Link2Marko Vauhkonen3Timo Lähivaara4Department of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, FinlandInstitute for Pulsed Power and Microwave Technology, Karlsruhe Institute of Technology (KIT), 76133 Karlsruhe, GermanyInstitute for Pulsed Power and Microwave Technology, Karlsruhe Institute of Technology (KIT), 76133 Karlsruhe, GermanyDepartment of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, FinlandDepartment of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, FinlandThe 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.https://www.mdpi.com/1424-8220/21/20/6919microwave dryingmoisture content distributionmicrowave tomographyinverse problemsneural networks |
| spellingShingle | Rahul Yadav Adel Omrani Guido Link Marko Vauhkonen Timo Lähivaara Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System Sensors microwave drying moisture content distribution microwave tomography inverse problems neural networks |
| title | Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System |
| title_full | Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System |
| title_fullStr | Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System |
| title_full_unstemmed | Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System |
| title_short | Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System |
| title_sort | microwave tomography using neural networks for its application in an industrial microwave drying system |
| topic | microwave drying moisture content distribution microwave tomography inverse problems neural networks |
| url | https://www.mdpi.com/1424-8220/21/20/6919 |
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