4.6 Article

System Identification of Conveyor Belt Microwave Drying Process of Polymer Foams Using Electrical Capacitance Tomography

Journal

SENSORS
Volume 21, Issue 21, Pages -

Publisher

MDPI
DOI: 10.3390/s21217170

Keywords

microwave drying; modeling; system identification; industrial tomography; electrical capacitance tomography

Funding

  1. European Union [764902]
  2. Academy of Finland [312344, 321761]
  3. Academy of Finland (AKA) [321761, 312344, 312344, 321761] Funding Source: Academy of Finland (AKA)

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The microwave drying process is widely used in industry, with a focus on drying polymer foams for sealings in construction. A state-space model is developed using system identification tools and electrical capacitance tomography (ECT) sensor for moisture measurement, allowing for accurate controller design. Multiple experiments validate the model's accuracy in controlling the microwave drying process.
The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process.

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