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Low Cost Microwave Sensor for Moisture Content Measurement in Paper Milling Industry

机译：用于造纸行业中水分含量测量的低成本微波传感器

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摘要

The interest about RF and microwave sensors for the measurement of dielectric properties of materials during industrial processes has been growing recently. Microwave sensors are particularly attractive for their ability of performing non-invasive measurements. An interesting field of application is the real-time moisture measurement during industrial processes. A smart active sensor for measuring the moisture content of felts used in the paper milling industry and more generally of sheet-like materials, is here proposed. The sensor consists of a cavity backed slot resonator interacting with the material under test through near fields. The design aspects of the sensor and the related measuring front-end are discussed with respect to the dielectric properties of the material, and to the resonator response. The parametric sensitivity of the measuring method with the distance between the sample and the sensor surface and the sample thickness is also analyzed. An inversion procedure based on an Artificial Neural Network (ANN) approach is proposed in order to determine the moisture content of the felts. Measurements on several reference felts, with different density, thickness, and moisture content levels, ranging from dry to waterlogged state, proved the effectiveness of the proposed sensor architecture and the ANN-based inversion procedure.
机译：近来，对于在工业过程中用于测量材料介电特性的RF和微波传感器的兴趣不断增长。微波传感器执行非侵入式测量的能力特别吸引人。一个有趣的应用领域是工业过程中的实时水分测量。在此提出一种用于测量在造纸工业中使用的毡的水分含量的智能主动传感器，并且更普遍地用于片状材料。该传感器由一个腔背隙缝隙谐振器组成，它通过近场与被测材料相互作用。关于材料的介电特性以及谐振器响应，讨论了传感器和相关测量前端的设计方面。还分析了测量方法的参数灵敏度以及样品与传感器表面之间的距离以及样品厚度。为了确定毛毯的水分含量，提出了一种基于人工神经网络（ANN）方法的反演程序。从干燥状态到浸水状态，对几种具有不同密度，厚度和水分含量的参考毡进行了测量，证明了所提出的传感器架构和基于ANN的反演程序的有效性。

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