4.7 Article

Relative humidity sensor compensation for a portable residential refrigeration dehumidifier

Journal

CASE STUDIES IN THERMAL ENGINEERING
Volume 35, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.csite.2022.102096

Keywords

Ambient temperature; Multiple linear regression; Relative humidity; Residential portable refrigeration dehumidifier; Sensor compensation

Categories

Funding

  1. Ministry of Science and Technology of the Republic of China (Taiwan)
  2. MOST [109-2222-E-003-003-MY3, \S1HCIFS01\DEMData\17413\MYFILES\ELSEVIER \CSITE\00102096\S-CEEDITING\gs1]

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Households in humid climates commonly use portable refrigeration dehumidifiers, but internal heat can lead to RH measurement deviations. This study proposes solutions using an external temperature sensor and multiple linear regression algorithm, which significantly reduce the deviations.
Households living in humid climates commonly utilize a portable refrigeration dehumidifier to control ambient relative humidity (RH). However, the built-in sensor could potentially be heated by the waste heat released from the surrounding components inside the dehumidifier, resulting in a deviation in RH measurement that leads to improper operation. In this study, such RH deviations are confirmed primarily due to the temperature difference between the ambient and internal space of the dehumidifier, which makes placing an external temperature sensor a straightforward solution. Additionally, a multiple linear regression algorithm is proposed to compensate for the RH readings. Experiment results indicate that within the typical ambient temperature and RH range (22-26 degrees C and 40-70%, respectively), the deviation between ambient and measured RH can range from -2.6% (at 22 degrees C, 40%RH) to -9.2% (at 26 degrees C, 70%RH). After using the proposed multiple linear regression compensation, the deviation is reduced to a range of +0.5% (at 22 degrees C, 70%RH) to -0.33% (at 26 degrees C, 55%RH), showing a satisfying 94% deviation reduction on average. Hence, the RH deviations can be eliminated efficiently by installing an external temperature sensor or using the proposed multiple linear regression compensation. The former is more generally applicable, while the latter seems more cost-effective.

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