An Inexpensive, Stable, and Accurate Relative Humidity Measurement Method for Challenging Environments

Abstract

In this research, an improved psychrometer is developed to solve practical issues arising in the relative humidity measurement of challenging drying environments for meat manufacturing in agricultural and agri-food industries. The design in this research focused on the structure of the improved psychrometer, signal conversion, and calculation methods. The experimental results showed the effect of varying psychrometer structure on relative humidity measurement accuracy. An industrial application to dry-cured meat products demonstrated the effective performance of the improved psychrometer being used as a relative humidity measurement sensor in meat-drying rooms. In a drying environment for meat manufacturing, the achieved measurement accuracy for relative humidity using the improved psychrometer was ±0.6%. The system test results showed that the improved psychrometer can provide reliable and long-term stable relative humidity measurements with high accuracy in the drying system of meat products.

Measuring the effects of heated windows on thermal comfort

“…The principle of operation is an accurate method based on the simultaneous measurement of dry and wet temperatures in a given air velocity with two thermometers, one dry and one humidified with water. In this case, the relative humidity is determined from the difference between the two types of measured temperature (dry and wet), the so-called "psychrometric temperature difference" [10].…”

Current Trends in Intelligent Control Neural Networks for Thermal Processing (Foods): Systematic Literature Review

“…A new methodology was studied in [14] for thermostatic room temperature measurement used in food-meat industries. A study in [15] showed that an improved psychrometer was developed to solve practical problems in measuring the relative humidity of drying environments, which are challenging for meat production. Besides, research on the boiling point and specific heat of meat extract was done [16].…”

Relative Humidity Estimation: Machine Learning Approach–Random Forest-Based Prediction Model

“…Hence, a rigorous and robust estimation of ɸ has an immense importance from daily life to industrial life. Several estimation tools and models [4][5][6][7] have been presented to predict the moist air properties including relative humidity, wet bulb temperature, and dew point. Most of them are so complicated due to number of parameters involved and thus difficult for process engineers to apply and get appropriate results.…”