An insight into the estimation of relative humidity of air using artificial intelligence schemes
- PDF / 6,264,471 Bytes
- 29 Pages / 439.37 x 666.142 pts Page_size
- 105 Downloads / 256 Views
An insight into the estimation of relative humidity of air using artificial intelligence schemes Mahdi Ghadiri1,2 · Azam Marjani3,4 · Samira Mohammadinia5 · Saeed Shirazian6 Received: 15 July 2020 / Accepted: 13 October 2020 © Springer Nature B.V. 2020
Abstract The present work suggested predicting models based on machine learning algorithms including the least square support vector machine (LSSVM), artificial neural network (ANN), and adaptive network-based fuzzy inference system (ANFIS) to calculate relative humidity as function of wet bulb depression and dry bulb temperature. These models are evaluated based on several statistical analyses between the real and determined data points. Outcomes from the suggested models expressed their high abilities to determine relative humidity for various ranges of dry bulb temperatures and also wet-bulb depression. According to the determined values of MRE and MSE were 0.933 and 0.134, 2.39 and 1, 1.291 and 0.193, 0.931 and 0.132 for the RBF-ANN, MLP-ANN, ANFIS, and LSSVM models, respectively. The aforementioned predictors have interesting value for the engineers and researchers who dealing with especial topics of air conditioning and wet cooling towers systems which measure the relative humidity. Keywords Wet-bulb depression · Relative humidity · ANFIS · Artificial neural network · LSSVM
* Azam Marjani [email protected] 1
Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam
2
The Faculty of Environment and Chemical Engineering, Duy Tan University, Da Nang 550000, Viet Nam
3
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
4
Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
5
Department of Chemical Engineering, Islamic Azad University, Mahshahr Branch, Mahshahr, Iran
6
Laboratory of Computational Modeling of Drugs, South Ural State University, 76 Lenin prospekt, 454080 Chelyabinsk, Russia
13
Vol.:(0123456789)
M. Ghadiri et al.
1 Introduction The main factors to control the quantity of moist air are the temperature and pressure. As the air temperature increases, the amount of water vapor is also increasing (Przybylak 2016; Vicente-Serrano et al. 2016; Lee and Wang 2018; Yang 2019). The widely applied parameter in practice for determining a characteristic of air is the dry-bulb temperature, which is known as the air temperature. Since the obtained air temperature by a thermometer is not function of the air humidity, it is named as dry bulb (Hasan 2010; Shallcross 2005). Conversely, the wet-bulb temperature obtained by a wet thermometer which is affected by the airflow. The rate of evaporative cooling that is a type of cooling with the capability of removing the moisture from a surface usually is measured by a thermometer. The main distinction of the wet bulb and dry bulb temperatures is their amount. Except at relative humidity equal to 1, all the time the quantity of the dry bulb temperature is more than temperatur
Data Loading...
