Simulation of dew point temperature in different time scales based on grasshopper algorithm optimized extreme gradient boosting

2022 ◽  
pp. 127452
Author(s):  
Jianhua Dong ◽  
Wenzhi Zeng ◽  
Guoqing Lei ◽  
Lifeng Wu ◽  
Haorui Chen ◽  
...  
Keyword(s):  
Time Scales ◽  
Dew Point ◽  
Gradient Boosting ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Extreme Gradient Boosting ◽  
Different Time Scales

scholarly journals Improving efficiency and lowering operating costs of evaporative cooling

2021 ◽  
Vol 338 ◽  
pp. 01027
Author(s):  
Jan Taler ◽  
Bartosz Jagieła ◽  
Magdalena Jaremkiewicz
Keyword(s):  
Electricity Consumption ◽  
Open Circuit ◽  
Dew Point ◽  
Operating Costs ◽  
Water Evaporation ◽  
Total Water ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Adiabatic Cooling ◽  
Positive Effect

Cooling towers, or so-called evaporation towers, use the natural effect of water evaporation to dissipate heat in industrial and comfort installations. Water, until it changes its state of aggregation, from liquid to gas, consumes energy (2.257 kJ/kg). By consuming this energy, it lowers the air temperature to the wet-bulb temperature, thanks to which the medium can be cooled below the ambient temperature. Evaporative solutions are characterized by continuous water evaporation (approx. 1.5% of the total water flow) and low electricity consumption (high EER). Evaporative (adiabatic) cooling also has a positive effect on the reduction of electricity consumption of cooled machines. Lowering the relative humidity (RH) by approx. 2% lowers the wet-bulb temperature by approx. 0.5°C, which increases the efficiency of the tower, operating in an open circuit, expressed in kW, by approx. 5%, while reducing water consumption and treatment costs. The use of the M-Cycle (Maisotsenko cycle) to lower the temperature of the wet thermometer to the dew point temperature will reduce operating costs and increase the efficiency of cooled machines.

scholarly journals Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Sultan Noman Qasem ◽  
Saeed Samadianfard ◽  
Hamed Sadri Nahand ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
M5 Model Tree ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters

In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (Vp), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96°, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, Vp, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.

Heat and Mass Transfer in Double-Filmwise Heat Exchanger for Industrial Food Processing Applications

2011 ◽  
Author(s):  
Helen Skop ◽  
James Pezzuto ◽  
Valeriy G. Oleynikov-White ◽  
John F. Cavallo ◽  
Robert Fesjian
Keyword(s):  
Water Vapor ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Cost Effective ◽  
Dew Point ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Gas Cooling ◽  
Stack Gas

The baking industry is considered as one of the major energy consuming food industries in North America. More than 40% of bakery fuel consumption is used to evaporate water in the processes [1]. In addition to the baking process’ vapor the oven stack gas contains water vapor from combustion products. Overall the content of water vapor in the typical oven stack gas is about 20% by volume. Most bakeries waste this vapor and its latent heat. Bakeries’ ovens have wide diversity in power and design. Off-the-shelve heat exchangers are not considered as cost effective equipment for stack gas cooling below gas’ dew point temperature. At typical oven stack gas composition water vapor condensation begins to condense at about 72° C. Not using the latent heat of stack water vapor and the heat from gas cooling from dew point temperature to ambient temperature results in low effectiveness of waste heat recovery. Mainly the effect from the recovery of stack gas cooling prior to condensation is considered as non cost effective and waste heat recovery is neglected.

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