• Journal of Energy Engineering
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• v.25 no.3
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• pp.27-33
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• 2016

The heat recovery system that was applied in this study, is the energy-saving type that can produce the maximum cooling capacity less power in use. In order to have a more precise control function the temperature and humidity of the constant temperature and humidity machine, control algorithm is applied to designed a fuzzy PID controller, and the outside air compensation device (air-cooled) demonstrated excellent ability to dehumidify the moisture, $-20^{\circ}C$ in winter. High efficiency and the low-noise type sirocco fan operate quitely and designed to fit the bottom-up and top-down in accordance with the characteristics of equipment. as a result of experiment data, the conversion efficiency is 95% or more, power recovery time is within 5sec, stop delay time is within 30sec, pump down time is 10sec, pump delay time is 5sec, heating delay time is 5sec, temperature deviation is ${\pm}2^{\circ}C$ (cooling deviation: $2^{\circ}C$, Heating deviation : $2^{\circ}C$), humidity deviation is a ${\pm}5%$ (humidification deviation 3.0%, dehumidification deviation 3.0%). Recently, ubiquitous technology is important. so, the constant temperature and humidity machine designed to be able to remotely control to via the mobile phone, and more scalable to support MMI software and automatic interface. Further, the life of the parts and equipment is extended by the failure.

• Journal of Bio-Environment Control
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• v.11 no.4
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• pp.151-156
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• 2002

Root zone cooling, such as soil or nutrient solution cooling, is less expensive than air cooling in the whole greenhouse and is effective in promoting root activity, improving water absorption rate, decreasing plant temperature, and reducing high temperature stress. The heat transfer of a soil cooling system in a plastic greenhouse was analyzed to estimate cooling loads. The thermal conductivity of soil, calculated by measured heat fluxes in the soil, showed the positive correlation with the soil water content. It ranged from 0.83 to 0.96 W.m$^{[-10]}$ .$^{\circ}C$$^{[-10]}$ at 19 to 36％ of soil water contents. As the indoor solar radiation increased, the temperature difference between soil surface and indoor air linearly increased. At 300 to 800 W.m$^{-2}$ of indoor solar radiations, the soil surface temperature rose from 3.5 to 7.$0^{\circ}C$ in bare ground and 1.0 to 2.5$^{\circ}C$ under the canopy. Cooling loads in the root zone soil were estimated with solar radiation, soil water content, and temperature difference between air and soil. At 300 to 600 W.m$^{-2}$ of indoor solar radiations and 20 to 40％ of soil water contents,46 to 59 W.m$^{-2}$ of soil cooling loads are required to maintain the temperature difference of 1$0^{\circ}C$ between indoor air and root zone soil.

• Clean Technology
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• v.24 no.1
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• pp.55-62
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• 2018

The adsorbent used in water-adsorption cooling system utilizing low-temperature heat of below $90^{\circ}C$ is required to exhibit high water uptake capacity at a relative humidity ($P/P_0$) between 0.1 and 0.3. Mesoporous silica (MCM-41) and MOF(MIL-101) exhibit quite large water adsorption capacity under saturated water vapor at $35^{\circ}C$. However, these adsorbents show small water adsorption capacity ($0.027{g_{water}\;g_{ads}}^{-1}$, $0.074{g_{water}\;g_{ads}}^{-1}$, respectively) in the relative humidity ($P/P_0$) range of 0.1 to 0.3. In this study, the surface properties of mesoporous silica and MOF were modified by simple methods to develop an adsorbent having a higher water uptake than the conventional water adsorbents at a relative humidity ($P/P_0$) of 0.1 ~ 0.3. In the case of mesoporous silica (MCM-41) exhibiting mainly water adsorption at $P/P_0=0.5{\sim}0.7$, aluminum species was functionalized on the mesopore walls and then cations existing near the aluminum were exchanged with various cations (e.g., $Na^+$, ${NH_4}^+$, and $(C_2H_5)_4N^+$). In addition, 20 wt% (to total weight of the composites) of hygroscopic inorganic salt ($CaCl_2$) was impregnated on the MCM-41. In the case of the MIL-101 (MOF), 20 wt% of hygroscopic inorganic salt ($CaCl_2$) was impregnated on the MIL-101. The MCM-41 which was ion-exchanged with various cations has main adsorption branch around 0.5 of $P/P_0$ which was slightly shifted with low-pressure direction in comparison with pristine MCM-41. However, tiny increases were observed on the adsorption in the range of $P/P_0$ between 0.1 and 0.3. After salt impregnation on the MCM-41, the adsorption capacity under $P/P_0=0.1{\sim}0.3$ at $35^{\circ}C$ was increased from $0.027{g_{water}\;g_{ads}}^{-1}$ to $0.152{g_{water}\;g_{ads}}^{-1}$. In the case of MIL-101, the amount of water adsorption at $35^{\circ}C$ under $P/P_0=0.1{\sim}0.3$ was increased from $0.074{g_{water}\;g_{ads}}^{-1}$ to $0.330{g_{water}\;g_{ads}}^{-1}$ after the salt impregnation.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.