• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11c
• /
• pp.639-646
• /
• 2000

Hot air heater with light oil combustion is the most common heater for greenhouse heating in the winter season in Korea. However, since the heat efficiency of the heater is about 80%, considerable unused heat in the form of exhaust gas heat discharges to atmosphere. In order to capture this exhaust gas heat a heat recovery system for plant bed heating in the greenhouse was built and tested in the hot air heating system of greenhouse. The system consists of a heat exchanger made of copper pipes, ${\phi}\;12.7{\times}0.7t$ located inside the rectangular column of $330{\times}330{\times}900mm$, a water circulation pump, circulation plastic pipe and a water tame The total heat exchanger area is $1.5m^2$, calculated considering the heat exchange amount between flue gas and water circulated in the copper pipes. The system was attached to the exhaust gas path. The heat recovery system was designed as to even recapture the latent heat of flue gas when exposing to low temperature water in the heat exchanger. According to performance test it can recover 45,200 to 51,000kJ/hr depending on the water circulation rates of 330 to $690{\ell}$/hr from the waste heat discharged. The exhaust gas temperature left from the heat exchanger dropped to $100^{circ}C$ from $270^{circ}C$ by the heat exchange between the water and the flue gas, while water gained the difference and temperature increased to $38^{circ}C$ from $21^{circ}C$ at the water flow rate of $690{\ell}$/hr. And, the condensed water amount varies from 16 to $43m{\ell}$ at the same water circulation rates. This condensing heat recovery system can reduce boiler fuel consumption amount in a day by 34% according to the feasibility study of the actual mimitomato greenhouse. No combustion load was observed in the hot air heater.

• Journal of the Korea Convergence Society
• /
• v.11 no.8
• /
• pp.173-182
• /
• 2020

The government continuously improves the RPS system to expand the supply of renewable energy, but there has been criticism that more environmental aspects should be considered to reduce GHG emission. REC weights are differentiated according to renewable energy sources. Greenhouse gas emission is one of the decisive factors, and its value is set by experts' opinion. This study assigns LCA to get accurate value of GHG emission. The LCA calculates emitted greenhouse gases from entire process of fuel production, transportation, power plant construction, operation, and decommission. This study suggests a method to change the greenhouse gas reduction effect from the existing qualitative method to the quantitative method and evaluates them. As a result, the evaluation score is changed, but the tier interval is so large that it does not affect the REC weight. Therefore, this study suggests the way that directly reflect the greenhouse gas reduction effect in the REC weight.

• Journal of Bio-Environment Control
• /
• v.23 no.3
• /
• pp.181-191
• /
• 2014

Modern commercial greenhouse requires the use of advanced climate control system to improve crop production and to reduce energy consumption. As an alternative to classical sensor-based control method, this paper introduces a model-based control method that consists of two models: the predictive model and the evaluation model. As a first step, this paper presents straightforward models to predict the effect of natural ventilation in a greenhouse according to meteorological factors, such as outdoor air temperature, soil temperature, solar radiation and mean wind speed, and structural factor, opening rate of roof ventilators. A multiple regression analysis was conducted to develop the predictive models on the basis of data obtained by computational fluid dynamics (CFD) simulations. The output of the models are air temperature drops due to ventilation at 9 sub-volumes in the greenhouse and individual volumetric ventilation rate through 6 roof ventilators, and showed a good agreement with the CFD-computed results. The resulting predictive models have an advantage of ensuring quick and reasonable predictions and thereby can be used as a part of a real-time model-based control system for a naturally ventilated greenhouse to predict the implications of alternative control operation.

• Journal of Advanced Navigation Technology
• /
• v.16 no.1
• /
• pp.89-95
• /
• 2012

This paper introduces domestic and international trends and researches related with U-Greenhouse systems. USN (Ubiquitous Sensor Networks), along with the development of networks as well as science and technology, is a new computing paradigm which is the convergence of user-oriented physical activity space and virtual space of electronics and computing and also provides services according to change in surrounding environment at anytime and anywhere. The U-Greenhouse system is to apply USN to agricultural production, logistics and distribution management which are relatively insufficient to utilize IT technology. Thus, applying u-IT technology to agriculture can reinforces international competitiveness of the agricultural sector through the effects such as cost cutting as a rise in output, logistics and distribution management.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2022.10a
• /
• pp.145-145
• /
• 2022

South Korea greenhouse gas emissions have increased year by year, resulting in a total emission of 727.6 million tons of CO₂ eq in 2018, a 2.5% increase compared to 2017. Among them, the agricultural sector emitted 21.2 million tons of CO₂ eq., accounting for 2.9% of the total. Among the greenhouse gases emitted from the agricultural sector, a particularly problematic is methane gas emitted from rice paddies. Methane is one of the important greenhouse gases with a global warming potential (GWP) that is about 21 times higher than that of carbon dioxide due to its high infrared absorption capacity despite its relatively short remaining atmospheric period. Since the pattern of methane generation varies depending on the rice variety and ecological type, research related to this is necessary for accurate emission calculation and development of reduction technology. Accordingly, a study was conducted to find out the changes in greenhouse gas emission according to rice varieties and ecology types. As for the rice eco-type cultivar, early maturing cultivar (Haedamssal) and medium-late rice cultivar (Saeilmi) were used. Haedamssal was transplanted on May 25 and June 25, and Saeilmi was transplanted on June 10 and June 25. The amount of methane generated according to the growing day showed a tendency to increase as the planting period was earlier. The difference between varieties was that Haedamssal showed higher methane production than Saeilmi. The total CH₄ flux in the saeilmi was 18.7 kg·h^-1(Jun 10 transplanting), 12.4 kg·h^-1(Jun 25 transplanting) during rice cultivation. Lower methane emission was observed in Saeilmi than in Haedam rice. In addition, the earlier the planting period, the higher the methane emission. This study is the result of the first year of research, and it is planned to investigate the amount of greenhouse gas emission between double cropping and single cropping using wheat cultivation after harvest for each ecological type.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• v.11 no.2
• /
• pp.266-269
• /
• 2006

• Journal of Ginseng Research
• /
• v.29 no.4
• /
• pp.182-184
• /
• 2005

A new cultural practice for commercial production of American ginseng by organically grown in a shaded greenhouse under controlled environment conditions has been developed. This new procedure will shorten the dormant period and reduce the cultivation period from 4 years in conventionally grown ginseng to 2 years.

• Transactions of the Korean hydrogen and new energy society
• /
• v.24 no.1
• /
• pp.12-19
• /
• 2013

A gasification process with pre-combustion $CO_2$ capture process, which converts coal into environment-friendly synthetic gas, might be promising option for sustainable energy conversion. In the coal gasification for power generation, coal is converted into $H_2$, CO and $CO_2$. To reduce the cost of $CO_2$ capture and to maximize hydrogen production, the removal of CO and the additional production of hydrogen might be needed. In this study, a 2l/min water gas shift system for a coal gasifier has been studied. To control the concentration of major components such as $H_2$, CO, and $CO_2$, MFCs were used in experimental apparatus. The gas concentration in these experiments was equal with syngas concentration from dry coal gasifiers ($H_2$: 25-35, CO: 60-65, $CO_2$: 5-15 vol%). The operation conditions of the WGS system were $200-400^{\circ}C$, 1-10bar. Steam/Carbon ratios were between 2.0 and 5.0. The commercial catalysts were used in the high temperature shift reactor and the low temperature shift reactor. As steam/carbon ratio increased, the conversion (1-$CO_{out}/CO_{in}$) increased from 93% to 97% at the condition of CO: 65, $H_2$: 30, $CO_2$: 5%. However the conversion decreased with increasing of gas flow and temperature. The gas concentration from LTS was $H_2$: 54.7-60.0, $CO_2$: 38.8-44.9, CO: 0.3-1%.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.581-584
• /
• 2019

Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.

• Journal of Climate Change Research
• /
• v.8 no.3
• /
• pp.213-220
• /
• 2017

The purpose of this study is to analyze effects of Greenhouse Gas (GHG) emission reduction in district energy business mainly based on Combined Heat and Power (CHP) plants. Firstly this paper compares the actual carbon intensity of power production between conventional power plants and district energy plants. To allocate the GHG from CHP plants, two of different methods which were Alternative Generation Method and Power Bonus Method, have been investigated. The carbon intensity of power production in district energy plants ($0.43tonCO_2e/MWh$) was relatively lower than conventional gas-fired power plants ($0.52tonCO_2e/MWh$). Secondly we assessed the cost effectiveness of reduction by district energy sector compared to the other means using TIMES model method. We find that GHG marginal abatement cost of 'expand CHP' scenario (-$134/ton$CO_2$) is even below than renewable energy scenario such as photovoltaic power generation ($87/ton$CO_2$). Finally the GHG emission reduction potential was reviewed on the projected GHG emission emitted when the same amount of energy produced in combination of conventional power plants and individual boilers as substitution of district energy. It showed there were 10.1~41.8% of GHG emission reduction potential in district energy compared to the combination of conventional power plants and individual boilers.