• Title/Summary/Keyword: water spray systems

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A Study on the Improvement of the Disaster Prevention and Control System for Underpasses by Analytic Hierarchy Process (계층분석법을 통한 지하차도 재해 예방 및 제어 시스템 개선 연구)

  • Kim, Phil Do;Kim, Kyoung Soo;Moon, Yoo Mi
    • Journal of the Society of Disaster Information
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    • v.16 no.4
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    • pp.734-746
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    • 2020
  • Purpose: Increase in the size and number of underpasses rises occurrence of disasters such as fire and flooding inundation in underpasses. In the occurrence of disasters, the underpasses are more vulnerable to expose of crucial disasters than the general roads due to they are built underground. Therefore, The purpose of this paper is to derive system improvement items to prevent and control disasters in underpasses. Method: A hierarchical model of disaster impact factors and alternatives was developed based on prior researches and expert advices on disaster analyses and impact factors in the underpasses. The developed model was employed for surveys of pairwise comparison, and rankings of improvement were determined by applying the AHP method. Result: With a consistency of the surveys, results of relative weights of evaluation criteria(traffic accidents, fire, flooding inundation) and alternatives(law, system/planning, maintenance/human factor/environment) shows that improvement of laws and system related to the fire disaster is a top priority to prevent and control disaster of the underpasses. Conclusion: From experts' point of view, strengthening laws and systems related to disater prevention facilities such as water spray facilities, external(ground) exit in relation to fire in underpasses showed that it is an alternative to prevent disasters and minimize damage to underpasses.

Development of Solid Self-nanoemulsifying Drug Delivery Systems of Ticagrelor Using Porous Carriers (다공성의 캐리어를 이용한 티카그렐러 함유 고형의 자가 나노유화 약물전달시스템 개발)

  • Choi, Hyung Joo;Kim, Kyeong Soo
    • Journal of Life Science
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    • v.31 no.5
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    • pp.502-510
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    • 2021
  • The objective of this study was to develop a novel ticagrelor-loaded self-nanoemulsifying drug delivery system with an enhanced solubility and dissolution rate. Numerous oils and surfactants were screened, then medium chain triglyceride (MCT) oil and the surfactants polyoxyethylene sorbitan monooleate (Tween 80) and Labrafil M1944CS were selected for the preparation of the ticagrelor-loaded self-nanoemulsifying drug delivery system. A pseudo-ternary phase diagram was constructed to detect the nanoemulsion region. Of the various formulations tested, the liquid SNEDDS, composed of MCT (oil), Tween 80 (surfactant), and Labrafil M1944CS (cosurfactant) at a weight ratio of 20/70/10 produced the smallest emulsion droplet size (around 20.56±0.70 nm). Then, particle size, polydispersity, and zeta potential were measured using drugs containing liquid SNEDDS. The selected ticagrelor-loaded liquid SNEDDS was spray-dried to convert it into a ticagrelor-loaded solid SNEDDS with a suitable inert carrier, such as silicon dioxide, calcium silicate, or magnesium aluminometasilicate. The solid SNEDDS was characterized by scanning electron microscopy, transmission electron microscopy, and in vitro dissolution studies. SEM, PXRD, and DSC results suggested that amorphous ticagrelor was present in the solid SNEDDS. Also, the solid SNEDDS significantly increased the dissolution rate of ticagrelor. In particular, the emulsion particle size and the polydispersity index of the solid SNEDDS using silicon dioxide (SS1) as a carrier was the smallest among the evaluated solid SNEDDS, and the flowability and compressibility result of the SS1 was the most suitable for the manufacturing of solid dosage forms. Therefore, solid SNEDDS using silicon dioxide (SS1) could be a potential nano-sized drug delivery system for the poorly water-soluble drug ticagrelor.

Analysis of Greenhouse Thermal Environment by Model Simulation (시뮬레이션 모형에 의한 온실의 열환경 분석)

  • 서원명;윤용철
    • Journal of Bio-Environment Control
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    • v.5 no.2
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    • pp.215-235
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    • 1996
  • The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad & fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH+7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad & fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).

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