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지준화,오민,김시문,김미영,이중원,김의식 한국수소및신에너지학회 2011 한국수소 및 신에너지학회논문집 Vol.22 No.3
Mathematical models for various steps in coal gasification reactions were developed and applied to investigate the effects of operation parameters on dynamic behavior of gasification process. Chemical reactions considered in these models were pyrolysis, volatile combustion, water shift reaction, steam-methane reformation,and char gasification. Kinetics of heterogeneous reactions between char and gaseous agents was based on Random pore model. Momentum balance and Stokes' law were used to estimate the residence time of solid particles (char) in an up-flow reactor. The effects of operation parameters on syngas composition, reaction temperature, carbon conversion were verified. Parameters considered here for this purpose were O_2-to-coal mass ratio, pressure of reactor, composition of coal, diameter of char particle. On the basis of these parametric studies some quantitative parameter-response relationships were established from both dynamic and steady-state point of view. Without depending on steady state approximation, the present model can describe both transient and long-time limit behavior of the gasification system and accordingly serve as a proto-type dynamic simulator of coal gasification process. Incorporation of heat transfer through heterogenous boundaries, slag formation and steam generation is under progress and additional refinement of mathematical models to reflect the actual design of commercial gasifiers will be made in the near futureK.
기공 구조와 반응 부산물의 영향을 고려한 촤의 가스화 모델
지준화 한국수소및신에너지학회 2010 한국수소 및 신에너지학회논문집 Vol.21 No.4
A new gasification model for coal char was developed considering the effects of pore structure and solid reaction product (ash) and compared with conventional models. Among various parameters reflecting microscopic pore structure, initial pore surface per unit volume of char was found to have the largest effect on carbon conversions. Reaction studies showed that the proposed model can predict carbon conversion more accurately over a broader range of reaction degree compared to the conventional models. Therefore the model proposed in this study would be useful for the design of pilot or commercial scale gasifiers.
분류층 가스화기에서의 고체 입자-슬래그 간 상호 작용에 대한 모델링
지준화,김기태,김성철,정재화,주지선,김의식 한국수소및신에너지학회 2011 한국수소 및 신에너지학회논문집 Vol.22 No.5
Mathematical models for char-slag interaction and near-wall particle segregation developed by Montagnaro et. al. were applied to predict various aspects of coal gasification in an up-flow entrained gasifier of commercial scale. For this purpose, some computer simulations were performed using gPROMS as the numerical solver. Typical design parameters and operating conditions of the commercial gasifiers were used as input values for the simulation. Development of a densely dispersed phase of solid carbon was found to have a critical effect on both carbon conversion and ash flow behavior. In general, such a slow-moving phase was turned out to enhance carbon conversion by lengthening the residence time of char or soot particles. Furthermore,it was also found that guiding the transfer of char or soot into the closer part of the wall to coal burner is favorable in terms of gasification efficiency and vitrified ash collection. Finally, to a certain degree densely dispersed phase of carbon showed an yield-enhancing effect of syngas.
전산 모델링을 통한 모노리스 촉매형 메탄화 반응기의 성능 특성 연구
지준화,김성철,홍진표 한국수소및신에너지학회 2014 한국수소 및 신에너지학회논문집 Vol.25 No.4
Simulation studies on catalytic methanation reaction in externally cooled tubular reactor filled withmonolithic catalysts were carried out using a general purpose modelling tool gPROMS®. We investigated the effectsof operating parameters such as gas space velocity, temperature and pressure of feeding gas on temperaturedistribution inside the reactor, overall CO conversion, and chemical composition of product gas. In general, performanceof methanation reaction is favored under low temperature and high pressure for a wide range of their values. However, methane production becomes negligible at temperatures below 573K when the reactor temperature is nothigh enough to ignite methanation reaction. Capacity enhancement of the reactor by increasing gas space velocityand/or gas inlet pressure resulted no significant reduction in reactor performance and heat transfer property of catalyst.
지준화 한국수소및신에너지학회 2015 한국수소 및 신에너지학회논문집 Vol.26 No.3
One-dimensional packed bed reactor model accounting for interfacial and intra-particle gradients was developed and based on it numerical analyses were performed to investigate the dynamic behavior of a commercial scale methanation reactor. Methanation reaction was almost complete near the reactor inlet and gases with equilibrated composition were discharged from the reactor. Both the intra-particle temperature gradient and differential surface temperature rise were found to be severe near the reactor inlet. To reduce the possible degradation or fracture of catalyst particles and prevent local overheating on the catalyst, addition of inert material can be an effective way.
Two-stream Convolutional Long- and Short-term Memory 모델의 2001-2021년 9월 북극 해빙 예측 성능 평가
지준화,Chi, Junhwa 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.