Application of CFD to Design Procedure of Ammonia Injection System in DeNOx Facilities in a Coal-Fired Power Plant (석탄화력 발전소 탈질설비의 암모니아 분사시스템 설계를 위한 CFD 기법 적용에 관한 연구)
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- Clean Technology
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- v.27 no.1
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- pp.61-68
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- 2021
Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.
Recently, many hangover cure products containing natural ingredients have been made available in the market that are effective for alcohol-related liver damage or for improved liver function. However, the cure for of liver damage or medication for improved liver function are different from hangover cure. Therefore, the efficacy hangover cure products needs to be verified. In this study, we investigated and compared the ameliorating effect of four commercially available hangover cure products on acute ethanol-induced hangover in Sprague - Dawley rats. The four samples were labeled as C, M, R, and S. The efficacy of the samples was evaluated based on the serum concentration and area under the curve (AUC) of blood ethanol and acetaldehyde concentrations to quantitatively assess the hangover cure effect. Ethanol administration to the rats significantly raised the serum alcohol and acetaldehyde levels. The Cmax reduction rates of ethanol for the samples C, M, R, and S were 5.9%, 3.1%, 8.4%, and 11.7%, and the AUC were 8.9%, 2.2%, 12.1%, and 19.6%, respectively, whereas the Cmax reduction rates of acetaldehyde were 14.2%, 15.2%, 28.2%, and 35.0%, and the AUC were 21.6%, 7.5%, 22.4%, and 29.9%, respectively. In conclusion, all samples showed a tendency to relieve hangover in the order of S, R, C, and M in terms of the ethanol concentration, but only sample S showed a statistically significant decrease in both Cmax and AUC for ethanol and acetaldehyde. These results suggest that an objective method for verifying the efficacy of hangover cure products is lacking.
This paper discusses the process and results of experimental research aimed at reducing Underwater Radiated Noise (URN) using air injection technology. Air Lubrication System (ALS) is an air injection technology mainly installed and operated to improve the propulsion efficiency of large commercial ship, such as LNGC. Recently, research institutes have been studying the potential of reducing URN using ALS. This paper performs an experiment as part of such research. The experiment was conducted in the Large Cavitation Tunnel (LCT), and the major devices applied in the experiment fall into two categories: ALS, which is directly applied to the model in use for LNGC and a modified air injection belt developed from the Masker-Air System (MAS), which is being developed to reduce URN of naval ships. The environmental conditions for the experiment mainly include the air injection flow rate and flow speed in the LCT. The flow rate was set to the actual air injection conditions of ALS, and the flow speed was adjusted to two different levels, considering the actual speeds of LNGC. The noise reduction performance was confirmed by calculating insertion loss with and without air injection.
From January 2020 to October 2021, more than 500,000 academic studies related to COVID-19 (Coronavirus-2, a fatal respiratory syndrome) have been published. The rapid increase in the number of papers related to COVID-19 is putting time and technical constraints on healthcare professionals and policy makers to quickly find important research. Therefore, in this study, we propose a method of extracting useful information from text data of extensive literature using LDA and Word2vec algorithm. Papers related to keywords to be searched were extracted from papers related to COVID-19, and detailed topics were identified. The data used the CORD-19 data set on Kaggle, a free academic resource prepared by major research groups and the White House to respond to the COVID-19 pandemic, updated weekly. The research methods are divided into two main categories. First, 41,062 articles were collected through data filtering and pre-processing of the abstracts of 47,110 academic papers including full text. For this purpose, the number of publications related to COVID-19 by year was analyzed through exploratory data analysis using a Python program, and the top 10 journals under active research were identified. LDA and Word2vec algorithm were used to derive research topics related to COVID-19, and after analyzing related words, similarity was measured. Second, papers containing 'vaccine' and 'treatment' were extracted from among the topics derived from all papers, and a total of 4,555 papers related to 'vaccine' and 5,971 papers related to 'treatment' were extracted. did For each collected paper, detailed topics were analyzed using LDA and Word2vec algorithms, and a clustering method through PCA dimension reduction was applied to visualize groups of papers with similar themes using the t-SNE algorithm. A noteworthy point from the results of this study is that the topics that were not derived from the topics derived for all papers being researched in relation to COVID-19 (