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IN713LC-SCM440 마찰용접부의 부식특성에 미치는 비틀림응력의 영향
조상근(S. K. Jo),공유식(Y. S. Kong),김영대(Y. D. Kim) 한국동력기계공학회 2004 한국동력기계공학회 학술대회 논문집 Vol.- No.-
The effect of loaded torsional stress on the corrosion characteristics in the dissimilar friction welded zone of IN713LC-SCM440 in the seawater was studied. The corrosion experiment was performed for 120 hours on the specimens in the natural seawater tank with four steps of the loaded torsional stress. The surface corrosion pattern of SCM440 area are showed global corrosion and narrow pitting, that was cause by galvanic corrosion between friction welded IN713LC and SCM440. But corrosion does not proceeded from IN713LC area. Initially, the average relative electrode potential and corrosion current are decreased suddenly, by and large, it was stabilized gradually tend to decreasing with the elapse of the immersion time. The corrosion rate was decreased by increasing the load stress. But 200 MPa specimen was showed most large value in the average relative electrode potential was higher and the average relative current was lower. The corrosion pattern of the dissimilar friction welded zone indicates that the depth and width of the pitting become increasing with the increasing loaded torsional stress.
딥러닝 기반 화력발전 보일러 출구 NOx 농도 선행 예측 모델
조현빈(Hyunbin Jo),강동협(Donghyup Kang),박성민(Seongmin Park),이종욱(Jongwuk Lee),류창국(Kang Y. Huh) 한국연소학회 2022 KOSCOSYMPOSIUM논문집 Vol.2022 No.11
In this study, we developed a deep learning model to forecast the NOx and oxygen concentration, and gas temperature at the boiler exit of a coal-fired power plant. The target boiler is a 500 MWe tangential firing boiler, which is one of 20 units often referred to as standard coal power plant. From the database of the power plant, 73 raw items of operation data with one-minute frequency were collected for a period of approximately 5 months. Through the feature selection procedure, the raw data items were condensed into 19 features which include coal feeder throughput to burners, air flow rate, and burner tilt. The features were then used to establish two types of data segments: segment #1 for current operation status and segment #2 for recent histories measured at the boiler exit. Considering the large fluctuations, the histories of the recent values at the boiler exit values were averaged over 5 min. After evaluating different prediction models with respect to the nature of the data segments, suitable models were applied in the form of ensemble model to forecast the boiler exit values 1 min in advance. When compared to measured data, the prediction quality was sufficiently high with a mean square error of 0.0123 for NOx emission.