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스컬용융법에 의한 패각을 이용한 큐빅지르코니아 단결정 성장
정진화,연석주,석정원,Jung, Jin-Hwa,Yon, Seog-Joo,Seok, Jeong-Won 한국결정성장학회 2013 韓國結晶成長學會誌 Vol.23 No.3
In this research, cubic zirconia is synthesized with a refined CaO from shells as a stabilizer through Skull melting method. The proper process time and concentration are defined by Hydration reaction to produce the refined CaO after two different treatments using 0.1 mol% of HCl respectively with Cockle shell. The highest purity of CaO is reached when the shell is immersed in 1 mol% HCl. In Hydration reaction step, the pure $Ca(OH)_2$ is produced at $45^{\circ}C$ for 24 hours. The highest purity of CaO is measured when the $Ca(OH)_2$ is treated by heat at $1200^{\circ}C$ for 5 hours. The single crystals are grown through Skull melting method by adding the different contents of the refined CaO from 10 mol% to 30 mol% into $ZrO_2$. The frequency of High-frequency oscillator used for Skull melting method is 3.4 MHz. The descending speed of the single crystal is 3 mm/hour. The grown length of the single crystal is 4 cm. As a result of this study, 15 mol% of CaO has the best crystallinity. 본 연구에서는 안정화제로, 패각으로부터 정제한 CaO를 사용하여 Skull melting법으로 큐빅 지르코니아를 성장시켰다. 꼬막 패각을 HCl을 0.1~1 mol%로 1차 처리한 후 수화반응을 거쳐 최적의 처리시간과 농도를 검토하였다. HCl을 1 mol%의 농도로 처리했을 때 가장 순도가 높았으며 수화반응시 $45^{\circ}C$의 온도로 24시간 동안 반응시킬 때 완전한 $Ca(OH)_2$ 를 얻을 수 있었다. $Ca(OH)_2$를 $1200^{\circ}C$에서 5시간 처리하였을 때 CaO의 순도가 가장 높게 측정되었다. 패각으로부터 얻어진 CaO를 $ZrO_2$에 첨가하여 함량을 10~30 mol% 로 변화시켜 Skull melting법으로 단결정을 성장시켰다. Skull melting법에 사용된 고주파 발진기의 주파수는 약 3.4 MHz이며 단결정의 하강속도는 시간당 3 mm로 4 cm 길이로 성장시켰다. 실험결과 CaO의 함량이 15 mol% 일 때 결정성이 가장 우수했다.
기계학습을 활용한 기상예측자료 기반 태양광 발전량 예측 향상기법
정진화(Jin-Hwa Jeong),채영태(Young-Tae Chae) 한국생활환경학회 2018 한국생활환경학회지 Vol.25 No.1
This study investigated a selection of machine learning model to forecast electric power output from photovoltaic arrays based on forecasted weather data and historic solar radiation data. It tested two approaches to improve forecasting accuracy of power output with three typical machine learning algorithms such as Random Forest(RF), Artificial Neural Network(ANN), and Support Vector Machine(SVM). A forecasting power output was conducted with conventional weather forecasting data from national weather service which does not include solar radiation. The other approach has two steps, forecasting solar radiation with weather forecasting data and historic solar radiation data then it forecasts the electric power output of photovoltaic arrays. It has been studied the importance variables incorporated with the power output forecasting. The results show that the forecasting accuracy of the power output improves by using forecasted solar radiation data and Random Forest outperforms on this power output forecasting problem among other machine learning algorithms.