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생물화공 , 에너지 / 환경 : 전기화학적 방법을 이용한 폐수내 유기물과 질소 처리
주재백(Jae Back Ju),손태원(Tae Won Son),박대원(Dea Won Pak),정도원(Do Won Chung),조한상(Han Sang Cho) 한국화학공학회 2001 Korean Chemical Engineering Research(HWAHAK KONGHA Vol.39 No.5
This research has been performed to study electrochemical treatment of organic compounds and nitrogen in wastewater. Organic compounds were removed through electrochemical treatment. The removal rate of organic compounds as increased with the increasing current density(A/㎠). Because reaction rate is increased. The results in this study suggested that DSA electrode was capable of removing organic compounds and nitrogen from industrial wastewater.
논문 : 습식제련 ; Nd-Fe-B계 자석 스크랩으로부터 황산에 의한 Nd의 추출
이재천 ( Jae Chun Lee ),김원백 ( Won Back Kim ),정진이 ( Jin Ki Jeong ),윤인주 ( In Ju Yoon ) 대한금속재료학회 ( 구 대한금속학회 ) 1998 대한금속·재료학회지 Vol.36 No.6
A new process to extract Nd from domestic Nd-Fe-B magnet scraps was developed. The scraps were roasted in air and leached with H2SO4. The selective extraction of Nd was investigated in terma of roasting temperature, H2SO4 concentration, and leaching temperature and time. The increase in roasting temperature resulted in a significant reduction in the extraction rate due to the formation of FeNdO3. Nevertheless, Nd could be extracted more selectively since the extraction of Fe was retarded by the concurrent formation of Fe2O3 at higher temperatures. The selective extraction of Nd was observed to increase with shorter leaching time, lower H2SO4 concentration and leaching temperature. The solubility difference between FeSO4 and Nd2(SO4)3 was utilized to precipitate Nd as Nd2(SO4)3, which was separated as residue during the solid/liquid separation process after leaching. When scraps roasted at 700℃ was leached at the pre-determined optimum conditions(H2SO4 concentration: 4M, leaching temperature: 70℃, leaching time: 180 minutes, pulp density 100g/1) about 70% of Nd could be selectively separated as Nd2(SO4)3 precipitates. The Nd/Fe ratio in the precipitate was found to be about 18.33.
김인재 ( In Jae Kim ),정치량 ( Chi Ryang Chung ),국민하 ( Min Ha Kuk ),박승용 ( Seung Yong Park ),최경화 ( Kyung Hwa Choi ),백초옥 ( Cho Ok Back ),이창훈 ( Chang Hun Lee ),오호준 ( Ho Jun Oh ),박성주 ( Seoung Ju Park ) 전북대학교 의과학연구소 2011 全北醫大論文集 Vol.35 No.2
Allopurinol (4-hydroxypyrazolo-[3,4-d]pyrimidine) is an effective and widely used xanthine oxidase inhibitor administered in the treatment of hyperuricemic states such as gout. Drug rash with eosinophilia and systemic symptoms (DRESS) syndrome is an unexpected complication of drug which can be characterized by eosinophilia, skin rash, fever, lymph node enlargement, and single or multiple organ involvement. The most common culprit drugs for DRESS syndrome are phenobarbital, phenytoin, and carbamazepine. Allopurinol can cause hypersensitivity syndrome, and DRESS syndrome induced by this drug has higher motality despite of proper management. We report here a case of allopurinol-induced DRESS syndrome who develops erythematous skin eruption, fever, hepatitis, and lymphadenopathy after allopurinol therapy for 35 days. This case may alert physicians to be cautious regarding allopurinol-induced DRESS syndrome, as this drug has been frequently prescribed.
전상백 ( Sang Back Jeon ),김평중 ( Pyoung Joong Kim ),김상수 ( Sang Su Kim ),주재식 ( Jae Sik Ju ),이용화 ( Young Hwa Lee ),장대수 ( Dae Soo Chang ),이정의 ( Jung Uie Lee ),박승윤 ( Seoung Yun Park ) 한국환경분석학회 2012 환경분석과 독성보건 Vol.15 No.3
We measured various geochemical parameters including texture, chemical oxygen demand (COD), ignition loss (IL), acid volatile sulfide (AVS), nonmetallics, alkali metals, alkaline earth metals, transition elements and heavy metals in order to determine the characteristics of spatial distribution in the surface sediments collected from 19 stations in the Deukryang Bay. It was observed that chemical oxygen demand (COD) and acid volatile sulfide in most stations were below the criteria proposed by Japan Fisheries Resource Conservation Association (JFRCA) to evaluate the pollution status of marine sediment. The pollution level was estimated by Enrichment factor (Ef) and Index of geoaccumulation (Igeo). For elements, the Enrichment factors (Ef) were smaller than 1 except for arsenic (As) which had highest Ef value of 1.72 and the Index of geoaccumulation (Igeo) for nonmetallic and metallic elements ranged between 1 and 2, indicating that the pollution levels by these elements were not serious. However, for the sustainability of the Deukryang Bay, continous monitoring should be necessary for with these harmful elements.
Prediction of golden time using SVR for recovering SIS under severe accidents
Yoo, Kwae Hwan,Back, Ju Hyun,Na, Man Gyun,Kim, Jae Hwan,Hur, Seop,Kim, Chang Hwoi Elsevier 2016 Annals of nuclear energy Vol.94 No.-
<P><B>Abstract</B></P> <P>Nuclear power plants (NPPs) are designed in consideration of design basis accidents (DBAs). However, if the safety injection system (SIS) is not working properly in a loss-of-coolant-accident (LOCA) situation, it can induce a severe accident that exceeds DBAs. Therefore, it is important to properly actuate the SIS before a DBA becomes a severe accident. If the SIS is not working in time, the reactor core may be uncovered and the reactor vessel (RV) may be damaged. In this paper, we defined the golden time as the available time from an initial SIS malfunction for actuating the SIS to prevent reactor core uncovery and RV failure. A support vector regression (SVR) model was applied to predict the golden time. The input variables and parameters of the SVR model were selected and optimized by using a genetic algorithm. The data set of severe accident scenarios was obtained by using the Modular Accident Analysis Program (MAAP) code. An optimized power reactor (OPR1000) was used for the simulations. It was shown that that the proposed SVR model could predict the golden time accurately.</P> <P><B>Highlights</B></P> <P> <UL> <LI> If the safety injection system is not working in time, the reactor vessel may be damaged. </LI> <LI> It is important to properly actuate the SIS before a DBA becomes a severe accident. </LI> <LI> Golden time is defined as the available time from an initial SIS malfunction for recovering SIS. </LI> <LI> A support vector regression (SVR) model was applied to predict the golden time. </LI> <LI> It was shown that the proposed SVR model could predict the golden time accurately. </LI> </UL> </P>