• Analytical Science and Technology
• /
• v.29 no.2
• /
• pp.57-64
• /
• 2016

Many phthalic acid esters (PAEs), including DMP, DEP, DBP, BBP, and DEHP, as well as DEHA are widely used as plasticizers in plastics. An analytical method was developed and used to analyze these compounds at 41 industrial facilities. The coefficient of determination (R²) for each constructed curve was higher than 0.98. The method detection limit (MDL) values were 0.4–0.7 μg/L for PAEs and 0.6 μg/L for DEHA. In addition, the recovery rate was shown to be 77.0–92.3%, while the relative standard deviation was shown to be in the range of 5.8-10.5%. DMP (n = 3), DEP (n = 2), DBP (n = 2), BBP (n = 2), and DEHA (n = 3) were detected in the range of 2.2-11.1% in the influent. DEHP was a predominant compound and was detected at > MDL in both the influent (n = 16, 35.6%) and the effluent (n = 4, 10.0%) at a high removal efficiency (92–100%). The highest levels of residue in industrial wastewater influent were 137.4 μg/L of DEHP at plastic products manufacturing facility, 12.5 μg/L of DEHA at a chemical manufacturing facility, and 14.0 μg/L of DEP at an electronics facility. The highest concentration of effluent was 12.5 μg/L of DEHP at a chemical manufacturing facility, which indicated that the effluent was below the allowable concentration (800 μg/L). Therefore, the levels of PAEs and DEHA that are discharged into nearby streams could not influence the health of the ecosystem.

• Korean Journal of Soil Science and Fertilizer
• /
• v.29 no.4
• /
• pp.432-438
• /
• 1996

This study was carried out to prepare information for the establishment of countermeasures for an area contaminated with minewaste from an old zinc mine at Kahak-long in Kwangmyong. Minewaste and bottom sediments from the streams in this area were sampled and were analyzed for Cd, Cu, Pb, and Zn extracted with different solution. Total heavy metal contents in both minewaste and bottom sediments were fairly high. Cadmium and Zn contents in the minewaste and Cd, Cu, Pb, and Zn contents in the bottom sediments extracted with 0.1 N HCl showed a much higher level than those in the background level of paddy soils and in the soils around the other metal mines regardless of the distance from the mine. Sulfide/residue forms of Cd, Cu, Pb, and Zn covered the highest portions for the minewaste. For bottom sediments, sulfide/residue forms of Cu and Zn formed the highest portions, whereas the contents of both carbonate and sulfide/residue forms of Cd and Pb were significant. The lower the pH of the extracting solutions, the more heavy metals extracted from both minewaste and bottom sediments.

• Journal of IKEEE
• /
• v.25 no.3
• /
• pp.493-500
• /
• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Resources Recycling
• /
• v.31 no.6
• /
• pp.44-51
• /
• 2022

In the steelmaking process using an electric arc furnace (EAF), light-burnt dolomite, which is a flux containing MgO, is used to protect refractory materials and improve desulfurization ability. Furthermore, a recarburizing agent is added to reduce energy consumption via slag foaming and to induce the deoxidation effect. Herein, a waste MgO-C based refractory material was used to achieve the aforementioned effects economically. The waste MgO-C refractory materials contain a significant amount of MgO and graphite components; however, most of these materials are currently discarded instead of being recycled. The mass recycling of waste MgO-C refractory materials would be achievable if their applicability as a flux for steelmaking is proven. Therefore, experiments were performed using a target composition range similar to the commercial EAF slag composition. A pre-melted base slag was prepared by mixing SiO₂, Al₂O₃, and FeO in an alumina crucible and heating at 1450℃ for 1 h or more. Subsequently, a mixed flux #2 (a mixture of light-burnt dolomite, waste MgO-C based refractory material, and limestone) was added to the prepared pre-melted base slag and a melting reaction test was performed. Injecting the pre-melted base slag with the flux facilitates the formation of the target EAF slag. These results were compared with that of mixed flux #1 (a mixture of light-burnt dolomite and limestone), which is a conventional steelmaking flux, and the possibility of replacement was evaluated. To obtain a reliable evaluation, characterization techniques like X-ray diffraction (XRD) analysis and X-ray fluorescence (XRF) spectrometry were used, and slag foam height, slag basicity, and Fe recovery were calculated.

• Journal of Chest Surgery
• /
• v.36 no.12
• /
• pp.929-936
• /
• 2003

We analysed the surgical outcomes of immediate reoperations after mitral valve repair. Material and Method: Eighteen patients who underwent immediate reoperation for failed mitral valve repair from April 1995 through July 2001 were reviewed retrospectively. There were 13 female patients. The mitral valve disease was regurgitation (MR) in 12 patients, stenosis (MS) in 3, and mixed lesion in 3. The etiologies of the valve disease were rheumatic in 9 patients, degenerative in 8, and endocarditis in 1. The causes of reoperation was residual MR in 13 patients, residual MS in 4, and rupture of left ventricle in 1. Fourteen patients had rerepair for residual mitral lesions (77.8%) and four underwent replacement. Result: There was no early death. After mean follow-vp of 33 months, there was one late death. Echocardiography revealed no or grade 1 of MR (64.3%) in 9 patients and no or mild MS in 11 patients (78,6%). Reoperation was done in one patient. The cumulative survival and freedom from valve-related reoperation at 6 years were 94% and 90%, respectively. The cumulative freedom from recurrent MR and MS at 4 years were 56% and 44%, respectively. Conclusion: This study suggests that immediate reoperation for failed mitral valve repair offers good early and intermediate survival, and mitral valve rerepair can be successfully performed in most of patients. However, because mitral rerepair have high failure rate, especially in rheumatic valve disease, adequate selections of valvuloplasty technique and indication are important to reduce the failure rate of mitral rerepair.