Tungsten carbide is widely used in carbide tools. However, its production process generates a significant number of end-of-life products and by-products. Therefore, it is necessary to develop efficient recycling methods and investigate the remanufacturing of tungsten carbide using recycled materials. Herein, we have recovered 99.9% of the tungsten in cemented carbide hard scrap as tungsten oxide via an alkali leaching process. Subsequently, using the recovered tungsten oxide as a starting material, tungsten carbide has been produced by employing a self-propagating high-temperature synthesis (SHS) method. SHS is advantageous as it reduces the reaction time and is energy-efficient. Tungsten carbide with a carbon content of 6.18 wt % and a particle size of 116 nm has been successfully synthesized by optimizing the SHS process parameters, pulverization, and mixing. In this study, a series of processes for the highefficiency recycling and quality improvement of tungsten-based materials have been developed.

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.

In this study, actual odor conditions were investigated in restaurants, livestock facilities, and major odor discharge facilities around daily life, and an odor modeling program was performed to find ways to improve odors in odor discharge facilities. The odor modeling results of restaurants around daily life showed that the complex odor concentration of large restaurants, which are close to residential areas, is higher than the acceptable complex odor standard at the receiving point. It was judged that a plan to increase the height of the restaurant odor outlets and a plan to reduce the amount of odor discharge was necessary. As a result of modeling the life odor of livestock housing facilities, when the distance from the housing facility is far away, the actual emission concentration is much lower than the acceptable emission concentration at the receiving point. It was judged that such facilities need to be reviewed for ways to reduce the emission of odorous substances, such as sealing the livestock housing facilities or improving the livestock environment. The main odor emission business sites that show complex odor concentration as 1,000 times or greater than the outlet odor emission standard were businesses associated with surfactant preparation, compounded feed manufacturing, textile dyeing processing, and waste disposal. Due to the separation distance and high exhaust gas flow rate, it was found that odor reduction measures are necessary. In this study, it was possible to present the allowable odor emission concentration at the discharge facilities such as restaurants, livestock houses, and industrial emission facilities by performing the process of verifying the discharge concentration of the actual discharge facility and the result of living odor modeling. It is believed that suitable odor management and prevention facilities can be operated.

Owing to increasing demand of rare metals present in ICT products, it is necessary to promote the rare metal recycling industry from an environmental viewpoint and to prevent climate change. Despite the fact that information for toxic substances is partly indicated, a legal basis and an international standard indicating usage of rare metals is insufficient. In order to address this issue, a newly created study group of environment and climate change at the ITU (International Telecommunication Union) is doing research to develop methodologies for recycling rare metals from ICT products in an eco-friendly way. Under this group, the Republic of Korea has established two international standards related to rare metals present in ICT products. The first is ‘Release of rare metal information for ICT products (ITU-T L.1100)’ and the other is ‘Quantitative and qualitative analysis methods for rare metals (ITU-T L.1101)’. A new proposal for recommending the provision of rare metal information through a label by manufacturers and consumer/recycling businesses has been approved recently and is supposed to be published later in 2016. Moreover, these recommendations are also being extended to IEC, ISO and other standardization organizations and a strategy to reinforce the ability for domestic standardization is being established in accordance with industrial requirements. This will promote efficient recycling of rare metals from ICT products and will help improve the domestic supply of rare metals.

In this study, a correlation analysis of odor was performed in order to assess the reliability and the field applicability of the Odorous gas sensor for continuous real-time monitoring. Hydrogen sulfide was found to have a correlation of 41.5~65.8%, and Ammonia is was found to have very low correlation in less than 200 ppb concentration. Reactivity evaluation result, hydrogen sulfide is the reactivity was higher than the low concentration condition of 100 ppb or less indicated by 31.3~36.4% in the 100 ppb or more high density condition based on the reference density value. For ammonia was very low reactivity in the low-concentration conditions below 200 ppb. TVOC and composite odor assessment did not occur Reactivity no reference concentration value, the specific comparison between both sensors showed a similar trend. In the same Odorous gas sensor accuracy between the result, 40.3~130.6% hydrogen sulfide, ammonia, 69.1~104.9%, TVOCs is 24.7~98.6%, exhibited human odor intensity from 5.5~33.2%.

The monolayer engineering diamond particles are aligned on the oxygen free Cu plates with electroless Ni plating layer. The mean diamond particle sizes of 15, 23 and 50 μm are used as thermal conductivity pathway for fabricating metal/carbon multi-layer composite material systems. Interconnected void structure of irregular shaped diamond particles allow dense electroless Ni plating layer on Cu plate and fixing them with 37-43% Ni thickness of their mean diameter. The thermal conductivity decrease with increasing measurement temperature up to 150oC in all diamond size conditions. When the diamond particle size is increased from 15 μm to 50 μm (Max. 304 W/mK at room temperature) tended to increase thermal conductivity, because the volume fraction of diamond is increased inside plating layer.