• Economic and Environmental Geology
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• v.56 no.6
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• pp.781-797
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• 2023

Recently, there has been a rapid response from mineral-demanding countries for securing critical minerals in a high tech industries. Graphite, while overwhelmingly dominated by China in production, is changing in global supply due to the exponential growth in EV battery sector, with active exploration in East Africa. Rare earth elements are essential raw materials widely used in advanced industries. Globally, there are ongoing developments in the production of REEs from three main deposit types: carbonatite, laterite, and ion-adsorption clay types. While China's production has decreased somewhat, it still maintains overwhelming dominance in this sector. Recent changes over the past few years include the rapid emergence of Myanmar and increased production in Vietnam. Nickel has been used in various chemical and metal industries for a long time, but recently, its significance in the market has been increasing, particularly in the battery sector. Worldwide, nickel deposits can be broadly classified into two types: laterite-type, which are derived from ultramafic rocks, and ultramafic hosted sulfide-type. It is predicted that the development of sulfide-type, primarily in Australia, will continue to grow, while the development of laterite-type is expected to be promoted in Indonesia. This is largely driven by the growing demand for nickel in response to the demand for lithium-ion batteries. The global lithium ores are produced in three main types: brine lake (78%), rock/mineral (19%), and clay types (3%). Rock/mineral type has a slightly higher grade compared to brine lake type, but they are less abundant. Chile, Argentina, and the United States primarily produce lithium from brine lake deposits, while Australia and China extract lithium from both brine lake and rock/mineral sources. Canada, on the other hand, exclusively produces lithium from rock/mineral type. Vanadium has traditionally been used in steel alloys, accounting for approximately 90% of its usage. However, there is a growing trend in the use for vanadium redox flow batteries, particularly for large-scale energy storage applications. The global sources of vanadium can be broadly categorized into two main types: vanadium contained in iron ore (81%) produced from mines and vanadium recovered from by-products (secondary sources, 18%). The primary source, accounting for 81%, is vanadium-iron ores, with 70% derived from vanadium slag in the steel making process and 30% from ore mined in primary sources. Intermediate vanadium oxides are manufactured from these sources. Vanadium deposits are classified into four types: vanadiferous titanomagnetite (VTM), sandstone-hosted, shale-hosted, and vanadate types. Currently, only the VTM-type ore is being produced.

• Industry Promotion Research
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• v.9 no.1
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• pp.203-212
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• 2024

The purpose of this study is to examine the validity of Hidden Stems (支藏干) in the Four Pillars of Destiny with regard to the use of human governance periods in the hidden stems (人元用事). First, there is a theory of assigning period of governance for designated constituents (司令論) in the Hidden Stems of the Earthly Branch. Second, there is a theory that determines the structure of the Four Pillars by the Exposed Constituent from the Hidden Stems (透出論) in the Month Earthly Branch. Since these two theories conflict with each other and cause confusions, this study examined the theory of Hidden Stems in the Four Pillars Classics and examined the historical development of governance period for constituent hidden stems and their validity. The results of the study are as follows: Firstly, the number of dates assigned to respective constituents does not correspond to the calendarical principle, and the assignment of the governance dates for each constituent does not correspond to the principles proposed in ancient books of Four Pillars. Second, though it is said in the Classics that 72 days are equally assigned to each of the Five Elements, actual distributed days for the five elements was 65 days for Wood, 55 days for Fire, 100 days for Earth, 65 days for Metal, and 65 days for Water. Third, though it is said that 7 days should be designated to Yang Earth Mu for the months of Tiger 寅, Monkey, Snake, and Pig, it is logically more legitimate to assign those days to Yin Earth Ki since the month before Tiger is Ox, and the month before Monkey is Goat. Lastly, rationale behind assigning Ki Earth only to Horse Oh as constituting Hidden Stem while disregarding months of Rat, Rabbit, and Rooster is considered not reasonable. Looking at these results comprehensively, it is concluded that the Exposed Constituent theory is logically more appropriate than Assigned Governance theory.

• Clean Technology
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• v.30 no.2
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• pp.123-133
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• 2024

Zeolite template carbon (ZTC) was synthesized as an adsorbent to remove low-concentration CH₄ from the atmosphere. The synthesis of ZTC was performed using CH₄ and C₂H₂ as carbon precursors and their impact on adsorption was investigated. ZTC was also synthesized using Y zeolite ion-exchanged with CaCl₂ and LiCl as templates to investigate the effect of using metals in ion exchange. The comparison of the carbon precursors revealed that C₂H₂ had a higher carbon yield than CH₄. The synthesized ZTC exhibited developed micropores due to carbon deposition deep inside the micropores of the zeolite template. The kinetic diameter of C₂H₂ (0.33 nm) is smaller than that of CH₄ (0.38 nm), which allowed for its deposition. The study compared metal precursors used for ion exchange and confirmed that the CaCl₂-based ZTC developed more micropores compared to the LiCl-based ZTC. The ion-exchanged Ca inhibited pore blocking by the carbon precursor, allowing it to enter the pores. The ability of synthesized ZTC to adsorb N₂ and CH₄ at 298 K was investigated. The results showed that CH₄ had a higher overall adsorption amount than N₂. The sample synthesized using C₂H₂ and CaY exhibited the highest N₂ and CH₄ adsorption capacity. However, the sample synthesized with CH₄ had the highest CH₄/N₂ gas uptake ratio, which is a crucial factor in designing an adsorption process. The observed difference was likely caused by the underdevelopment of ultrafine pores that are associated with N₂ adsorption. This resulted in a reduction of N₂ adsorption, leading to an increase in CH₄/N₂ separation.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.