• Journal of the Korean Ceramic Society
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• v.24 no.1
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• pp.47-55
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• 1987

The purpose of this work is to establish the basic calibration data for the efficiency of grinding by investigating the Bond's Work Index employing Korean Kaolin as a reference mateial with the laboratory-scale ball mill. A small ordinary ball mill has a dimension of 133 inside diameter and 144mm long. The analysis of the experimental results in this work sets up a equivalent calibration method with the laboratory-scale ball mill to those with special mill. The theoretical expression, derived from the rate equation proposed by Miwa, is obtained to anticipitate the stable revolution number for the next grinding cycle. The proposed equation is more systematic and acurate than lshihara's empirical equation is more systematic and acurate than lshihara's empirical equation for the measurement of gindability of a ball mill.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.6
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• pp.1139-1148
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• 2000

To treat and recycle a large quantity of converter slag. the milling properties of -14/ +24 mesh-sized slag has been considered. The optimal conditions in milling process were investigated for producing powder-type slag and the required consumption was derived for the economical grinding. The characteristics of milling processes were studied in the variation of the rotational speed, milling time, filling ratio of ball, and size and amount of feed. The grinding efficiency was also examined. The optimal rotational speed in this experimental condition was observed to be the value of 79% of critical speed. The extent of grinding was increased with increasing the grinding time. but the efficiency of milling was decreased with the time. 50% ball filling was shown to have the optimal grinding effect, and less amount and small-sized feed made the milling efficiency high. As the result, using Bond's equation, power required for efficient milling was considered and the highest value was observed in the condition of high grinding time and optimal rotational speed.

• Journal of Energy Engineering
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• v.28 no.1
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• pp.1-9
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• 2019

In the flue gas desulfurization process of the coal-fired power plant, the grinding efficiencies of the limestone as the sorbent for desulfurization were compared after BWI and HGI measurements. As a result, the grinding index of the domestic desulfurization limestone were linear inversely proportional relationship with decreasing BWI was observed with increasing HGI. There was a difference in grinding efficiency depending on the chemical composition and crystal structure. Therefore, it is considered that when grinding ability of limestone is measured, the grinding property of the sample can be confirmed even by using HGI which can be measured more easily than BWI which is difficult to measure and takes a long time. The desulfurization efficiency can be improved by selective utilization of limestone depending on the crushing characteristics.

• 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.