• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1412-1416
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• 2003

Induction Heaters are commonly used in heating steel strip product, because it can rapidly and efficiently heat steel strip/bar. In this study, a inductive heating model is developed and the predictions are compared with measured temperatures. The temperatures are measured from POSCO thin-slab rolling facility (so called Minimill). Induction heater is installed between reduction unit and holding furnace This induction heater raise the temperature of steel bars from $930^{\circ}C$ to about $1100^{\circ}C$ which gives the required temperature for finishing mill process after holding period at holding furnace. Unlike other simple equation models, this model allows us to predict temperature profiles of sections of steel bars.

• 한국연소학회:학술대회논문집
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• 2012.04a
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• pp.217-219
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• 2012

This study describes 3D RANS simulation of a 2.1 MW swirling pulverized coal flame in a semi-industrial scale furnace. The simulation of pulverized coal combustion involves various models for complex physical processes and needs information of pyrolysis rate, the yields and compositions of volatiles and char especially in coal conversion. The coal conversion information can be acquired by the experiment or the pre-processor code. The empirical model based on the experiment of the IFRF and the structural model based on the pre-processor code of the PC-COAL-LAB were evaluated against the measurement data.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.3
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• pp.40-49
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• 1992

In this paper, an approach to identify fuzzy rules is proposed. The decision of the optimal number of fuzzy rule is made by means of fuzzy c-means clustering. The identification of the parameters of fuzzy implications is carried out by use of genetic algorithms. For the efficinet and fast parameter identification, the reduction thechnique of search areas of genetica algorithms is proposed. The feasibility of the proposed approach is evaluated through the identification of the fuzzy model to describe an input-output relation of Gas Furnace. Despite the simplicity of the propsed apprach the accuracy of the identified fuzzy model of gas furnace is superior as compared with that of other fuzzy modles.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2001.11a
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• pp.25-32
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• 2001

• Journal of Conservation Science
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• v.34 no.4
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• pp.273-281
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• 2018

$Ji{\check{u}}di{\grave{a}}n$ iron production site in China is a relic smelting site, which in the past produced pig iron. In this study, scientific analysis of the smelting furnace and collected slag was conducted to reveal some aspects of the ancient Chinese smelting technique. A 3D model of the smelting furnace showed a narrow lower part and an upper section which increased in diameter upwards. Although the smelting furnace relic does not include the upper part and its complete shape cannot be predicted, the remaining part suggests that the furnace had a larger diameter in the central part compared to the upper and lower parts. Most of the collected slag was completely vitrified. Long prismatic fayalite was observed in the matrix of some samples. The iron particles contained phosphorus, which could not be discharged during smelting work. In addition, as the $CaO/SiO_2$ ratio was 0.42 or lower in the results of the content analysis, no CaO slag former had been added. However, the ratio of $CaO/SiO_2$ to $Al_2O_3/SiO_2$ did not have a constant trend. This needs to be investigated in a further study.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.40-48
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• 2012

This study evaluates the shear performance of reinforced concrete beams with electric arc furnace oxidizing slag aggregates generated from iron manufacture. A total of six simple supported specimens were cast and tested in shear. The main test variables were the type of aggregates and the amount of shear reinforcements. The specimens under four point loading had a shear span-to-depth ratio of 2.5 and a rectangular section with a width of 200mm and an effective depth of 300mm. Existing equations to predict the shear strength of the specimens were used in this study. Furthermore, a finite element analysis using shear analytical model was performed to trace the shear behavior of the specimens with electric arc furnace oxidizing aggregates. From the test results, the shear performance of specimens with electric arc furnace oxidizing aggregates is similar to that of specimens with natural aggregates.

• 한국연소학회:학술대회논문집
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• 2012.11a
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• pp.75-78
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• 2012

Rotary kiln furnace is one of the most widely used reactors in industrial field. In this paper, 0-dimensional heat and mass balance for direct coal flame rotary kiln was performed preferentially, then a simplified 1-dimensional model was developed based on 0-dimensional analysis data to proceed additional thermal analysis. Compared the results with the currently operating rotary kiln data to validate 1-dimensional model. Through this procedure, it can help to derive fundamental idea for design and operation of rotary kiln.

• Journal of the Korean Society of Safety
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• v.30 no.1
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• pp.8-13
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• 2015

The experimental design methodology was applied in the drop tube furnace (DTF) to predict the various combustion properties according to the operating conditions and to assess the coal plant safety. Response surface method (RSM) was introduced as a design of experiment, and the database for RSM was set with the numerical simulation of DTF. The dependent variables such as burnout ratios (BOR) of coal and $CO/CO_2$ ratios were mathematically described as a function of three independent variables (coal particle size, carrier gas flow rate, wall temperature) being modeled by the use of the central composite design (CCD), and evaluated using a second-order polynomial multiple regression model. The prediction of BOR showed a high coefficient of determination (R2) value, thus ensuring a satisfactory adjustment of the second-order polynomial multiple regression model with the simulation data. However, $CO/CO_2$ ratio had a big difference between calculated values and predicted values using conventional RSM, which might be mainly due to the dependent variable increses or decrease very steeply, and hence the second order polynomial cannot follow the rates. To relax the increasing rate of dependent variable, $CO/CO_2$ ratio was taken as common logarithms and worked again with RSM. The application of logarithms in the transformation of dependent variables showed that the accuracy was highly enhanced and predicted the simulation data well.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.7
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• pp.424-433
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.7
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• pp.424-424
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• 2003

Evolutionary design related to the optimal design of Polynomial Neural Networks (PNNs) structure for model identification of complex and nonlinear system is studied in this paper. The PNN structure is consisted of layers and nodes like conventional neural networks but is not fixed and can be changable according to the system environments. three types of polynomials such as linear, quadratic, and modified quadratic is used in each node that is connected with various kinds of multi-variable inputs. Inputs and order of polynomials in each node are very important element for the performance of model. In most cases these factors are decided by the background information and trial and error of designer. For the high reliability and good performance of the PNN, the factors must be decided according to a logical and systematic way. In the paper evolutionary algorithm is applied to choose the optimal input variables and order. Evolutionary (genetic) algorithm is a random search optimization technique. The evolved PNN with optimally chosen input variables and order is not fixed in advance but becomes fully optimized automatically during the identification process. Gas furnace and pH neutralization processes are used in conventional PNN version are modeled. It shows that the designed PNN architecture with evolutionary structure optimization can produce the model with higher accuracy than previous PNN and other works.