• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1452-1461
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• 2020

A triple bubbler sensor was tested in LiCl-KCl molten salt from 450 to 525 ℃ in a transparent furnace to validate thermal-expansion corrections and provide additional molten salt data sets for calibration and validation of the sensor. In addition to these tests, a model was identified and further developed to accurately determine the density, surface tension, and depth from the measured bubble pressures. A unique feature of the model is that calibration constants can be estimated using independent depth measurements, which allow calibration and validation of the sensor in an electrorefiner where the salt density and surface tension are largely unknown. This model and approach were tested using the current and previous triple bubbler data sets, and results indicate that accuracies are as high as 0.03%, 4.6%, and 0.15% for density, surface tension, and depth, respectively.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.493-495
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• 2005

This paper proposes a swarm-based fuzzy system modeling technique for coke oven combustion control diagnosis. The coke plant produces coke for the blast furnace plant in steel making process by charging coal into oven and supplying gas to carbonize it. A conventional mathematical model for coke oven combustion control has been used to control the amount of gas input, but it does not work well because of highly nonlinear feature of coke plant. To solve this problem, swarm-based fuzzy system modeling technique is suggested to construct a diagnosis model of coke oven combustion control. Based on the measured input-output data pairs, the fuzzy rules are generated and the parameters are tuned by the PSO(Particle Swarm Optimizer) to increase the accuracy of the fuzzy system is operated. This system computes the proper amount of gas input taking the operation conditions of coke oven into account, and compares the computed result with the supplied gas input.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2946-2948
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• 2000

In this paper, we proposed the Fuzzy Polynomial Neural Networks(FPNN) model with fuzzy activation node. The proposed FPNN structure is generated from the mutual combination of PNN(Polynomial Neural Networks) structure and fuzzy inference system. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. The structure of FPNN is not fixed like in conventional Neural Networks and can be generated. The design procedure to obtain an optimal model structure utilizing FPNN algorithm is shown in each stage. Gas furnace time series data used to evaluate the performance of our proposed model.

• Computers and Concrete
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• v.2 no.1
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• pp.19-30
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• 2005

We study the kinetics of absorption of water in Portland cement concretes added with 60, 70 and 80% of granulated blast furnace slag (GGBS) cured in water and at open air and preheated at 50 and $100^{\circ}C$. A mathematical model is presented that allows describing the process not only in early ages where the capillary sorption is predominant but also for later and long times where the diffusive processes through the finer and gel pores are considered. The fitting of the model by computerized methods enables us to determine the parameters that characterize the process: i.e., the sorptivity coefficient (S) and diffusion coefficient (D). This allows the description of the process for all times and offers the possibility to know the contributions of both, the diffusive and capillary processes. The results show the influence of the curing regime and the preheating temperature on the behavior of GGBS mortars.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.197-202
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• 1999

Electric arc furnaces (EAFs) use bulk electrical energy to create heat in metal refining industries. The electric arc process is a main cause of the degradation of the electric power quality such as voltage flicker due to the interaction of the high demand currents of the load with the supply system impedance. The stochastic models have described the aperiodic physical phenomena of EAFs. An alternative approach is to include deterministic chaos in the characterization of the arc currents. In this parer, a chaotic approach to such modeling is described and justified. At the same time, a DLL(Dynamic Link Library) module, which is a FORTRAN interface with TACS (Transient Analysis of Control Systems), is developed to implement the chaotic load model in the Electromagnetic Transients Program (EMTP). The details of the module and the results of tests performed on the module to verify the model and to illustrate its capabilities are presented in this paper.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.7
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• pp.708-712
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• 2004

Shallow $p^+-n$ junctions were formed by ion implantation and dual-step annealing processes. The dopant implantation was performed into the crystalline substrates using BF$_2$ ions. The annealing was performed with a rapid thermal processor and a furnace. FA+RTA annealing sequence exhibited better junction characteristics than RTA+FA thermal cycle from the viewpoint of junction depth and sheet resistance. A new simulator is designed to model boron diffusion in silicon. The model which is used in this simulator takes into account nonequilibrium diffusion, reactions of point defects, and defect-dopant pairs considering their charge states, and the dopant inactivation by introducing a boron clustering reaction. Using initial conditions and boundary conditions, coupled diffusion equations are solved successfully. The simulator reproduced experimental data successfully.

• Bulletin of the Korean Chemical Society
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• v.16 no.7
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• pp.661-666
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• 1995

The surface structure of the adsorption site for the identification of active sites involved in the Ziegler-Natta catalyst was studied by surface science techniques. As an example of a real catalyst, TiCl3 single crystals were prepared in a gradient furnace designed for this study and characterized by Auger Electron Spectroscopy (AES) and Low Energy Electron Diffraction (LEED) under ultrahigh vacuum condition. The chlorine covered Ti (0001) surface was employed as a model catalyst for the study of Ziegler-Natta catalyst. The diffuse LEED (DLEED) technique for the surface structural determination was applied to this disordered chlorine adsorbed on Ti (0001) surface. The diffuse scattering intensities were measured by a TV-computer method using a low light level video camera. From an analysis of two catalyst systems, the informations for the surface structure of the model catalyst surfaces were derived.

• Journal of Appropriate Technology
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• v.7 no.1
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• pp.41-50
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• 2021

Gongali model Co. Ltd located in Arusha, Tanzania is operating a Nanofilter water station using locally produced bone char to remove fluoride in groundwater. Bone char produced locally had a high turbidity and high concentration of organic matter, which cause color. In addition, since the fluorine adsorption efficiency is low, there is a problem in high maintenance cost due to a short replacement cycle of bone char. In order to overcome this challenge, our research team was that a local furnace was manufactured and applied for produce high adsorption bone char in Gongali model Co. Ltd. By producing high-adsorption bone char locally, the operating efficiency of the Nanofilter water station increased, and it was possible to stably and continuously provide drinking water to local residents. In addition, by presenting a sustainable business model to Gongali model Co Ltd, the persistence of high adsorption bone char and a plan to spread the Nanofilter water station were suggested. Therefore, it was possible to propose a plan to continuously supply low-cost drinking water to the low-income and the neglected class through this local project.

• Journal of the Korean Society of Combustion
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• v.15 no.1
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• pp.22-29
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• 2010

In this study, carbon conversion was measured using an electronic mass balance. In a lab scale furnace, each coal sample was pyrolyzed in a nitrogen environment and became coal char, which was then gasified with steam under isothermal conditions. The reactivity of coal char was investigated at various temperatures and steam concentrations. The VRM(volume reaction model), SCM(shrinking core model), and RPM(random pore model) were used to interpret experimental data. For each model the activation energy(Ea), pre-exponential factor (A), and reaction order(n) of the coal char-steam reaction were determined by applying the Arrhenius equation into the data obtained with thermo-gravimetric analysis(TGA). According to this study, it was found that experimental data agreed better with the VRM and SCM for 1,000 and $1,100^{\circ}C$, and the RPM for 1,200 and $1,300^{\circ}C$. The reactivity of chars increased with the increase of gasification temperature. The structure parameter(${\psi}$) of the surface area for the RPM was obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.