• Journal of the Korean Society of Combustion
• /
• v.15 no.2
• /
• pp.41-49
• /
• 2010

Various coals from many countries around the world have been used for pulverized coal boiler in power plants in Korea. In this study, the gasification reactivities of various coal chars with $CO_2$ were investigated. Carbon conversion was measured using a real time gas analyzer with NDIR CO/$CO_2$ sensor. In a lab scale furnace, each coal sample was devolatilized at $950^{\circ}C$ in nitrogen atmosphere and became coal char and then further heated up to reach to a desired temperature. Each char was then gasified with $CO_2$ under isothermal conditions. The reactivities of coal chars were investigated at different temperatures. The shrinking core model (SCM) and volume reaction model(VRM) were used to interpret the experiment data. It was found that the SCM and VRM could describe well the experimental results within the carbon conversion of 0-0.98. The gasification rates for various coals were very different. The gasification rate for any coal increased as the volatile matter content increased.

• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.261-270
• /
• 1999

Sintering transforms powdered ore into lumped ore so that the latter can be used in a blast furnace. The powdered ore combined with coke and other materials is loaded into a container and moved along by a pallet while the ignited coke bums. The speed by which the pallet moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, we propose a neural network-based pallet speed controller which copies human operator knowledge. Actual process data were collected from a sintering plant fer eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a back-propagation learning algorithm. In on-line testing at the sinter plant, the proposed model reliably controlled pallet speed during normal operation without the help of human operators. Moreover, the duality and productivity was as good as with human operators.

• Journal of the Speleological Society of Korea
• /
• no.82
• /
• pp.5-7
• /
• 2007

In this paper, p-n junction formation using screen-printed metalization and co-firing is used to fabricate high-efficiency solar cells on single- crystalline silicon substrates. In order to form high-quality contacts, co-firing of a screen-printed Ag grid on the front and Al on the back surface field is implemented. These contacts require low contact resistance, high conductivity, and good adhesion to achieve high efficiency. Before co-firing, a statistically designed experiment is conducted. After the experiment, a neural network (NN) trained by the error back-propagation algorithm is employed to model the crucial relationships between several input factors and solar cell efficiency. The trained NN model is also used to optimize the beltline furnace process through genetic algorithms.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.03a
• /
• pp.261-270
• /
• 1999

Sintering transforms powdered ore into lumped ore so that the latter can be used in a blast furnace. The powdered or combined with coke and other materials is loaded into a container and moved along by a pallet while the ignited coke burns. The speed by which the pallet moves determines how much sintering takes place. Since the process is complicated and lacks an accurate mathematical model, human operators manually control the speed by monitoring various factors in the plant. In this paper, we propose a neural network-based pallet speed controller which copies human operator knowledge. Actual process data were collected from a sintering plant for eight months and preprocessed to remove noisy and inconsistent data. A multilayer perceptron was trained using a back-propagation learning algorithm. In on-line testing at the sinter plant, the proposed model reliably controlled pallet speed during normal operation without the help of human operators. Moreover, the quality and productivity was as good as with human operators.

• Journal of the Korean Society of Combustion
• /
• v.17 no.2
• /
• pp.32-39
• /
• 2012

In this study, pre-processor code based on structural behavior of coal is applied to predict yields, pyrolysis rate and compositions of volatile and char. These parameters are used in the devolatilization and char burnout sub-models as user-defined functions of commercial CFD code. The predicted characteristics of these sub-models are compared with those employing the conventional model based on experiment and validated against the measurement of a 2.1 MW swirling pulverized coal flame in a semi-industrial scale furnace. And the influence of the turbulence-chemistry interaction on pulverized coal combustion is analyzed.

• Proceedings of the KIEE Conference
• /
• 2000.11d
• /
• pp.803-805
• /
• 2000

In this paper, we propose fuzzy-neural Networks(FNN) which is useful for identification algorithms. The proposed FNN model consists of two steps: the first step, which determines premise and consequent parameters approximately using FCM_RI method, the second step, which adjusts the premise and consequent parameters more precisely by gradient descent algorithm. The FCM_RI algorithm consists FCM clustering algorithm and Recursive least squared(RLS) method, this divides the input space more efficiently than convention methods by taking into consideration correlations between components of sample data. To evaluate the performance of the proposed FNN model, we use the time series data for gas furnace.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2018.05a
• /
• pp.130-131
• /
• 2018

The compressive strength of concrete is determined by various influencing factors. However, the conventional method for estimating the compressive strength of concrete has been suggested by considering only 1 to 3 specific influential factors as variables. In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were learned using the Deep Learning Algorithm to derive an estimated function model. The purpose of this study is to investigate the effect of the number of hidden layers on the prediction performance in the process of estimating the compressive strength for an arbitrary combination.

• Proceedings of the KIEE Conference
• /
• 2001.07a
• /
• pp.392-394
• /
• 2001

Coal power plants are large, non-linear systems with numerous interactions between its component parts. In the analysis of such complex systems, dynamic simulation is recognized as a powerful method of keeping track of the myriad of interactions. This paper shows and discusses the developed analysis model, such as the forced draft fan the primary air fan, the furnace and burner system, air preheater and induced draft fan, etc. in accordance with BMCR condition of boiler using the Modular Modeling System(MMS) software.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.566-568
• /
• 1998

In this paper, we suggest a optimal design method of Fuzzy-Neural Networks model for complex and nonlinear systems. FNNs have the stucture of fusion of both fuzzy inference with linguistic variables and Neural Networks. The network structure uses the simpified inference as fuzzy inference system and the BP algorithm as learning procedure. And we use a clustering algorithm to find initial parameters of membership function. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance index, we use the time series data for gas furnace and the sewage treatment process.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.2911-2913
• /
• 1999

This paper suggest an optimal identification method to complex and nonlinear system modeling that is based on Fuzzy-Neural Network(FNN). The FNN modeling implements parameter identification using HCM algorithm and optimal identification algorithm structure combined with two types of optimization theories for nonlinear systems, we use a HCM Clustering Algorithm to find initial parameters of membership function. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using optimal identification algorithm. The proposed optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregate objective function(performance index) with weighted value is proposed 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, the data of sewage treatment process and traffic route choice process.