• Journal of Environmental Science International
• /
• v.14 no.11
• /
• pp.1049-1056
• /
• 2005

[ $NO_2$ ] concentration characteristics of Busan metropolitan city was analysed by statistical method using hourly $NO_2$ concentration data$(1998\~2000)$ collected from air quality monitoring sites of the metropolitan city. 4 representative regions were selected among air quality monitoring sites of Ministry of environment. Concentration data of $NO_2$, 5 air pollutants, and data collected at AWS was used. Both Stepwise Multiple Regression model and ARIMA model for prediction of $NO_2$ concentrations were adopted, and then their results were compared with observed concentration. While ARIMA model was useful for the prediction of daily variation of the concentration, it was not satisfactory for the prediction of both rapid variation and seasonal variation of the concentration. Multiple Regression model was better estimated than ARIMA model for prediction of $NO_2$ concentration.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.4
• /
• pp.397-414
• /
• 2003

The performance of one-particle stochastic Lagrangian models for passive tracer dispersion are evaluated against measurements in horizontally-homogeneous neutrally-stratified atmospheric surface layer. State-of-the-technology models as well as classical Langevin models, all in class of well mixed models are numerically implemented for inter-model comparison study. Model results (far-downstream asymptotic behavior and vertical profiles of the time averaged concentrations, concentration fluxes, and concentration fluctuations) are compared with the reported measurements. The results are: 1) the far-downstream asymptotic trends of all models except Reynolds model agree well with Garger and Zhukov's measurements. 2) profiles of the average concentrations and vertical concentration fluxes by all models except Reynolds model show good agreement with Raupach and Legg's experimental data. Reynolds model produces horizontal concentration flux profiles most close to measurements, yet all other models fail severely. 3) With temporally correlated emissions, one-particle models seems to simulate fairly the concentration fluctuations induced by plume meandering, when the statistical random noises are removed from the calculated concentration fluctuations. Analytical expression for the statistical random noise of one-particle model is presented. This study finds no indication that recent models of most delicate theoretical background are superior to the simple Langevin model in accuracy and numerical performance at well.

• Nuclear Engineering and Technology
• /
• v.55 no.8
• /
• pp.2757-2772
• /
• 2023

The fluid retention effect of the Volume Control Tank (VCT) leads to a long time delay in Reactor Coolant System (RCS) concentration during the boration process. A coupled model combining a lumped-parameter sub-model and a computational fluid dynamics sub-model is currently used to investigate the concentration dynamic characteristic of RCS during the boration process. This model is validated by comparison with experimental data, and the predicted results show excellent agreement with experimental data. We provide detailed fields in VCT and concentration variations of RCS to study the interaction between mixing in VCT and the transient responses of RCS. Moreover, the impacts of the inlet flow rate, inlet nozzle diameter, original concentration, and replenishing temperature of VCT on the RCS concentration characteristic are studied. The inlet flow rate and nozzle diameter of VCT remarkably affect the RCS concentration characteristic. Too-large or too-small inlet flow rates and nozzle diameters will lead to unacceptable long delays. In this work, the optimal inlet flow rate and nozzle diameter of VCT are 5 m³/h and 58.8 mm, respectively. Besides, the impacts of the original concentration and replenishing temperature of VCT are negligible under normal operating conditions.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.7 no.5
• /
• pp.275-280
• /
• 2002

In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.4 no.1
• /
• pp.51-58
• /
• 1999

A mathematical model for a three phase fluidized bed bioreactor (TFBBR) was proposed to describe oxygen utilization rate, biomass concentration and the removal efficiency of Chemical Oxygen Demand (COD) in wastewater treatment. The model consisted of the biofilm model to describe the oxygen uptake rate and the hydraulic model to describe flow characteristics to cause the oxygen distribution in the reactor. The biofilm model represented the oxygen uptake rate by individual bioparticle and the hydrodynamics of fluids presented an axial dispersion flow with back mixing in the liquid phase and a plug flow in the gas phase. The difference of setting velocity along the column height due to the distributions of size and number of bioparticle was considered. The proposed model was able to predict the biomass concentration and the dissolved oxygen concentration along the column height. The removal efficiency of COD was calculated based on the oxygen consumption amounts that were obtained from the dissolved oxygen concentration. The predicted oxygen concentration by the proposed model agreed reasonably well with experimental measurement in a TFBBR. The effects of various operating parameters on the oxygen concentration were simulated based on the proposed model. The media size and media density affected the performance of a TFBBR. The dissolved oxygen concentration was significantly affected by the superficial liquid velocity but the removal efficiency of COD was significantly affected by the superficial gas velocity.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.129-135
• /
• 2018

In this study, we designed a model that can measure the level of user's concentration by measuring and analyzing EEG data of the subjects who are performing Continuous Performance Test based on visual stimulus. This study focused on alpha and beta waves, which are closely related to concentration in various brain waves. There are a lot of research and services to enhance not only concentration but also brain activity. However, there are formidable barriers to ordinary people for using routinely because of high cost and complex procedures. Therefore, this study designed the model using the portable EEG measurement device with reasonable cost and Visual Continuous Performance Test which we developed as a simplified version of the existing CPT. This study aims to measure the concentration level of the subject objectively through simple and affordable way, EEG analysis. Concentration is also closely related to various brain diseases such as dementia, depression, and ADHD. Therefore, we believe that our proposed model can be useful not only for improving concentration but also brain disease prediction and monitoring research. In addition, the combination of this model and the Brain Computer Interface technology can create greater synergy in various fields.

• Journal of Biosystems Engineering
• /
• v.43 no.3
• /
• pp.229-236
• /
• 2018

Purpose: Makgeolli is a traditional alcoholic beverage made from rice with a fermentation starter called "nuruk." The concentration of alcohol in makgeolli depends on the temperature of the fermentation tank. It is important to monitor the alcohol concentration to manage the makgeolli production process. Methods: Data were collected from 84 makgeolli fermentation tanks over a year period. Independent variables included the temperatures of the tanks and the room where the tanks were located, as well as the quantity, acidity, and water concentration of the source. Software for the multilayer perceptron model (MLP) was written in Python using the Scikit-learn library. Results: Many models were created for which the optimization converged within 100 iterations, and their coefficients of determination $R^2$ were considerably high. The coefficient of determination $R^2$ of the best model with the training set and the test set were 0.94 and 0.93, respectively. The fact that the difference between them was very small indicated that the model was not overfitted. The maximum and minimum error was approximately 2% and the total MSE was 0.078%. Conclusions: The MLP model could help predict the alcohol concentration and to control the production process of makgeolli. In future research, the optimization of the production process will be studied based on the model.

• Journal of Environmental Science International
• /
• v.16 no.12
• /
• pp.1319-1324
• /
• 2007

A tank model in conjunction with Kalman filter is developed for prediction of sediment yield from an upland watershed in Northwestern Mississippi. The state vector of the system model represents the parameters of the tank model. The initial values of the state vector were estimated by trial and error. The sediment yield of each tank is computed by multiplying the total sediment yield by the sediment yield coefficient. The sediment concentration of the first tank is computed from its storage and the sediment concentration distribution(SCD); the sediment concentration of the next lower tank is obtained by its storage and the sediment infiltration of the upper tank; and so on. The sediment yield computed by the tank model using Kalman filter was in good agreement with the observed sediment yield and was more accurate than the sediment yield computed by the tank model.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.18 no.4
• /
• pp.344-350
• /
• 2006

The present study investigates the characteristics of fire-driven heat flows and gas concentration in a compartment fire by using the modified VHS model (MVHS). The main idea of this model is to add some source terms for combustion products and oxygen consumption to the original VHS model for providing more accurate and useful information on gas concentration distributions as well as thermal fields. It is found that the present MVHS model shows fairly good agreement with the experimental data and the eddy breakup combustion model. The tilting angle of fire plume calculated by MVHS is larger than that of EBU model because the fire source of VHS is affected by ventilating flow less than EBU. However, this discrepancy is apparently reduced in the downstream region of fire source.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.277-279
• /
• 2021

There is a problem that learning of a prediction model is not well performed depending on the characteristics of each particular matter concentration. To solve this problem, it is necessary to design a prediction model for low concentration and high concentration separately. Therefore, a classification model is needed to classify the concentration of particular matter into low and high concentrations. This paper proposes a classification model to classify low and high concentrations based on the concentration of particular matter. DNN was used as the classification model algorithm, and the classification model was designed by applying the optimal parameters after searching for hyper parameters. As for the result of evaluating the performance of the model, 97.54% of the low concentration classification was measured. And in the case of high concentration classification, 85.51% was measured.