• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1661-1666
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• 2010

본 연구의 목적은 충북대학교 부속농장에서 2009년 영농기간을 중심으로 담수된 논에서의 산화환원전위(Eh)의 변화와 시비에 따라 영양물질인 인(P)의 농도변화 특성을 파악함으로써, 논으로 부터의 인의 유출제어에 관한 기초 자료를 제공하는데 있다. 이 연구는 2009년 5월부터 11월까지 논에서 담수의 총인(T-P)과 인산염 인($PO_4$-P)의 농도변화와 토양의 산화환원전위(Eh)와의 관계 특성을 파악하였다. 관개기의 논에서 인은 분얼비 시기에 인성분이 시비되지 않았는데도 불구하고 T-P농도가 0.68 mg/L로 높게 나타났다. 이는 담수의 영향으로 논이 환원상태로 되어, 논바닥에 침전된 철이온에 흡착되어 있던 인이 철이온의 환원으로 함께 용출하기 때문이라고 생각된다. 높은 Eh는 산화경향을, 낮은 Eh는 환원경향을 나타낸다. 본 연구기간 동안의 Eh 값은 연속적으로 담수되었던 7월 중순까지는 74~112 mV 가량 나타냈고, 그 이후에는 담수상태가 아닌 경우가 많아 179~636 mV로 높게 나타났다. 논 담수의 T-P와 $PO_4$-P 농도는 분얼비 직후 1주일후까지 같이 상승하다가 T-P농도는 약 2주일까지 더 상승한 반면 $PO_4$-P 농도는 하강하였는데 이는 논 토양이 환원상태로 되면서 바닥에 있던 입자성 인이 논 표면으로 떠올랐기 때문으로 사료된다. 그 후에는 담수가 끝나는 시점까지인 농도는 낮아졌다. 관개초기에 인의 농도는 비교적 높게 나타났지만, 7월 이후로는 작물의 생장에 필요한 영양물질 섭취 등으로 인 농도가 낮게 나타났는데 이는 7월 이후의 논은 인의 유출을 억제하고 있는 것으로 추정된다. 또한 논 담수위의 증감에 따른 $PO_4$-P 농도와 Eh 값을 회귀분석 한 결과 각각 정의 상관관계와 부의 상관관계가 있는 것으로 나타났다. 이와 같이 논 담수 및 시비에 따른 인의 유출부하 특성과 산화환원전위(Eh)의 변화 특성이 규명된다면, 향후 환경부하가 작은 물관리가 가능해 질 것으로 판단된다.

• 한국생물공학회:학술대회논문집
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• 2000.04a
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• pp.173-176
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• 2000

A neural network based controller (NN controller) was studied for the control of ammonia concentrations in biological processes. An ammonia FIA has been employed to on-line monitor the concentrations of ammonia in a bioreactor. The optimal neural network structure was investigated by computer simulation and found to be a 3(inputlayer)-2(hidden layer)-1(output layer). The NN controller had advantage over the PID controller, even though the former is more time consuming. The 3-2-1 NN controller has been used to control the ammonia concentrations in a simulated bioprocess and also in a real cultivation process of yeast, and its performance were investigated.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.10a
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• pp.135-140
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• 1995

원자로를 임계에 도달시키기 위해서는 먼저 운전원이 예상임계 제어봉위치를 설정한 후, 예상 임계점을 계산하여, 원자로 냉각재 붕산농도를 조절하고 제어봉을 인출하여 원자로를 임계에 도달하도록 한다. 현재 원자력발전소에서는 이러한 기동과정에서의 예상임계점 계산은 수작업으로 하고있다. 본 논문은 고온대기에서 2% 출력까지 자동기동 시스템을 개발하기 위해 예상임계점 계산 전산화가 필요하므로 자동으로 예상임계점을 계산하는 프로그램을 개발하였다.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2002.04a
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• pp.65-66
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• 2002

대도시 오존농도의 저감을 실현시키기 위한 가장 중요한 과제로 오존의 생성과 관련 있는 전구물질(precursor)에 대한 제어과정이 대두됨에 따라 주요 배출원 중 자동차배출 VOCs에 대한 정확한 총 배출량을 파악하고, VOCs의 각 성분별 배출구성비를 파악하는 것은 오존생성반응을 모사ㆍ예측하고 대기 중 오존 오염을 제어하는데 있어서 필수적이라 하겠다. 그러나 국내에서는 자동차등 이동오염원에 대한 배출실태조사가 이루어지지 않아 배출원 관리에 어려움이 있는 실정이다. (중략)

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.368-368
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Since the mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, modeling of ozone prediction system has many problems and results of prediction are not good performance so far. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• Proceedings of the Korean Vacuum Society Conference
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• 1995.06a
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• pp.81-82
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• 1995

약한 자장을 사용하는 평면형 유도 결합 플라즈마 식각 장치에서 라디카르이 농도 제어를 위하여 벽면온도를 증가시켜 라디칼 손실을 줄였다. 폴리머를 잘 형성하는 C4F8의 경우 이에 따라 식각 특성이 25%정도 개선되었고, OES(opptical emission sppectroscoppy)와 AMS(appearance mass sppectroscoppy)를 이용하여 라디칼과 이온의 밀도 변화를 측정하여 식각 결과를 설명할 수 있었으며, 반면에 CF4의 경우 큰변화를 볼 수 없었다.

• Korean Journal of Ecology and Environment
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• v.39 no.4 s.118
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• pp.471-480
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• 2006

The field scale experiment was performed to examine the performance of the constructed wetland for nonpoint source (NPS) pollution loading reduction. Four sets (each set of 0.88 ha) of wetland (0.8 ha) and pond (0.08 ha) systems were used. Water flowing into the Seokmoon estuarine reservoir from the Dangjin stream was pumped into wetland systems. Water depth was maintained at 0.3-0.5 m and hydraulic retention time was managed to about 2-5 days; emergent plants were allowed to grow in the wetland. The wetland effluent concentrations of $BOD_5$, TSS, and T-N were higher in winter than in the growing season excepting the T-P, and effluent $BOD_5$ concentration was higher than influents in winter. Mass retention of T-N and T-P was stable throughout the year, whereas mass retention of $BOD_5$ and TSS was decreased in winter. $BOD_5$, TSS, T-N, and T-P performance of the experi-mental system was compared with the existing database (North American Treatment Wetland Database), and was within the range of general system performance. From the first-order analysis, T-P was virtually not temperature dependent, and $BOD_5$ and TSS were more temperature dependent than T-N. Overall, the wetland system was found to be an adequate alternative for treating polluted stream water with stable removal efficiency and recommended as a NPS control measures.

• Journal of Bio-Environment Control
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• v.20 no.3
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• pp.169-175
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• 2011

The adaptive neuro-fuzzy inference system (ANFIS) based automatic control system framework was proposed for integrated environment management of ubiquitous plant factory which can collect information of crop cultivation environment and monitor it in real-time by using various environment sensors. Installed wireless sensor nodes, based on the sensor network, collect the growing condition's information such as temperature, humidity, $CO_2$, and the control system is to monitor the control devices by using ANFIS. The proposed automatic control system provides that users can control all equipments installed on the plant factory directly or remotely and the equipments can be controlled automatically when the measured values such as temperature, humidity, $CO_2$, and illuminance deviated from the decent criteria. In addition, the better quality of the agricultural products can be gained through the proposed automatic control system for plant factory.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.2
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• pp.147-155
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• 2005

For a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution level below the required level. To control the tunnel pollution level, a closed loop control algorithm may be used. The cascade control algorithm, which composed of a jet fan control algorithm and an air velocity setpoint algorithm, was developed to regulate the CO level in a tunnel. The verification of control algorithms was carried out by dynamic models developed from real tunnel data sets. The simulation results showed that control algorithms developed for this study were effective to control the tunnel ventilation system.