• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2008년도 학술발표회 논문집
• /
• pp.500-504
• /
• 2008

The objective of this paper is to evaluate the feasibility of using an optimization model as a effective way to search conjunctive operation scheme to meet two conditions; one is to minimize the electric cost for pumping and another is to meet the water demand for satisfying customers. The feasibility is confirmed as comparing the best combinations of pumps between multi-regional water supply networks from multiple sources which are obtained through an optimization modeling and EPAnet modeling. KModsim model, a network optimization model, was used to determine conjunctive operation scheme in the pipe system. KModsim, based on Lagrangian Relaxation algorithm, is useful for modeling network system and obtaining simultaneously pump combination and water allocation with given input option such as energy unit cost supplying from a source into a consumer, operating pumping combination. This study develops the procedure of determining optimal conjunctive operation scheme with using KModsim model. As a study region, the water supplying systems of the Geojae-city in the Geongsang Namdo Province was selected and investigated. The EPAnet hydraulic simulation result(Ryu et al, 2007, KSWW) gave input data for optimization model; energy unit price(won/$m^3$), water service available area etc.. It was assured that the combination of pump operation through optimum conjunctive operation is to be optimum scheme to obtain the best economic water allocation with comparison to the hydraulic simulation result such as electric cost and pump combination cases. The results obtained through the study are as follows. First, It was found that a well-allocated water supply scheme, the best combination of pump operation through optimum joint operation, promises to save the electric cost and satisfy all operational goals such as stability and revenues during the period. Second, an application of KModSim, a network model, gave the amount of water allocation from each source to a consumer with consideration of economic supply. Finally, in a service area available to supply through conjunctive operation of existing inter-regional water supply networks within short distance, a conjunctive operation is useful for determining each transmission pipeline's service area and maximizing the effectiveness of optimizations in pumping operation time.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2019년도 학술발표회
• /
• pp.161-161
• /
• 2019

Water security issues, stimulated by increasing population and changing climate, are growing and pausing major challenges for water resources managers around the world. Proper utilization, management and distribution of all available water resources is key to sustainable development for achieving water security To alleviate the water shortage, most of the current research on multi-sources combined water supplies depends on an overall generalization of regional water supply systems, which are seldom broken down into the detail required to address specific research objectives. This paper proposes the concept of optimization framework on multi water sources selection. A multi-objective water allocation model with four objective functions is introduced in this paper. Harmony search algorithm is employed to solve the applied model. The objective functions addresses the economic, environmental, and social factors that must be considered for achieving a sustainable water allocation to solve the issue of water security.

• 상하수도학회지
• /
• 제35권1호
• /
• pp.83-91
• /
• 2021

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.

• 설비공학논문집
• /
• 제15권6호
• /
• pp.495-501
• /
• 2003

Control algorithms for the absorption air conditioning system may be developed by using dynamic models of the system. The simplified effective dynamic models, which can predict the dynamic behaviors of the system, may help to develop effective control algorithms for the system. In this study, control algorithms for an absorption air conditioning system were developed by using a dynamic simulation program. A cooling water inlet temperature control algorithm, a chilled water outlet temperature control algorithm, and a supply air temperature control algorithm, were developed and analyzed. The steepest descent method was used as an optimal algorithm. The simulation results showed energy savings and the effective controls of an absorption air conditioning system.

• 상하수도학회지
• /
• 제34권1호
• /
• pp.35-43
• /
• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• 설비공학논문집
• /
• 제16권9호
• /
• pp.881-888
• /
• 2004

Control algorithms for a dual source chiller air conditioning system were developed. These are control algorithms for the supply air temperature control, the supply header chilled water temperature control, the chiller chilled water temperature control, and the cooling tower water temperature control. These algorithms were analyzed by using a dynamic simulation program. Simulation results showed the energy savings and the satisfactory controls of an absorption and centrifugal chiller air conditioning system. Therefore, control algorithms developed for this study may effectively be used for the improved controls of the dual source chiller air conditioning system.

• 한국방재학회 논문집
• /
• 제9권6호
• /
• pp.133-141
• /
• 2009

상수관망의 용수는 수용가가 사용하기에 불편함이 없는 적절한 유량과 압력으로 사용성이 충분히 만족된 상태에서 공급되어야 한다. 상수도 시스템의 수리학적 해석 방법으로 사용되는 Demand Driven Analysis (DDA) 방법은 관망의 수리학적 상태가 변화했을 때 부압이 발생하는 등 비현실적인 결과를 발생시킬 수 있다. Pressure Driven Analysis (PDA) 방법은 비정상상태에서의 압력 및 공급량의 변화를 알기 위하여, 절점수두-용수공급량 관계 (Head-Outflow Relationship, HOR)를 이용하는데, 이는 실측자료의 부족으로 인하여 대부분의 연구에서는 HOR이 가정되어 사용되었다. 본 연구에서는 PDA 분석에서 단점을 가진 HOR 대신, 절점에서 실제 사용성을 만족시키면서 공급이 가능한 용수량인 유효유량을 제안하였다. 그리고 Subsystem이 격리되었을 때 유효유량의 변화를 산정함으로써 격리된 부분의 관망에 대한 영향을 평가하여 이를 Subsystem 중요도 지수(Subsystem Importance Index, SII)로 정의하였다. 이를 위해 최적화 기법 중 하나인 Harmony Search와 상수관망 해석 프로그램인 EPANET을 결합하여 모형을 구축하였다. 제안된 모형을 대규모 상수관망에 적용하였으며, 본 모형은 상수관망의 유지, 보수 시에 관거 혹은 밸브 등의 처리 우선순위 산정 및 상수관망 신뢰도 평가로 활용 가능할 것으로 판단된다. 또한 유효유량산정을 통하여 상수관망이 실제로 사용함에 불편함이 없을 정도로 용수공급이 얼마나 가능한가를 종전에 비하여 보다 정량적으로 산정 가능하다.

• 대한설비공학회:학술대회논문집
• /
• 대한설비공학회 2008년도 동계학술발표대회 논문집
• /
• pp.399-404
• /
• 2008

Condensing gas boiler units may make a big role for the reduction of energy consumption in heating industries. In order to decrease the energy consumption of a condensing gas boiler unit, the effective control of the system is necessary. In this study, control algorithms of a condensing gas boiler were developed. Control algorithms are composed of the setpoint algorithm and the control algorithm. The setpoint algorithm consists of the supply water temperature setpoint algorithm and the pump setpoint algorithm. The control algorithm consists of the gas valve control algorithm and the blower control algorithm. In order to analyse the performance of control algorithms, dynamic models of a condensing gas boiler system were used. Simulation results showed that control algorithms developed for this study may be practically applied to the condensing gas boiler.

• 한국수자원학회논문집
• /
• 제56권10호
• /
• pp.619-629
• /
• 2023

최근 기후변화가 심화됨에 따라 가뭄으로 유발되는 피해가 증가하고 있다. 현재 국내의 가뭄 강도를 결정하기 위해 표준강수지수(Standardized Precipitation Index, SPI)를 기준으로 분류를 수행하고 있다. 현재 국내에서는 최근 6개월 동안의 누적강수량을 기준(SPI-6)으로 관심, 주의, 경계, 심각의 기상학적 가뭄의 강도를 분류하고 있다. 그러나 강수량만을 기초자료로 활용하기 때문에 가뭄 강도를 분류하는 데 한계가 있다는 문제점이 있다. 따라서 본 연구에서는 SPI에 따른 국내 기상학적 가뭄 예･경보 기준의 한계점을 극복하고자 국가가뭄정보포털(National Drought Information Portal, NDIP)에서 제공하는 비상급수 피해자료를 수집하여 가뭄의 강도를 분류하였다. 그리고 SPI의 인자인 강수량과 증발산량 산정에 사용되는 인자인 온도, 습도 등을 min-max 정규화로 지수화한 후 유전 알고리즘(Genetic Algorithm, GA) 기반으로 각 인자들에 대한 계수를 산정하였다. 비상급수에 따른 가뭄의 강도를 분류하여 종속변수로 활용하고, GA에 의한 각 기상인자들의 계수를 활용하여 새로운 가뭄 강도 분류 지수(Drought Severity Classification Index, DSCI)를 도출하고자 하였다. DSCI를 도출한 후 누적분포함수를 활용하여 분위별 경계를 강도 단계 분류 기준으로 제시하였다. 본 연구에서 제시한 DSCI를 활용하면 기존 SPI보다 가뭄 강도를 정확하게 분류할 수 있어, 재난 담당자들의 의사결정을 지원할 수 있을 것으로 판단된다.

• 전력전자학회논문지
• /
• 제15권2호
• /
• pp.134-141
• /
• 2010

소형 PEM (Proton Exchange Membrane) 연료전지 시스템은 가습이 필요치 않아 상용화의 가능성이 크지만 그 제어 방법은 뚜렷하게 정립되어 있지 않다. 따라서 본 논문에서는 소형 PEM 연료전지 시스템의 제어를 위해 이중 루프 구조의 제어방식을 정립하고 DSP (Digital Signal Processor)를 이용하여 구현한다. 일반적으로 연료전지 시스템에서 제어의 핵심 요소는 크게 공기와 수소의 공급, 스택 내부의 수분 관리, 스택의 온도 관리로 나뉜다. 별도의 가습이 없이 공랭식으로 구동되는 소형 PEM 연료전지 스택의 제어에 있어서 팬은 스택의 공기 공급과 열관리 및 수분관리를 위한 핵심적인 역할을 하며, 퍼지밸브는 스택 내부의 잉여수분을 배출한다. 제안된 방식은 이중 제어루프를 이용한 팬의 제어를 통해 팬의 과도응답을 빠르게 하여 공기의 공급 속도를 개선시키며, 연료전지 스택의 전압변화를 피드백 하여 보상해줌으로써 연료전지가 부하변동에 대해 신속한 응답 특성을 갖도록 하였다. 제안된 방법의 유용함은 60W급 소형 PEM 연료전지 시스템의 실험과 이를 이용한 노트북 컴퓨터의 구동을 통해 검증된다.