• 대한설비공학회:학술대회논문집
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• 대한설비공학회 2009년도 하계학술발표대회 논문집
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• pp.161-166
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• 2009

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 boiler unit, the effective operation is necessary. In this study, the supply water temperature algorithm of a condensing gas boiler was developed. This includes the setpoint algorithm and the control algorithm of the supply water temperature. The setpoint algorithm was developed by the fuzzy logic and the control algorithm was developed by the proportional integral algorithm. In order to analyse the performance of the supply water temperature algorithm, the dynamic model of a condensing gas boiler system was used. Simulation results showed that the supply water temperature algorithm developed for this study may be practically applied for the control of the condensing gas boiler.

• 설비공학논문집
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• 제23권6호
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• pp.441-448
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• 2011

The energy consumption of a condensing gas boiler may be greatly reduced by the effective operation of the unit. In this study, the supply water algorithm for a condensing gas boiler control was developed by using the fuzzy logic. This includes the supply water set temperature algorithm, and the control algorithms of a gas valve, a blower and a pump. For the set temperature algorithm, the outside air temperature and the return water temperature were used as input variables. The supply water temperature difference and its slope were used as input variables of the gas valve and blower control algorithm. And the supply water temperature and the return water temperature were used as input variables of the pump control algorithm. In order to analyse performances of these algorithms, the dynamic model of a condensing gas boiler was used. The initial start-up test, the supply water set temperature change test, the outside air temperature change test, and the return water temperature change test were performed. Simulation results showed that algorithms developed in this study may be practically applied for the effective control of a condensing gas boiler.

• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1056-1061
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• 2009

This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.

• 한국기계기술학회지
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• 제13권2호
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• pp.63-70
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• 2011

In the present study, we developed optimal heat supply algorithm which minimizes the heat loss through the distribution pipe line in group energy apartment. Heating load variation of group energy apartment building in accordance with outdoor air temperature was predicted by the correlation obtained from calorimeter measurements of whole households of apartment building. Supply water temperature and mass flow rate were conjugately controlled to minimize the heat loss rate through distribution pipe line. Group heating apartment located in Hwaseong city, Korea, which has 1,473 households divided in 4 regions, was selected as the object apartment for verifying the present heat supply control algorithm. Compared to the original heat supply system, 10.4% heat loss rate reduction can be accomplished by employing the present control algorithm.

• 한국농공학회논문집
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• 제64권6호
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• pp.43-53
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• 2022

Rice paddy has been actively converted into upland crop fields as more profitable upland crop cultivation are encouraged along with the decrease in rice consumption. However, the current water supply system remains mainly for paddy water supply, so research on pipeline water supply for upland cultivation is needed. The objective of this study was to optimize storage tank installation locations for pipeline water supply in reservoir irrigation districts. Five of reservoir irrigation districts were selected as the study sites and gridded of 10×10 m in size. Then genetic algorithm was adopted to evaluate the effects of spatial storage tank allocation on total pipeline cost. The lengths of the main and branch pipelines were considered as the objective cost function for the optimization of storage tank installation. Overall the shorter the branch pipeline and the longer the main pipeline, as the number of storage tanks increase. The minimal pipeline cost, i.e., optimal condition was reached when approximately 10% of the storage tank numbers to total upland plots were installed. The methodology presented in this study can be applied to determine the number and spatial arrangement of storage tanks for upland pipeline irrigation system design.

• 상하수도학회지
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• 제32권5호
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• pp.421-434
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• 2018

Water supply/intake pumps operation use 70~80% of power costs in water treatment plants. In the water treatment plant, seasonal and hourly differential electricity rates are applied, so proper pump scheduling can yield power cost savings. Accordingly, the purpose of this study was to develop an optimal water supply pump scheduling scheme. An optimal operation method of water supply pumps by using genetic algorithm was developed. Also, a method to minimize power cost for water supply pump operation based on pump performance derived from the thermodynamic pump efficiency measurement method was proposed. Water level constraints to provide sufficient disinfection performance in a clearwell and reservoirs were calibrated. In addition, continuous operation time constraints were calibrated to prevent frequent pump switching. As a result of optimization, savings ratios during 7 days in winter and summer were 4.5% and 5.1%, respectively. In this study, the method for optimal water pump operation was developed to secure disinfection performance in the clearwell and to save power cost. It is expected that it will be used as a more advanced optimal water pump operation method through further studies such as water demand forecasting and efficiency according to pump combination.

• 한국정보통신학회논문지
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• 제26권7호
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• pp.963-971
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• 2022

상수도 관망은 국가 수도 시설의 주요한 구성 요소이지만 대부분이 지중에 매립되어 있어 배관의 노후화 정도 및 누수를 파악하기 어려우므로 유지관리 하기가 매우 어렵다. 본 연구에서는 관망에 설치된 다양한 센서 조합을 가정하여, 데이터 조합에 따른 관로 누수 판별 가능성을 검토하기 위하여 선형회귀분석, 뉴로퍼지 등의 인공지능 알고리즘을 통한 유량과 압력 예측을 실시하여 최적 알고리즘을 도출하였다. 공급압력 예측을 통한 누수판별의 경우 뉴로퍼지 알고리즘이 선형회귀분석에 비하여 우수하였다. 누수유량 예측에서는 뉴로퍼지를 이용한 유량예측이 우선 고려되어야 한다. 다만, 유량을 모사하기 힘든 경우에는 선형 알고리즘을 이용한 공급압력 예측이 이루어져야 할 것으로 사료 된다.

• 상하수도학회지
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• 제34권5호
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• pp.311-321
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• 2020

In this study, a model to optimize residual chlorine concentrations in a water supply system was developed using a multi-objective genetic algorithm. Moreover, to quantify the effects of optimized residual chlorine concentration management and to consider customer service requirements, this study developed indices to quantify the spatial and temporal distributions of residual chlorine concentration. Based on the results, the most economical operational method to manage booster chlorination was derived, which would supply water that satisfies the service level required by consumers, as well as the cost-effectiveness and operation requirements relevant to the service providers. A simulation model was then created based on an actual water supply system (i.e., the Multi-regional Water Supply W in Korea). Simulated optimizations were successful, evidencing that it is possible to meet the residual chlorine concentration demanded by consumers at a low cost.

• 한국물환경학회지
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• 제37권5호
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• pp.335-343
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• 2021

Water quality prediction is essential for the proper management of water supply systems. Increased suspended sediment concentration (SSC) has various effects on water supply systems such as increased treatment cost and consequently, there have been various efforts to develop a model for predicting SSC. However, SSC is affected by both the natural and anthropogenic environment, making it challenging to predict SSC. Recently, advanced machine learning models have increasingly been used for water quality prediction. This study developed an ensemble machine learning model to predict SSC using the XGBoost (XGB) algorithm. The observed discharge (Q) and SSC in two fields monitoring stations were used to develop the model. The input variables were clustered in two groups with low and high ranges of Q using the k-means clustering algorithm. Then each group of data was separately used to optimize XGB (Model 1). The model performance was compared with that of the XGB model using the entire data (Model 2). The models were evaluated by mean squared error-ob servation standard deviation ratio (RSR) and root mean squared error. The RSR were 0.51 and 0.57 in the two monitoring stations for Model 2, respectively, while the model performance improved to RSR 0.46 and 0.55, respectively, for Model 1.

• 설비공학논문집
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• 제23권5호
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• pp.334-341
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• 2011

In the present study, optimal heat supply algorithm which minimize the heat loss through the distribution pipe line in group energy apartment was developed. Variation of heating load of group energy apartment building in accord with the outdoor air temperature was predicted by the heating load-outdoor temperature correlation. Supply water temperature and mass flow rate were controlled to minimize the heat loss through distribution pipe line. District heating apartment building located in Hwaseong city, which has 1,473 households, was selected as the object building for testing the present heat supply a1gorithm. Compared to the previous heat supply system, 10.4% heat loss reduction can be accomplished by employing the present method.