• Coupled systems mechanics
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• v.11 no.1
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• pp.79-91
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• 2022

Models for water treatment processes include simulation, i.e., modelling of water quality, flow hydraulics, process controls and design. Water treatment processes are inherently dynamic because of the large variations in the influent water flow rate, concentration and composition. Moreover, these variations are to a large extent not possible to control. Mathematical models and computer simulations are essential to describe, predict and control the complicated interactions of the water treatment processes. An accurate description of such systems can therefore result in highly complex models, which may not be very useful from a practical, operational point of view. The main objective is to combine knowledge of the process dynamics with mathematical methods for processes estimation and identification. Good modelling practice is way to obtain this objective and to improve water treatment processes(its understanding, design, control and performance- efficiency). By synthesize of existing knowledge and experience on good modelling practices and principles the aim is to help address the critical strategic gaps and weaknessesin water treatment models application.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.369-375
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• 2008

To achieve zero-emission, one of the main goals of an eco-industrial park (EIP), it is needed to develop an effective water exchange network. The network includes various subsystems and decision making processes, which make the modeling process extremely complicated. Agent-based modeling was used to simulate water exchange network in an EIP. Firm agents were created based on the behavior pattern of firms, and an agent-based model (ABM) was made with the agents, showing the growth of the exchange network. An existing steel and iron making industrial park was chosen as a case study, and the ABM model shows eco-efficient behavior with a decreased environmental cost. Water reuse network based on the ABM model results in 35% decrease of the fresh water supply and 50% reduction of the wastewater generation in EIP. A case study shows that agent-based model can be a powerful tool in modeling and designing complex eco-industrial parks, especially when a part of the system needs to be changed.

• Journal of The Korean Society of Agricultural Engineers
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• v.56 no.6
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• pp.129-137
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• 2014

This research presents an streamflow modeling approach in a data-scarce estuary reservoir watershed which has been suffered from high salinity irrigation water problem after completion of land reclamation project in South Korea. Since limited hydrology data was available on the Iwon estuary reservoir watershed, water balance relation of the reservoir was used to estimate runoff from upstream of the reservoir. Water balance components in the reservoir consists precipitation, inflow from upstream, discharge through sluice, and evaporation. Estimated daily inflow data, which is stream discharge from upstream, shows a good consistency with the observed water level data in the reservoir in terms of EI (0.93) and $R^2$ (0.94), and were used as observed flow data for the streamflow modeling. HSPF (Hydrological Simulation Program - Fortran) was used to simulate hydrologic response of upstream of the reservoir. The model was calibrated and validated for the periods of 2006 to 2007 and 2008 to 2009, respectively, showing that values of EI and $R^2$ were 0.89 and 0.91 for calibration period, 0.71 and 0.84 for validation period.

• Journal of Korean Society on Water Environment
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• v.31 no.2
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• pp.181-195
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• 2015

Algal blooms in lakes are one of major environmental issues in Korea. A three-dimensional, hydrodynamic and water quality model was developed and tested in Lake Euiam to assess the performance and limitations of numerical modeling with multiple algal groups using field data commonly collected for algal management. In this study, EFDC was adopted as the basic model framework. Simulated vertical profiles of water temperature, dissolved oxygen and nutrients monitored at five water quality monitoring stations from March to October 2013, which are closely related to algal dynamics simulation, showed good agreement with those of observed data. The overall spatio-temporal variations of three algal groups were reasonably simulated against the chlorophyll-a levels of those estimated from the limited monitoring data (chlorophyll-a level and cell numbers of algal species) with the RMSEs ranging from 2.6 to $17.5mg/m^3$. Also, note that $PO_4-P$ level in the water column was a key limiting factor controlling the growth of three algal groups during most of simulation period. However, the algal modeling results were not fully attainable to the levels of observation during short periods of time showing abrupt increase in algae throughout the lake. In particular, the green algae/cyanobacteria and diatom simulations were underestimated in late June to early July and early October, respectively. The results shows that better understanding of internal algal processes, neglected in most algal modeling studies, is necessary to predict the sudden algal blooms more accurately because the concentrations of external $PO_4-P$ and specific algal groups originated from the tributaries (mainly, dam water releases) during the periods were too low to fully capture the sharp rise of internal algal levels. In this respect, this study suggests that future modeling efforts should be focused on the quantification of internal cycling processes including vertical movement of algal species with respect to changes in environmental conditions to enhance the modeling performance on complex algal dynamics.

• Water for future
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• v.30 no.1
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• pp.74-87
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• 1997

• Water for future
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• v.30 no.4
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• pp.46-54
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• 1997

• Journal of Korean Society on Water Environment
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• v.32 no.3
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• pp.243-252
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• 2016

In cases of water pollution accidents, accurate prediction for arrival time and concentration of contaminants in a river is essential to take proper measures and minimize their impact on downstream water intake facilities. It is critical to fully understand the behavior characteristics of contaminants on river surface, especially in case of oil spill accidents. Therefore, in this study, the effects of main parameters of advection and diffusion of contaminants were analyzed and validated by comparing the results of Lagrangian particle tracking (LPT) simulation of Environmental Fluid Dynamic Code (EFDC) model with those of Global Position System (GPS)-equipped drifter experiment. Prevention scenario modeling was accomplished by taking cases of movable weir operation into account. The simulated water level and flow velocity fluctuations agreed well with observations. There was no significant difference in the speed of surface particle movement between 5 and 10 layer modeling. Therefore, 5 layer modeling could be chosen to reduce computational time. It was found that full three dimensional modeling simulated wind effects on surface particle movements more sensitively than depth-averaged two dimensional modeling. The diffusion range of particles was linearly proportional to horizontal diffusivity by sensitivity analysis. Horizontal diffusivity estimated from the results of GPS-equipped drifter experiment was 0.096 m²/sec, which was considered to be valid for applying the LPT module in this area. Finally, the scenario analysis results showed that particle movements could be stagnant when discharge from the upstream weir was reduced, implying the possibility of securing time for mitigation actions such as oil boom installation and wiping oil contaminants. The outcomes of this study can help improve the prediction accuracy of particle tracking simulation to establish the most suitable mitigation plan considering the combination of movable weir operation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.9
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• pp.355-360
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• 2016

Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be used in prediction of heating energy usage. Similar, but more severely, to heating load, hot water supply load varies in highly nonlinear manner. Such nonlinearity makes analytical model of it hardly exist in the literatures. LS-SVM is known as a good modeling tool for the system, especially for the nonlinear system depended by many independent factors. We collect 26,208 data of hot water supply load over a 13-week period in winter time, from 12 heat exchangers in seven different apartments. Then part of the collected data were used to construct LS-SVM based model and the rest of those were used to test the formed model accuracy. In modeling, we first constructed the model of district heating system's hot water supply load, using the unit heating area's hot water supply load of seven apartments. Such model will be used to estimate the total hot water supply load of which the district heating system needs to provide. Then the individual apartment hot water supply load model is also formed, which can be used to predict and to control the energy consumption of the individual apartment. The results obtained show that the total hot water supply load, which will be provided by the district heating system in winter time, can be predicted within 10% in MAPE (mean absolute percentage error). Also the individual apartment models can predict the individual apartment energy consumption for hot water supply load within 10% ~ 20% in MAPE.

• Journal of Environmental Health Sciences
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• v.28 no.2
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• pp.61-69
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• 2002

Oship stream is located nearby south eastern coasts. This study was performed to find out waters quality modeling and then to predict water quality of Oship stream. Based on survey data, BOD, T-N, T-P calibration and verification result were in good agreement with measured value within mean coefficient variance(MSE) value, which were 13.9%, 9.0%, 26.5% and 19.5%, 12.0%, 16.5%, respectively. Sectional water quality predictions of the main stream of Oship stream are executed on the basis of the following cases 1) with sewage treatment of Dogye-eup 2) reduction of mine wastewater treatment of 80% in th basin. As a result, BOD, T-P improvement rates at down stream of Oship stream, case 1) were appeared 12.2%, 22.2%, case 2) maximum sulfate ion and conductivity reduction removal rate of Oship stream were 58%, 68%. The main pollution sources of Oship-stream were almost domestic wastewater and mine wastewater discharged from Dogye-eup which located in uppers stream. The large effects will appear after the construction of Dogye sewage water treatment plant which remove the organic matter and nutrients in these sewage water. The waste water from mine can not easily to treat for characteristics of effluence and economic problems. However, to achieve the goal of water quality in Oship-stream water system, treatments of those are necessary.

• Advances in environmental research
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• v.4 no.3
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• pp.173-181
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• 2015

This article outlines a case study of water and energy savings in a typical building through a modelling process and analysis of simultaneous water-energy saving measures. Wet cooling towers are one of the most important equipments in buildings with a considerable amount of water and energy consumption. A variety of methods are provided to reduce water and energy consumption in these facilities. In this paper, thorough the modeling of a typical building, water and energy consumption are measured. Then, After application of modern methods known to be effective in saving water and energy, including the ozone treatment for cooling towers and shade installation for windows, i.e. fins and overhangs, the amount of water and energy saving are compared with the base case using the Simergy model. The annual water consumption of the building, by more than 50% reduction, has been reached to 500 cubic meters from 1024 cubic meters. The annual electric energy consumption has been decreased from 405,178 kWh to 340,944 kWh, which is about 16%. After modeling, monthly peak of electrical energy consumption of 49,428 has dropped to 40,562 kWh. The reduction of 18% in the monthly peak can largely reduce the expenses of electricity consumption at peak.