• Clean Technology
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• v.28 no.1
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• pp.79-93
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• 2022

In this study, an electronics industrial wastewater activated sludge model (e-ASM) to be used as a Water Digital Twin was calibrated based on real high-tech electronics industrial wastewater treatment measurements from lab-scale and pilot-scale reactors, and examined for its treatment performance, effluent quality prediction, and optimal process selection. For specialized modeling of a high-tech electronics industrial wastewater treatment system, the kinetic parameters of the e-ASM were identified by a sensitivity analysis and calibrated by the multiple response surface method (MRS). The calibrated e-ASM showed a high compatibility of more than 90% with the experimental data from the lab-scale and pilot-scale processes. Four electronics industrial wastewater treatment processes-MLE, A2/O, 4-stage MLE-MBR, and Bardenpo-MBR-were implemented with the proposed Water Digital Twin to compare their removal efficiencies according to various electronics industrial wastewater characteristics. Bardenpo-MBR stably removed more than 90% of the chemical oxygen demand (COD) and showed the highest nitrogen removal efficiency. Furthermore, a high concentration of 1,800 mg L^-1 T MAH influent could be 98% removed when the HRT of the Bardenpho-MBR process was more than 3 days. Hence, it is expected that the e-ASM in this study can be used as a Water Digital Twin platform with high compatibility in a variety of situations, including plant optimization, Water AI, and the selection of best available technology (BAT) for a sustainable high-tech electronics industry.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of the Korean Geotechnical Society
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• v.26 no.12
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• pp.19-30
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• 2010

Bentonite-based grout has been widely used to seal a borehole constructed for a closed-loop vertical ground heat exchanger in a geothermal heat pump system (GHP) because of its high swelling potential and low hydraulic conductivity. Three types of bentonites were compared one another in terms of viscosity and thermal conductivity in this paper. The viscosity and thermal conductivity of the grouts with bentonite contents of 5%, 10%, 15%, 20% and 25% by weight were examined to take into account a variable water content of bentonite grout depending on field conditions. To evaluate the effect of salinity (i.e., concentration of NaCl : 0.1M, 0.25M, and 0.5M) on swelling potential of the bentonite-based grouts, a series of volume reduction tests were performed. In addition, if the viscosity of bentonite-water mixture is relatively low, particle segregation can occur. To examine the segregation phenomenon, the degree of segregation has been evaluated for the bentonite grouts especially in case of relatively low viscosity. From the experimental results, it is found that (1) the viscosity of the bentonite mixture increased with time and/or with increasing the mixing ratio. However, the thermal conductivity of the bentonite mixture did not increase with time but increased with increasing the mixing ratio; (2) If bentonite grout has a relatively high swelling index, the volume reduction ratio in the saline condition will be low; (3) The additive, such as a silica sand, can settle down on the bottom of the borehole if the bentonite has a very low viscosity. Consequently, the thermal conductivity of the upper portion of the ground heat exchanger will be much smaller than that of the lower portion.