• Journal of the Korean GEO-environmental Society
• /
• v.22 no.2
• /
• pp.15-23
• /
• 2021

A method of improving the post-management end system of a landfill that reflected total pollutant load was applied to the SUDOKWON 1st Landfill Site. Modeling results showed that the ratio of remaining methane, when compared to the total maximum potential of 2,521 × 106 Nm3, was estimated to be 8.8% in 2020, 7.0% in 2030, and 6.5% in 2040. If the average oxidation rate of 89.1% in 2005-2019 was applied, the ratio decreased by 1.01% in 2020, 0.76% in 2030, and 0.70% in 2040. This suggests that if the amount of methane generated is all emitted from the surface of the landfill after 2025, the real amount emitted to the atmosphere is less than that in 2019; therefore, the post-management end is possible. According to the results of trend analysis of the quality of leachate water, effluent criteria for Biochemical Oxygen Demand (BOD) can be satisfied in 2024, while those for Chemical Oxygen Demand (COD) and Total Nitrogen (T-N) can be satisfied in 2047 and 2117, respectively. If the post-management end system changed based on total pollutant load, the post-management can be terminated BOD today and COD within a few years; however, the fact that T-N could be terminated only after 2041 shows the need to fundamentally change management methods.

• Journal of Korean Society of Environmental Engineers
• /
• v.22 no.10
• /
• pp.1905-1919
• /
• 2000

The main motivation of this research is to develop an intelligent control strategy for Activated Sludge Process (ASP). ASP is a complex and nonlinear dynamic system because of the characteristic of wastewater, the change in influent flow rate, weather conditions, and etc. The mathematical model of ASP also includes uncertainties which are ignored or not considered by process engineer or controller designer. The ASP is generally controlled by a PID controller that consists of fixed proportional, integral, and derivative gain values. The PID gains are adjusted by the expert who has much experience in the ASP. The ASP model based on $Matlab^{(R)}5.3/Simulink^{(R)}3.0$ is developed in this paper. The performance of the model is tested by IWA(International Water Association) and COST(European Cooperation in the field of Scientific and Technical Research) data that include steady-state results during 14 days. The advantage of the developed model is that the user can easily modify or change the controller by the help of the graphical user interface. The ASP model as a typical nonlinear system can be used to simulate and test the proposed controller for an educational purpose. Various control methods are applied to the ASP model and the control results are compared to apply the proposed intelligent control strategy to a real ASP. Three control methods are designed and tested: conventional PID controller, fuzzy logic control approach to modify setpoints, and fuzzy-PID control method. The proposed setpoints changer based on the fuzzy logic shows a better performance and robustness under disturbances. The objective function can be defined and included in the proposed control strategy to improve the effluent water quality and to reduce the operating cost in a real ASP.

• Journal of Korean Society of Environmental Engineers
• /
• v.27 no.12
• /
• pp.1249-1256
• /
• 2005

This study was performed to investigate the efficiency of the corrosion prevention in the simulated distribution system using CCPP(Calcium Carbonate Precipitation Potential) as the anti-corrosive index by adjusting pH, total dissolved solids, alkalinity and calcium hardness in the water treatment pilot process. The materials of the simulated distribution system(SDS) were equiped with same materials of real field water distribution system. CCPP concentrations controlled by $Ca(OH)_2$, $CO_2$ gas and $Na_2CO_3$ in the simulated distribution system and uncontrolled by the chemicals in the general water distribution system were average 0.61 mg/L and -7.77 mg/L. The concentrations of heavy metals like Fe, Zn, Cu ions in effluent water of the simulated distribution system controlled with water quality were decreased rather than the general water distribution system uncontrolled with water quality. In simulated distribution system(SDS), corrosion prevention film formed by CCPP control was observed that scale was come into forming six months later and it was formed into density as time goes on. We were analyzed XRD(X-ray diffraction) for investigating component of crystal compounds and structure for galvanized steel pipe(15 mm). Finding on analysis, scale was compounded to $Zn_4CO_3(OH)_6{\cdot}H_2O$ (Zinc Carbonate Hydroxide Hydrate) after ten months late, and it was compounded on $CaCO_3$(Calcium Carbonate) and $ZnCO_3$(Smithsonite) after nineteen months later.

• Clean Technology
• /
• v.28 no.1
• /
• pp.63-78
• /
• 2022

Electronics industrial wastewater treatment facilities release organic wastewaters containing high concentrations of organic pollutants and more than 20 toxic non-biodegradable pollutants. One of the major challenges of the fourth industrial revolution era for the electronics industry is how to treat electronics industrial wastewater efficiently. Therefore, it is necessary to develop an electronics industrial wastewater modeling technique that can evaluate the removal efficiency of organic pollutants, such as chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), and tetramethylammonium hydroxide (TMAH), by digital twinning an electronics industrial organic wastewater treatment facility in a cyber physical system (CPS). In this study, an electronics industrial wastewater activated sludge model (e-ASM) was developed based on the theoretical reaction rates for the removal mechanisms of electronics industrial wastewater considering the growth and decay of micro-organisms. The developed e-ASM can model complex biological removal mechanisms, such as the inhibition of nitrification micro-organisms by non-biodegradable organic pollutants including TMAH, as well as the oxidation, nitrification, and denitrification processes. The proposed e-ASM can be implemented as a Water Digital Twin for real electronics industrial wastewater treatment systems and be utilized for process modeling, effluent quality prediction, process selection, and design efficiency across varying influent characteristics on a CPS.

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