Hyosoo Kim

Directly applicable microbial fuel cells in aeration tank for wastewater treatment

The application of microbial fuel cell (MFC) for wastewater treatment is a promising strategy for the simultaneous treatment of pollutants and generation of electricity. However, for practical application, there are several limitations to the MFC that involve biological and engineering aspects. In this study, a single-chambered MFC able to submerge into the aeration tank of the activated sludge process was developed to optimize the cell configuration and electrode materials. Among four MFCs with different electrode materials, the MFC with a graphite felt (GF) anode and a GF cathode showed the highest power density of 16.7 W m(-3) and the lowest internal resistance of 17 Omega. When the blower was stopped to evaluate the effect of mixing intensity, the concentration of dissolved oxygen nevertheless remained at 8 mg O2 L(-1), and the cell voltage of MFCs dropped rapidly and reached 30 mV. However, the cell voltage immediately returned to around 200 mV after the blowing of air. The MFCs with a GF cathode were sensitive to mixing intensity. At the very low concentration of 0.2 mg O2 L(-1), the cell voltage remained at a high level of 200 mV when the oxygen close to the cathode remained and mixing was sufficient.

Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

The k-nearest neighbor (k-NN) method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand (COD), suspended solid (SS), total nitrogen (T-N) and total phosphorus (T-P) at a wastewater treatment plant (WWTP). The search range and approach for determining the number of nearest neighbors (NNs) under dry and wet weather conditions were initially optimized based on the root mean square error (RMSE). The optimum search range for considering data size was one year. The square root-based (SR) approach was superior to the distance factor-based (DF) approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error (MAPE) under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.

Enhanced monitoring of water quality variation in Nakdong River downstream using multivariate statistical techniques

AbstractThe variation in downstream river water quality was investigated using three multivariate statistical techniques: factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA). Four main factors (FA1, FA2, FA3, and FA4) were defined as changes of “organic matter and nitrogen,” “suspended solid and climate conditions,” “phosphorous and electrical conductivity,” and “discharge,” respectively. The states of each factor were clustered into Low, Normal (Normal_low and Normal_high), and High groups using CA. These groups used to summarize water quality data measured as a series of numbers of contaminants for fast evaluation of water quality and enhanced monitoring capability. To set up a procedure for enhanced monitoring of water quality, Fisher’s linear discriminant functions were deduced to determine the groups in which newly obtained water quality data should be included. To investigate the effectiveness of the proposed tool for enhanced monitoring of river water quality, a case study w...

Performance evaluation of a full-scale advanced phase isolation ditch process by using real-time control strategies

This paper proposes real-time control strategies that can be applied in a full-scale advanced phase isolation ditch (APID) process. Real-time operation mode control (OMC) and aeration section control (ASC) strategies were developed to cope more stably with fluctuations in the influent loading and to increase the nitrification and denitrification reactions within the entire volume. The real-time OMC and ASC strategies were evaluated using mathematical models. When the NH4-N in the reactor was maintained at a high level, appropriate control actions, such as continuing the aeration state, stopping the influent inflow and increasing the aeration section, were applied in the APID process. In contrast, when the NOX-N in the reactor was maintained at a high level, the non-aeration state, influent inflow, and decreased aeration section were continued. It was concluded that stable operation in the APID process could be achieved by applying real-time OMC and ASC strategies developed in this study.

Application of real-time feedback control strategies based on effluent NH4-N and NOX-N concentrations in an A2/O process

This paper proposes two real-time feedback control strategies based on hourly measurements of effluent NH4-N and NOX-N concentrations. Using modified sigmoid functions to decide the DO setpoint, a control structure similar to the cascade-type control loop was selected as the real-time feedback NH4-N control strategy. For the realtime feedback NOX-N control strategy, a proper external carbon dose flow rate could be calculated based on the estimated NOX-N concentration in anoxic reactor by using the empirical equation. A control system, which included two proposed control strategies, was developed and applied in the pilot-scale A2/O process. As a result, the effluent NH4-N and NOX-N concentrations were maintained stably lower than the target values of 3 and 5 mg/L, respectively. Moreover, because the manipulated variables for removing the NH4-N and NOX-N concentrations were divided in the control strategies, the two different control strategies could be successfully applied together in the A2/O process.

Development and Evaluation of Model-based Predictive Control Algorithm for Effluent

In this study, model-based NH4-N predictive control algorithm by using influent pattern was developed and evaluated for effective control application in A/O process. A pilot-scale A/O process at S wastewater treatment plant in B city was selected. The behaviors of organic, nitrogen and phosphorous in the biological reactors were described by using the modified ASM3+Bio-P model. A one-dimensional double exponential function model was selected for modeling of the secondary settlers. The effluent NH4-N concentration on the next day was predicted according to model-based simulation by using influent pattern. After the objective effluent quality and simulation result were compared, the optimal operational condition which able to meet the objective effluent quality was deduced through repetitive simulation. Next the effluent NH4-N control schedule was generated by using the optimal operational condition and this control schedule on the next day was applied in pilot-scale A/O process. DO concentration in aerobic reactor in predictive control algorithm was selected as the manipulated variable. Without control case and with control case were compared to confirm the control applicability and the study of the applied NH4-N control schedule in summer and winter was performed to confirm the seasonal effect. In this result, the effluent NH4-N concentration without control case was exceeded the objective effluent quality. However the effluent NH4-N concentration with control case was not exceeded the objective effluent quality both summer and winter season. As compared in case of without predictive control algorithm, in case of application of predictive control algorithm, the RPM of air blower was increased about 9.1%, however the effluent NH4-N concentration was decreased about 45.2%. Therefore it was concluded that the developed predictive control algorithm to the effluent NH4-N in this study was properly applied in a full-scale wastewater treatment process and was more efficient in aspect to stable effluent.

NH_4-N

In wastewater treatment plants (WWTPs), the portion of operating costs related to electric power consumption is increasing. If the electric power consumption decreased, however, it would be difficult to comply with the effluent water quality requirements. A protocol was proposed to minimize the environmental impacts as well as to optimize the electric power consumption under the conditions needed to meet the effluent water quality standards in this study. This protocol was comprised of six phases of procedure and was tested using operating data from S-WWTP to prove its applicability. The 11 major operating variables were categorized into three groups using principal component analysis and K-mean cluster analysis. Life cycle assessment (LCA) was conducted for each group to deduce the optimal operating conditions for each operating state. Then, employing mathematical modeling, six improvement plans to reduce electric power consumption were deduced. The electric power consumptions for suggested plans were estimated using an artificial neural network. This was followed by a second round of LCA conducted on the plans. As a result, a set of optimized improvement plans were derived for each group that were able to optimize the electric power consumption and life cycle environmental impact, at the same time. Based on these test results, the WWTP operating management protocol presented in this study is deemed able to suggest optimal operating conditions under which power consumption can be optimized with minimal life cycle environmental impact, while allowing the plant to meet water quality requirements.