SeungChul Lee
Kyung Hee University
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Publication
Featured researches published by SeungChul Lee.
Clean Technologies and Environmental Policy | 2016
Minhyun Kim; MinJeong Kim; SeHee Pyo; SeungChul Lee; Payam Ghorbannezhad; Dominic Chwan Yee Foo; ChangKyoo Yoo
Wastewater treatment plants (WWTPs) are one of the important sources of greenhouse gas (GHG) emission. There is a need for systematical tool that can be used to analyze GHG emission from WWTPs, and to evaluate the associated reduction strategies. In this paper, a systematic analysis methodology, called the greenhouse emission pinch analysis (GEPA), is developed for this purpose. GEPA is graphical in nature, and can be used to analyze the on-site and off-site GHG emissions of the WWTP. Furthermore, three GHG reduction strategies, i.e., increased aeration capacity, external carbon source controller, and reuse of biogas, are evaluated for their environmental load and operational cost reduction using the GEPA. A case study is used to elucidate the proposed method. In this study, the third strategy which reuses biogas from anaerobic digestion shows the largest reduction of GHG emissions.
Desalination and Water Treatment | 2012
TaeSuk Oh; Hongbin Liu; MingJung Kim; SeungChul Lee; Min-Kyeong Yeo; ChangKyoo Yoo
Abstract In general, the operation conditions of water treatment plants happen to be affected by external environmental variations such as temperature, viscosity, and loading changes. They sometimes result in bad treatment performance due to fouling or sludge decay and some process faults. Therefore, when designing a process model, negative effects of the external variables are needed to be incorporated. The purposes of this study are to propose a new external fuzzy partial least squares method (eFPLS) and apply it to predict the treatment performance of a pilot-scale membrane bioreactor (MBR). The proposed eFPLS model can represent an interpretability of the original FPLS of the inner and outer relationship with the viewpoint of physical meaning as well as keeping the capability of the original FPLS with handling the nonlinear correlation between inputs and outputs, while incorporating operation condition changes by the external analysis. It was used to predict the transmembrane pressure and the removal ...
Indoor and Built Environment | 2014
Hongbin Liu; SeungChul Lee; MinJeong Kim; Honglan Shi; Jeong Tai Kim; ChangKyoo Yoo
The aim of this study was to propose a new multi-objective optimization (MOO) of a ventilation controller which finds the optimal set points for simultaneously improving the indoor air quality (IAQ) and increasing the energy efficiency in buildings under changes in outdoor air quality and climate change. The outdoor weather information, such as ambient temperature, humidity, solar radiation and wind speed, was applied and treated as external disturbances in the building system. Two control strategies including proportional-integral (PI) control and multivariate model predictive control (MPC) were implemented and compared while controlling the indoor air temperature and CO2 concentration in the targeted building system. A control performance assessment (CPA) technique was proposed and implemented for monitoring the MPC controller performance. With the goal of determining the optimal set points for the MPC controller, the multi-objective genetic algorithm was developed to enhance the energy consumption as well as to keep the IAQ within an acceptable range. The results indicate that the performance of the MPC controller with the optimized set points is superior to that of the PI controller in indoor building systems. More specifically, the MPC controller with the optimal set points for the indoor temperature and CO2 control could reduce energy consumption by 5.22% and CO2 content by 13.39% in comparison to the PI controller. In addition, the MPC controller equipped with MOO could be useful for building climate control depending on the variation of the outdoor air pollutants.
Korean Journal of Chemical Engineering | 2013
OnYu Kang; SeungChul Lee; Kailas L. Wasewar; MinJeong Kim; Hongbin Liu; TaeSeok Oh; Emad Janghorban; ChangKyoo Yoo
As the importance of watershed management has emerged for water systems, non-point pollutant sources have been blamed as the main problem of water pollution. To control non-point pollutant sources, it is necessary to monitor sewers connected to the watershed and to analyze their effects on the sewer network. As the cost to monitor a sewer network depends on the number of sensors installed, the monitoring stations should be decided with proper guide of the installation location rule. In present paper, a new method to select the proper sensor location is proposed by combining monitoring information with data mining techniques. To estimate the amount of pollutants by wash-off and to find the sensor locations in a sewer network, three scenarios are considered based on rainfall intensity, influent concentrations and flow rate. The optimal locations of the sensor were selected based on the proposed method to facilitate the management of non-point pollutant source in sewer network. The presented approach can be extended to a complex sewer network system to design a minimum number of sensors and optimum locations for the sensors.
Indoor and Built Environment | 2013
Hongbin Liu; OnYu Kang; MinJeong Kim; TaeSeok Oh; SeungChul Lee; Jeong Tai Kim; ChangKyoo Yoo
The purpose of this study is to develop a self-validating soft sensor to improve the prediction performance of indoor air quality soft sensors in an underground subway station. The reconstruction-based self-validation method was proposed and implemented in order to: (1) determine the optimal number of principal components when building a principal component analysis training model, (2) enhance the diagnosis accuracy when identifying faulty sensors and (3) reconstruct faulty measurements in a straightforward manner. Two soft sensors based on partial least squares and recursive partial least squares models were developed and their prediction performance was compared in the cases of using faulty sensor measurements and using reconstructed sensor values. Two types of sensor faults including a bias fault and a drifting fault were evaluated using the proposed method. The monitoring results show that the developed sensor self-validation strategy has a powerful ability to correctly detect, identify and reconstruct the sensor faults in the subway system. In addition, the proposed self-validation soft sensing technique could achieve sustainable monitoring of indoor air pollutants in the underground subway environment, because the reconstructed values can be used to replace the measured data when sensor faults have been detected by the detection indices.
Korean Journal of Chemical Engineering | 2015
Honglan Shi; MinJeong Kim; SeungChul Lee; SeHee Pyo; Iman Janghorban Esfahani; ChangKyoo Yoo
Indoor air quality (IAQ) in subway systems shows periodic dynamics due to the number of passengers, train schedules, and air pollutants accumulated in the system, which are considered as an engineering big data. We developed a new IAQ monitoring model using a sub-principal component analysis (sub-PCA) method to account for the periodic dynamics of the IAQ big data. In addition, the IAQ data in subway systems are different on the weekdays and weekend due to weekly effect, since the patterns of the number of passengers and their access time on the weekdays and weekend are different. Sub-PCA-based local monitoring was developed for separating the weekday and weekend environmental IAQ big data, respectively. The monitoring results for the test data at the Y-subway station clearly showed that the proposed method could analyze an environmental IAQ big data, improve the monitoring efficiency and greatly reduce the false alarm rate of the local on-line monitoring by comparison with the multi-way PCA.
Environmental Technology | 2015
SeungChul Lee; Sankara Rao; MinJeong Kim; Iman Janghorban Esfahani; ChangKyoo Yoo
Environmental plants are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function, where the measurements and flow rate equipment in plants must be mutually consistent. The aim of this study is to detect any error in the measured data in an environmental plant and reconcile the data with some gross errors by using a closed data reconciliation of mass balance and the Lagrange multiplier method. A data reconciliation method based on closed-loop mass balance is suggested in order to reduce or remove error within data and obtain reliable process data. The proposed method is applied to a full-scale plant to detect the gross error in measured data, investigate the effects of erroneous data on modelling errors and compare the modelling performances of the faulty data and reconciled data. The results show that the proposed method can efficiently detect any gross error in data, estimate the error-free data by a reconciliation method and enhance the modelling accuracy by using reconciled data. This study provides a simple way to incorporate prior knowledge of plant modelling of a closed-loop mass balancing to identify any gross error and reconcile the faulty measurements.
Science of The Total Environment | 2018
Paulina Vilela; Hongbin Liu; SeungChul Lee; Soonho Hwangbo; KiJeon Nam; ChangKyoo Yoo
The release of silver nanoparticles (AgNPs) to wastewater caused by over-generation and poor treatment of the remaining nanomaterial has raised the interest of researchers. AgNPs can have a negative impact on watersheds and generate degradation of the effluent quality of wastewater treatment plants (WWTPs). The aim of this research is to design and analyze an integrated model system for the removal of AgNPs with high effluent quality in WWTPs using a systematic approach of removal mechanisms modeling, optimization, and control of the removal of silver nanoparticles. The activated sludge model 1 was modified with the inclusion of AgNPs removal mechanisms, such as adsorption/desorption, dissolution, and inhibition of microbial organisms. Response surface methodology was performed to minimize the AgNPs and total nitrogen concentrations in the effluent by optimizing operating conditions of the system. Then, the optimal operating conditions were utilized for the implementation of control strategies into the system for further analysis of enhancement of AgNPs removal efficiency. Thus, the overall AgNP removal efficiency was found to be slightly higher than 80%, which was an improvement of almost 7% compared to the BSM1 reference value. This study provides a systematic approach to find an optimal solution for enhancing AgNP removal efficiency in WWTPs and thereby to prevent pollution in the environment.
Korean Journal of Chemical Engineering | 2017
Minhyun Kim; Dongwoo Kim; Iman Janghorban Esfahani; SeungChul Lee; MinJeong Kim; ChangKyoo Yoo
We propose a systematic approach for performance evaluation and improvement of a combined cycle power plant (CCPP). Exergoeconomic and exergoenvironmental analyses are used to assess CCPP performance and suggest improvement potentials in economic and environmental aspects, respectively. Economic and environmental impacts of individual system components are calculated by cost functions and life cycle assessments. Both analyses are based on a CCPP case study located in Turkey, which consists of two gas turbine cycles and a steam turbine cycle with two different pressure heat recovery units. The results of the exergoeconomic analysis indicate that the combustion chamber and condenser have a high performance improvement potential by increasing capital cost. Furthermore, the exergoenvironmental analysis shows that the exergy destruction of the steam turbine and combustion chamber and/or the capacity of heat recovery units must be reduced in order to improve environmental performance. This study demonstrates that combined exergoeconomic and exergoenvironmental analyses are useful for finding improvement potentials for system optimization by simultaneously evaluating economic and environmental impacts.
Korean Journal of Chemical Engineering | 2017
KiJeon Nam; MinJeong Kim; SeungChul Lee; Soonho Hwangbo; ChangKyoo Yoo
Fouling is a principal constraint of membrane bioreactors (MBRs). It blocks the wide use of MBRs and aggravates the ability of MBRs. Trans-membrane pressure (TMP) is measured simply from MBRs and is a useful factor for evaluating fouling phenomena such as fouling mechanisms. Fouling mechanism diagnosis based on a measured TMP was used to evaluate MBRs operation conditions. However, diagnosis of MBR conditions is difficult due to the dynamic conditions of MBRs. Therefore, we used differential calculus, exponential weighted moving average (EWMA) and fast Fourier transform (FFT) to determine a periodic pattern for diagnosing fouling mechanisms in the dynamic operating conditions of MBRs. The periodic pattern was reflected in the operating conditions of MBRs, based on the fouling mechanism using TMP. We used two data sets obtained from pilot-scale MBR to suggest a periodic pattern and validated the proposed method using a lab-scale MBR experiment. Consequently, the suggested periodic pattern can diagnose fouling mechanisms using the proposed method, because the methods can be adjusted under the dynamic conditions of MBRs.