OnYu Kang

Sensor Validation for Monitoring Indoor Air Quality in a Subway Station

Indoor air quality (IAQ) is an important factor, which can influence the health and comfort of passengers in subway stations. Various types of hazardous pollutants, such as particulate matters, remain accumulated in the subway space due to overcrowding and inadequate ventilation system. Subway stations are extremely crowded during rush hours and indoor air of the subway stations could be strongly affected, which in turn, affects passengers’ respiratory system. In this study, several key air pollutants data were collected every minute by the air sampler and tele-monitoring system (TMS) to effectively monitor and control IAQ in subway stations. The quality of the online measurement could decide the failure and success in environmental process assessment. Therefore, prompt detection of the occurrence of sensor faults and identification of those locations are of primary importance for efficient monitoring and control of IAQ. In this paper, Principal components analysis based approach is used to detect, identify and reconstruct the sensor faults in monitoring IAQ. Four types of sensor failures, namely, bias, drifting, complete failure and precision degradation are tested for monitoring IAQ. Several test results of a real subway TMS showed that the developed sensor validation technique can work well for the four kinds of sensor faults.

Parametric analysis and optimization of combined gas turbine and reverse osmosis system using refrigeration cycle

Abstract This study proposes a systematic approach to analyzing and optimizing combined gas turbine (GT) and reverse osmosis (RO) systems. Two systems combining RO to produce freshwater and a GT power plant to generate the required power for the RO system were modeled. In the first system, the coupling between the RO and the power plant was only mechanical; while in the second system, the coupling was both mechanical and thermal, using a refrigeration cycle. The effects of seawater temperature and intake air temperature on the freshwater production of the systems were investigated and their optimal values were calculated. Economic modeling was applied in order to calculate the unit product cost of freshwater. The second system, with two RO units under optimal operation conditions, can increase freshwater production by 26% and save 21% in the production cost of 1 m3of freshwater as compared to the first system as a base system.

Periodic Local Multi-way Analysis and Monitoring of Indoor Air Quality in a Subway System Considering the Weekly Effect

Indoor air quality (IAQ) is one of the major concerns to people who spend most of the time in an indoor environment, as many hazardous pollutants are emitted from the buildings or underground spaces, or enter from outside sources through the various vents and other entry/exit points. In particular, the IAQ in subway stations have periodic variations in indoor air pollutants, depending on the numbers of passengers and trains, as well as the previous IAQ. Global and weekly models (divided into weekday and weekend model) have been developed and compared with one another using parallel factor analysis to consider the periodic characteristics, as IAQ shows changeable aspects within a day or the week, owing to differences in the passengers’ travel patterns. The IAQ monitoring results of the weekly model showed that the proposed monitoring method could detect abnormal IAQ conditions in a timely manner when compared to the global model. The proposed monitoring method could identify not only the most active time periods but also the main contaminant sources resulting in the abnormality of the IAQ, based on fault detection.

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations.

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover, the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO2, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well. Implications: Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO2 pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.

Determination of key sensor locations for non-point pollutant sources management in sewer network

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.

Sustainable Monitoring of Indoor Air Pollutants in an Underground Subway Environment Using Self-Validating Soft Sensors

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.

A systematic model calibration methodology based on multiple errors minimization method for the optimal parameter estimation of ASM1

A one-step model calibration methodology of the activated sludge model no. 1 (ASM1) of a full-scale wastewater treatment plant (WWTP) is proposed. First, the key parameters among all parameters of the ASM1 model are selected by sensitivity analysis based on the effluent quality index. Second, multiple response surface methodology (MRSM) is conducted to find the optimal parameter values of the ASM1 model. Lastly, an MRSM analysis is conducted in order to determine the optimal parameter values. This study was conducted in order to develop a new systematic model calibration methodology that can greatly help the modeler to find the optimal solution by selecting the key parameters and optimizing the parameters. In two case studies of simple activated sludge process and a full-scale plant, the experimental results indicated that the calibrated models can improve the prediction quality of the ASM model and the efficiency of the modeling.

Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

A new key variable selection and prediction model of IAQ that can select key variables governing indoor air quality (IAQ), such as PM10, CO2, CO, VOCs and formaldehyde, are suggested in this paper. The essential problem of the prediction model is the question of which of the original variables are the most important for predicting IAQ. The next issue is determining the number of key variables that should be ranked. A new index of discriminant importance in the projection (DIP) of Fisher’s linear discriminant (FLD) is suggested for selecting key variables of the prediction models with multiple linear regression (MLR) and partial least squares (PLS), as well as for ranking the importance of input measurement variables on IAQ prediction. The prediction models were applied to a real IAQ dataset from telemonitoring data (TMS) in a metro system. The prediction results of the model using all variables were compared with the results of the model using only key variables of DIP. It shows that the use of our new variable selection method cannot only reduce computational effort, but will also enhance the prediction performances of the models.

Estimation of nitrous oxide emissions (GHG) from wastewater treatment plants using closed-loop mass balance and data reconciliation

The amount of greenhouse gases (GHG), especially, nitrous oxide (N2O) emitted from wastewater treatment plants (WWTP) using data reconciliation and closed-loop mass balance was estimated. This study is based on a flowbased emission estimation approach which depends on the accuracy of the measurement data. To reduce the (random) measurement error, data reconciliation was used to enhance the accuracy of the flow measurements. After performing data reconciliation, N2O emission was estimated with more precision by using the closed-loop mass balance. The results in both pilot-scale and full-scale plants show that the suggested method can easily obtain the precise flow measurement for GHG emission, which in turn, results in the accurate estimation of the N2O amounts emitted from WWTP. Moreover, it is shown that the estimated flowrate values can be used as a software sensor, which can replace the faulty sensors and can validate the existing field measurements.