Heekyung Park

Characterization of humic substances present in landfill leachates with different landfill ages and its implications

Humic and fulvic acids extracted from landfill leachates were characterized using elemental analysis and various spectroscopic methods. Molecular size distribution of the humic substances (HS) was also determined using batch ultrafiltration technique and permeation coefficient model. The element analysis and spectral features obtained from UV/visible, IR, and 1H and 13C NMR showed that the aromatic character in the leachate HS was relatively lower than that of commercial humic acid (Aldrich Co.), and higher in the HS of older landfill leachate. Fluorescence spectra indicated that humic acids had a relatively higher content of condensed aromatic compounds than the fulvic acids obtained from the same sources, and the spectrum of commercial humic acid showed that aromatic compounds may be present in a much more condensed and complex form. Molecular size distribution data revealed that the leachate humic acids contained a higher percentage of smaller molecules of < 10,000 D, compared with that of the commercial humic acid (45 approximately 49% vs. 33%), and molecular size of the leachate HS had a tendency to increase as landfill age increased. These results indicate that the HS from landfill leachates were in an early stage of humification, and the degree of humification increased as the landfilling age increased, which implies important information on various related researches, such as interactions of HA with pollutants in terrestrial environments, and optimization of leachate treatment processes with respect to landfill age.

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Transformation of the fungicide cyprodinil by a laccase of Trametes villosa in the presence of phenolic mediators and humic acid

Xenobiotic chemicals can be transformed or covalently bound to humic materials by oxidoreductive enzymes present in terrestrial systems. Chemicals that are not substrates for oxidoreductive enzymes may undergo transformation in the presence of certain reactive compounds, which are often referred to as mediators. In this study, cyprodinil, a broad-spectrum fungicide, did not show any transformation when incubated alone with a laccase from Trametes villosa. It was transformed to a significant extent, however, when a mediator was present. All of the 13 tested mediators belonged to the group of naturally occurring phenols. With some exceptions (2,6-dimethoxyphenol, syringic acid, and ferulic acid), phenols substituted with one or two methoxy groups were very effective mediators. In experiments with 14C-labeled cyprodinil, the radioactive label was largely associated with brown transformation products that precipitated out of the aqueous solution. As determined by mass spectrometry, the products were mixed oligomers resulting from cross-coupling between cyprodinil and a mediator. The addition of large amounts of humic acid (HA) (400 mg/L) to the reaction mixtures involving the most effective mediators reduced cyprodinil transformation (42.6-68.6%) by 12-48%, probably due to an inhibitory effect. The inhibition decreased with decreasing concentration of HA. The addition of HA (400 mg/L) to the reaction mixtures involving the least effective mediators or no mediators (control) enhanced cyprodinil transformation (0.3-17.6%) by 2.9-17.1%, probably as a result of binding to HA.

The effects of data preprocessing in the determination of coagulant dosing rate

The artificial neural network (ANN) is known as an excellent estimator of nonlinear relationships between accumulated input and output numerical data. Using this nature of the ANN, the optimal coagulant dosing rate can be predicted from the operating data with accuracy and in time. But, the accumulated operating data used in ANN training may have some corrupt and noisy data records. So, to enhance the reliability of the trained ANN, a data preprocessing method is necessary for preparing the train and test data set. In this study, a data preprocessing method was devised, four data sets were prepared using the proposed data preprocessing method and the prediction capabilities of the ANN by each data set were compared in terms of a root-mean-square normalized error (RMSE). The purpose of the data preprocessing method is to remove the outliers of the confidence interval (CI) of the predicted value of the trained ANN from the accumulated operating data set. The data set prepared by data preprocessing shows enhancement of the learning rate and the terminal error. That is, the decrease in the confidence interval of the predicted value leads to an increase in the number of outliers, which results in a rapid learning rate and small terminal error. The ANN trained by the preprocessed data set also improves the prediction capability for the test data set. These results mean that the proper data preprocessing method can facilitate the ANN in formulating the latent structure and in removing real noises and measurement errors within the training data set.

An economic analysis of desalination for potential application in Korea

Abstract Korea is a country which currently suffers from water shortages. It is expected in the early 21st century that Koreas water shortage will be more than 10% of the annual demand. The water shortage problem is more serious in the coastal areas where many industrial complexes are located. To solve the water shortage problem, seawater desalination gets more attention as an alternative water supply source since Korea is surrounded by seawater on along three sides of the nations borders. For the potential application of seawater desalination in Korea, this economic analysis was conducted using cost data for plants in the Middle East, the United States, and other countries. This study is to provide a basis for the government to establish a strategy for using seawater desalination in Korea. This analysis discusses how economic the RO process is compared to the thermal processes of MSF and ME, especially in cases where the capacity is less than about 50,000 ton/d. In addition, the largest obstacle to the application, the low water charge in Korea, is discussed. The unit cost of the RO seawater is also analyzed to be about

Effects of plume spacing and flowrate on destratification efficiency of air diffusers

1.35/t at 1990 prices. As a conservative estimate, this unit cost is assumed for the RO application in Korea which seems possible early in the 21st century. In 1995 the average tapwater charges in Koreas coastal areas was

A statistical approach to analyze factors affecting silt density index

0.53/t at the 1990 price. If the water charges are raised at an annual rate of 8.9%, it will become

Investigating the Vulnerability of Dry-Season Water Supplies to Climate Change: A Case Study of the Gwangdong Reservoir Drought Management System, Korea

1.35/t by 2006 and the RO desalination is able to be widely applied for dealing with a severe water shortage expected. Since the desalination plant can be operated regardless of weather conditions, industries in Koreas coastal areas which suffer from frequent droughts and water shortages will look into this option with more attention.

Lessons from water scarcity of the 2008-2009 Gwangdong reservoir: needs to address drought management with the adaptiveness concept

This study adopts techniques of computational fluid dynamics (CFD) to analyze the combined effect of adjacent plumes of an air-diffuser system on its destratification efficiency. Lab experiments were carried out to calibrate and verify the CFD models in thermally stratified freshwater. The CFD simulation and lab experiment results were analyzed to relate destratification efficiency with four non-dimensional variables. The results indicate that destratification number, D(N), has the best relationship that includes air flowrate, stratification frequency, water depth, and bubble slip velocity. Since plume spacing and air flowrate are the major control variables of the system, especially in the field, two charts showing the relationships between destratification efficiency, plume spacing, and destratification number are developed for guiding their control in its design and operation.

Oxidation reduction potential automatic control potential of intermittently aerated membrane bioreactor for nitrification and denitrification.

Abstract Silt Density Index (SDI) has been used as the most popular fouling index for reverse osmosis (RO) feed water to select a proper pretreatment option for RO processes. However, SDI lacks the fundamental consideration of RO membrane fouling, because SDI is supposed to be only sensitive to particles larger than 0.45 μm in diameter while fine particles (which can pass through a 0.45 μm filter) and dissolved organic matters can be potent foulants for RO processes. Our study started from the suspected performance of SDI based on its lack of the fundamental basis. Various sources of SDI data from nine literatures were collected and analyzed with turbidity and dissolved organic carbon (DOC). Interestingly, the result of our study shows that SDI can express the amount of particulate and organic fouling together. SDI can be described as a function of turbidity, DOC, and a categorical binary variable, M, for pretreatment type (i.e., M = 1 for membrane filtration and M = 0 for other methods). SDI increases if...