Tae-Young Heo

Prediction of Compression Index Using Regression Analysis of Transformed Variables Method

It has been proposed that correlation equations derived using empirical methods can be used to estimate compression indexes and can be easily calculated using soil parameters obtained through simple experiments when the number of consolidation tests is small or the dispersion is wide. However, most empirical equations are developed without accurate verification of the suggested regression model using normality test; even the empirical equations based on the data from a specific area are not verified. Therefore, in this study, a new equation using Box-Cox transformation of variables that considers the uncertainty of the sediment is used to minimize the uncertainty in test data.

Nitrogen and Phosphorus Removal Efficiency of Aluminum Usage in DEPHANOX Process

Removal of total nitrogen (T-N) and total phosphorus (T-P) was evaluated in a DEPHANOX process by adding Al(III) to the separator to maintain T-P in the final effluent below 0.2 mg/L. pH in each reactor was maintained 7~8 after addition of Al(III) to the levels of 5, 10, 15 mg/L. The removal efficiency of COD and T-N decreased at higher Al(III) dose, but T-P removal efficiency increased from 76.28 to 84.02, 94.66% at Al(III) dose of 5, 10, 15 mg/L, respectively. T-P in effluent showed 0.17 mg/L at Al(III) dose of 15 mg/L. Minimum 15 mg/L of Al(III) was required to maitain T-P below 0.2 mg/L in the final effluent.

Uncertainty adjustments to deterministic atmospheric dispersion models

Atmospheric Dispersion Models (ADMs) are routinely used in environmental impact assessment, risk analysis, and source apportionment studies. There are a variety of such computational ADMs, but these models usually only provide deterministic predictions or estimation of uncertainty. By introducing error components in ADMs, we formulate statistical modelling to obtain more precise prediction. These error components are based on the default neighbourhood structures created by the point source and already recognised by ADMs. Application is made to a real dataset. Posterior inference and model choice are assessed via Markov Chain Monte Carlo techniques, deviance information criterion, and mean squared predicted error.