Characterization of design and economic sensitivity of vapor phase carbon adsorption systems to environmental uncertainties

Vapor phase carbon adsorption systems are used to remove aromatics, aliphatics, and halogenated hydrocarbons. The adsorption capacity of granular activated carbon is reduced when environmental parameters (temperature, pressure, and humidity) interfere with homogeneous surface diffusion and pore distribution dynamics. The purpose of this study was to investigate the effects of parametric uncertainties in adsorption efficiency. ^ Modified versions of the Langmuir isotherm in conjunction with thermodynamic equations described gaseous adsorption of single component influent onto microporous media. Experimental test results derived from Wang et al. (1999) simulated adsorption kinetics while the Myer and monsoon Langmuir constant accounted for isothermal gas compression and energetic heterogeneity under thermodynamic equilibrium conditions. Responsiveness of adsorption capacity to environmental uncertainties was analyzed by statistical sensitivity and modeled by breakthrough curves. Results indicated that extensive fluctuations in adsorption capacity significantly reduced carbon consumption while isothermal variations had a pronounced effect on saturation capacity. ^

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.