Browsing by Author "Daniel H. Loughlin, Committee Member"

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    Development and Demonstration of a Methodology for Characterizing and Managing Uncertainty and Variability in Emission Inventories
    (2003-08-15) Li, Song; H. Christopher Frey, Committee Chair; E. Downey Brill, Jr., Committee Member; Donald Van der Vaart, Committee Member; Daniel H. Loughlin, Committee Member
    Emission factors and emission inventories are subject to both variability and uncertainty. Variability refers to observed differences attributable to true heterogeneity or diversity in emissions. Uncertainty refers to lack of knowledge regarding the true value of emissions. Variability in emissions can be attributed to variations over time, space or across different populations. Uncertainty in emissions typically arises due to limited sample size, lack of accuracy, non-representativeness of data, measurement errors, use of surrogate data, and human errors. This work successfully demonstrated new applications of quantitative methods for characterizing variability and uncertainty in emission estimates. The methods were demonstrated with respect to cases studies on nitrogen oxides (NO[subscript x]) and volatile organic compound (VOC) emissions from natural gas-fueled internal combustion engines, and VOC emissions from consumer/commercial product use, gasoline terminal loading, cutback asphalt paving, architectural coatings and wood furniture coatings. Emission data must be nonnegative, typically are positively skewed and have limited sample size. The restrictive assumption of normality used in analytical methods can lead to biased uncertainty estimates. Therefore, in this work, variability was characterized by fitting parametric distributions and uncertainty due to random sampling errors was quantified based upon numerical bootstrap simulation. Uncertainty in mean emission factors was found as much as minus 90 percent to plus 180 percent in a relative basis. Key methodological issues, including separation of intra- and inter-facility/engine variability, and methods for fitting parametric distributions to unequally weighted data, were addressed. Recommendations include extending these efforts to more emission source categories and for EPA and others to routinely report well-documented emission data to facilitate uncertainty analysis.
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    Optimization of Wastewater Treatment Design under Uncertainty and Variability
    (2005-04-19) Doby, Troy A. Jr.; Daniel H. Loughlin, Committee Member; S. Ranjithan, Committee Member; E. Downey Brill, Committee Member; John W. Baugh, Jr., Committee Chair; Sarah K. Liehr, Committee Member; Francis L. de los Reyes, Committee Member
    Typical goals of domestic wastewater treatment are a low cost process that is reliable in terms of meeting effluent quality standards. Designers using traditional steady-state design with modeling of domestic wastewater treatment plants use scalar values as inputs. The inputs are typically of two types — (1) design loadings based upon historical data and (2) stoichiometric and kinetic parameters based upon literature values. There is typically not a way of knowing a priori the reliability of the design or whether the design is least cost using the traditional design approach. Designers using deterministic optimization with modeling of domestic wastewater treatment plants also use scalar values of the two types of inputs as with traditional design. While the designer may now know that the design is least cost given the inputs, there is no way of knowing a priori whether the design is the most reliable for the cost. It is possible to take an existing design — whether obtained by traditional design methods, deterministic optimization, or by any other design method — and determine the reliability of the design. To do so, however, requires characterization of both uncertainty and variability of the data. Uncertainty arises because of a lack of knowledge about an input value and its statistical distribution. Variability arises because of the heterogeneity of the processes determining the input value. In this particular case, the historical input loadings are known and thus the variability is characterized. The characterization of the load variability is then used for future predictions of behavior. The stoichiometric and kinetic parameter values for the particular case are not typically known and thus are uncertain. An approach to the uncertainty of these values is proposed. Different loading criteria (based on percentiles of historical flow and waste concentration data) were used in deterministic optimization. It was determined that the higher the flow percentile, the more expensive the design. However, a more reliable design could be found at a lower cost (and at a lower flow percentile). A different design procedure using stochastic programming is illustrated taking both cost and reliability into account during the design procedure. As a result, a reliability-cost tradeoff curve is generated. This curve is characterized by (1) a very steep portion where slight increases in cost lead to large improvements in reliability; and (2) a very flat portion where large increases in cost lead to small improvements in reliability. This design procedure also allows determination of the value of an experimental program characterizing the uncertainty and variability of the stoichiometric and kinetic parameters and their statistical distribution.