Browsing by Subject "experimental uncertainty"
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Item Accurate Measurements and Modeling of the PpT Behavior of Pure Substances and Natural Gas-Like Hydrocarbon Mixtures(2012-10-19) Mantilla, IvanThe scale of the energy business today and a favorable and promising economic environment for the production of natural gas, requires study of the thermophysical behavior of fluids: sophisticated experimentation yielding accurate, new volumetric data, and development and improvement of thermodynamic models. This work contains theoretical and experimental contributions in the form of 1) the revision and update of a field model to calculate compressibility factors starting from the gross heating value and the mole fractions of diluents in natural gas mixtures; 2) new reference quality volumetric data, gathered with state of the art techniques such as magnetic suspension densimetry and isochoric phase boundary determinations; 3) a rigorous first-principles uncertainty assessment for density measurements; and 4) a departure technique for the extension of these experimental data for calculating energy functions. These steps provide a complete experimental thermodynamic characterization of fluid samples. A modification of the SGERG model, a standard virial-type model for prediction of compressibility factors of natural gas mixtures, matches predictions from the master GERG-2008 equation of state, using least squares routines coded at NIST. The modification contains new values for parametric constants, such as molecular weights and the universal gas constant, as well as a new set of coefficients. A state-of-the-art high-pressure, single-sinker magnetic suspension densimeter is used to perform density measurements over a wide range of temperatures and pressures. This work contains data on nitrogen, carbon dioxide, and a typical residual gas mixture (95% methane, 4% ethane, and 1% propane). Experimental uncertainty results from a rigorous, first-principles estimation including composition uncertainty effects. Both low- and high-pressure isochoric apparatus are used to perform phase boundary measurements. Isochoric P-T data can determine the phase boundaries. Combined with density measurements, isochoric data provides isochoric densities. Further mathematical treatment, including noxious volume and thermal expansion corrections, and isothermal integration, leads to energy functions and thus to a full thermodynamic characterization.Item Back-calculating emission rates for ammonia and particulate matter from area sources using dispersion modeling(Texas A&M University, 2004-11-15) Price, Jacqueline ElaineEngineering directly impacts current and future regulatory policy decisions. The foundation of air pollution control and air pollution dispersion modeling lies in the math, chemistry, and physics of the environment. Therefore, regulatory decision making must rely upon sound science and engineering as the core of appropriate policy making (objective analysis in lieu of subjective opinion). This research evaluated particulate matter and ammonia concentration data as well as two modeling methods, a backward Lagrangian stochastic model and a Gaussian plume dispersion model. This analysis assessed the uncertainty surrounding each sampling procedure in order to gain a better understanding of the uncertainty in the final emission rate calculation (a basis for federal regulation), and it assessed the differences between emission rates generated using two different dispersion models. First, this research evaluated the uncertainty encompassing the gravimetric sampling of particulate matter and the passive ammonia sampling technique at an animal feeding operation. Future research will be to further determine the wind velocity profile as well as determining the vertical temperature gradient during the modeling time period. This information will help quantify the uncertainty of the meteorological model inputs into the dispersion model, which will aid in understanding the propagated uncertainty in the dispersion modeling outputs. Next, an evaluation of the emission rates generated by both the Industrial Source Complex (Gaussian) model and the WindTrax (backward-Lagrangian stochastic) model revealed that the calculated emission concentrations from each model using the average emission rate generated by the model are extremely close in value. However, the average emission rates calculated by the models vary by a factor of 10. This is extremely troubling. In conclusion, current and future sources are regulated based on emission rate data from previous time periods. Emission factors are published for regulation of various sources, and these emission factors are derived based upon back-calculated model emission rates and site management practices. Thus, this factor of 10 ratio in the emission rates could prove troubling in terms of regulation if the model that the emission rate is back-calculated from is not used as the model to predict a future downwind pollutant concentration.