Method of measurement of dust deposition and generation rates for livestock buildings
Date
1995-12-01
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Degree Level
Doctoral
Abstract
Measuring dust deposition and generation rates is important for both its fundamental physics and the application of indoor air quality control. These measurements are very difficult to conduct directly in occupied livestock buildings. Two indirect methods were developed for the measurement of both deposition and generation rates under steady-state conditions and unsteady-state conditions respectively. A new mathematical model of particle size distribution was also developed for swine building dust. Based on the mass balance equation for a typical well mixed ventilation space, the nonlinear mathematical solutions were derived. For steady-state conditions, a transformation was employed to the nonlinear solution so the linear model was obtained. Then the sequential parameter estimation method was developed to estimate both dust deposition and generation rates. The input data were airborne dust concentrations versus filtration efficiency. This steady-state method actually converted the difficult measurements of dust deposition and generation rates into the simple measurements of dust concentrations versus filtration efficiency. For unsteady-state conditions, the nonlinear parameter estimation technique was employed to estimate the unknown parameters in the solution of the model. The input variable to this model was dust concentration versus time. The estimated unknowns were dust deposition and generation rates. This unsteady-state method actually converted the difficult measurements of dust deposition and generation rates into the simple measurements of dust concentration versus time. For the validation of the steady-state method, two sets (six sub-sets) of steady-state condition experiments were conducted in the laboratory and the data were compared to computer simulations. Three experiments were conducted to validate the unsteady-state method. The proposed methods were also tested using data from Gao and Feddes (1992) for steady-state conditions, and from Wadden and Scheff (1983) for unsteady-state conditions, respectively.
Description
Keywords
agricultural engineering
Citation
Degree
Doctor of Philosophy (Ph.D.)
Department
Agricultural and Bioresource Engineering
Program
Agricultural and Bioresource Engineering