Prediction of Indoor Climate and Long-Term Air Quality Using the BTA-AQP Model: Part I. BTA Model Development and Evaluation
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Since 1905, the Department of Agricultural Engineering, now the Department of Agricultural and Biosystems Engineering (ABE), has been a leader in providing engineering solutions to agricultural problems in the United States and the world. The department’s original mission was to mechanize agriculture. That mission has evolved to encompass a global view of the entire food production system–the wise management of natural resources in the production, processing, storage, handling, and use of food fiber and other biological products.
History
In 1905 Agricultural Engineering was recognized as a subdivision of the Department of Agronomy, and in 1907 it was recognized as a unique department. It was renamed the Department of Agricultural and Biosystems Engineering in 1990. The department merged with the Department of Industrial Education and Technology in 2004.
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1905–present
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- Department of Agricultural Engineering (1907–1990)
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- College of Agriculture and Life Sciences (parent college)
- College of Engineering (parent college)
- Department of Industrial Education and Technology, (merged, 2004)
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Abstract
The objective of this research was to develop a building thermal analysis and air quality predictive (BTA-AQP) model to predict ventilation rate, indoor temperature, and long-term air quality (NH3, H2S, and CO2 concentrations and emissions) for swine deep-pit buildings. This article, part I of II, presents a lumped capacitance model (BTA model) to predict the transient behavior of ventilation rate and indoor air temperature according to the thermo-physical properties of a typical swine building, setpoint temperature scheme, fan staging scheme, transient outside temperature, and the heat fluxes from pigs and supplemental heaters. The obtained ventilation rate and resulting indoor air temperature, combined with animal growth cycle, in-house manure storage level, and typical meteorological year (TMY3) data, were used as inputs to the air quality predictive model (part II) based on the generalized regression neural network (GRNN-AQP model), which was presented in an earlier article. The statistical results indicated that the performance of the BTA model for predicting ventilation rate and indoor air temperature was very good in terms of low mean absolute error, a coefficient of mass residual values equal to 0, an index of agreement value close to 1, and Nash-Sutcliffe model efficiency values higher than 0.65. Graphical presentations of predicted vs. actual ventilation rate and indoor temperature are provided to demonstrate that the BTA model was able to accurately estimate indoor climate and therefore could be used as input for the GRNN-AQP model discussed in part II of this research.
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This article is from Transactions of the ASABE 53, no. 3 (2010): 863–870.