There are over six
million gestation sows in the United States and most of them are kept in
gestation stalls. The inside environment of large livestock buildings requires
advanced environmental control systems to maintain animal health and optimize
animal production efficiency. The ventilation rate, inside temperature, and
supplemental heating and cooling are the main control variables to manage the
barn environment. About 144 barn-months of unpublished thermal data were
obtained from six commercial gestation houses by the National Air Emission
Monitoring Study (NAEMS). The data from this site was reviewed, corrected, and
re-analyzed to improve its quality, completion, accuracy and reliability using
the methods of comparison between onsite measurements and data collected from
nearest weather stations, introducing corrected models to adjust the onsite
data, substituting invalid and missing onsite data by weather station data and
other improved methodologies. The data completeness for solar radiation,
relative humidity, atmospheric pressure, outside temperature, and wind speed
and direction were increased by 5.6 to 17.9%. The six NAEMS gestation barns
were used to test and validate a building environmental prediction model (BEPM)
based on known thermodynamic and heat transfer principles for simultaneously
predicting inside temperatures and ventilation rates. The BEPM inputs included
the weather, the building dimensions and materials, geographical location and
building orientation, and sow herd characteristics. Predictions of ventilation
rates and inside temperatures followed the expected yearly patterns as the
measured NAEMS data. Four combinations of heat production rate and inside
temperature submodel combinations CIGR-T, CIGR-T<sup>2</sup>, US-T, and US-T<sup>2</sup>
were compared and evaluated based on the root-mean-square-deviation and fitness
tests to determine the best submodel combination. The average predicted and measured means of ventilation rate were 24.8
and 24.1 m<sup>3</sup>/s for NAEMS Site IA4B, 27.5 and 24.9 m<sup>3</sup>/s for
Site NC4B, and 24.6 and 23.9 m<sup>3</sup>/s for Site OK4B, respectively. The
average predicted and measured means of inside temperature were 20.3 and 19.7°C
for IA4B, 23.3 and 22.9°C for NC4B, and 20.8 and 20.9°C for OK4B, respectively,
based on their top performing submodel combinations. The overall optimal combination of four different submodels
was determined to be the CIGR-T<sup>2</sup> submodel, which consisted of the CIGR
International Commission of Agricultural and Biosystems Engineering heat
production rate equations for sows and a second order polynomial regression of
inside versus outside temperatures in the temperature control region between the
minimum and maximum temperature setpoints. The CIGR-T<sup>2</sup> submodel simultaneously
predicted the daily mean ventilation rate and daily mean inside temperature
with good performance. The average RMSDs of the
three sites for ventilation rate and inside temperature were 7.05 m<sup>3</sup>/s
and 2.78°C, respectively. Sensitivity tests simulated
based on the optimal BEPM (CIGR-T<sup>2</sup>) showed that annual total energy
costs including electricity for powering fans and supplemental heat were
influenced significantly by the minimum inside temperature setpoint, the
thickness of ceiling insulation, and the minimum ventilation rate. This BEPM can
be used for energy usage predictions, cooling and heating systems analysis and
design, and as an important module of process-based gas emission models. It can
be expanded to other livestock species (swine farrowing and finishing, egg
laying operations, freestall dairy barns, etc.) by changing the heat production
rate prediction submodel.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/7498412 |
| Date | 12 February 2019 |
| Creators | Xiaoyu Feng (5929670) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/BUILDING_ENVIRONMENTAL_PREDICTION_MODEL_FOR_SWINE_GESTATION_BARNS/7498412 |
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