Development and Comparison of Backpropagation and Generalized Regression Neural Network Models to Predict Diurnal and Seasonal Gas and PM10 Concentrations and Emissions from Swine Buildings

The quantification of diurnal and seasonal gas (NH3, H2S, and CO2) and PM10 concentrations and emission rates (GPCER) from livestock production facilities is indispensable for the development of science-based setback determination methods and evaluation of improved downwind community air quality resulting from the implementation of gas pollution control. The purpose of this study was to employ backpropagation neural network (BPNN) and generalized regression neural network (GRNN) techniques to model GPCER generated and emitted from swine deep-pit finishing buildings as affected by time of day, season, ventilation rates, animal growth cycles, in-house manure storage levels, and weather conditions. The statistical results revealed that the BPNN and GRNN models were successfully developed to forecast hourly GPCER with very high coefficients of determination (R2) from 81.15% to 99.46% and very low values of systemic performance indexes. These good results indicated that the artificial neural network (ANN) technologies were capable of accurately modeling source air quality within and from the animal operations. It was also found that the process of constructing, training, and simulating the BPNN models was very complex. Some trial-and-error methods combined with a thorough understanding of theoretical backpropagation were required in order to obtain satisfying predictive results. The GRNN, based on nonlinear regression theory, can approximate any arbitrary function between input and output vectors and has a fast training time, great stability, and relatively easy network parameter settings during the training stage in comparison to the BPNN method. Thus, the GRNN was characterized as a preferred solution for its use in air quality modeling

Assessing Air Velocity Distribution in Three Sizes of Commercial Broiler Houses During Tunnel Ventilation

Convective cooling is a critical management strategy for maintaining an environment that promotes production efficiency, thermal comfort, and animal well-being in commercial broiler houses. Variations in house size, design, and equipment configuration contribute greatly to the air velocity distribution within the facility. This study assessed total airflow, air velocity distribution, and quantified the floor area in three facilities experiencing insufficient air velocity for maintenance of production efficiency, thermal comfort, and animal well-being. Test facility 1 was an 18.3 x 170.7 m solid side-wall broiler house, test facility 2 was a 15.24 x 144.8 m solid side-wall broiler house, and test facility 3 was a 12.19 x 121.9 m curtain side-wall broiler house. Total airflow of each facility, measured with a Fan Assessment and Numeration System, was 512,730, 389,495, and 329,270 m3 h-1 for test facilities 1, 2, and 3, respectively. Air velocity distribution patterns were characterized in each house with a Scalable Environment Assessment System (SEAS) and spatial statistics. The air velocity distributions within the test facilities were variable, with notable maxima immediately downstream of the tunnel inlets, which serve as a well-defined vena contracta, and local minima near the leading end of the evaporative pads and the exhaust fans. Equipment within the facilities had an impact on the air velocity distribution by creating reduced cross-sectional areas that resulted in localized increases in air velocity. The percentage of total bird-level floor area in each facility experiencing air velocities below 1.5 m s-1 was 14.3%, 20.7%, and 10.0% for test facilities 1, 2, and 3, respectively. The effective design velocity (Ved) was calculated from total airflow using the measured building cross-sectional area. The Ved measured 2.97, 2.45, and 2.34 m s-1 for test facilities 1, 2, and 3, respectively. Mean cross-sectional air velocity (Vcs) was calculated from SEAS data and normalized using each facility‘s Ved to account for differences in building size for comparison. Test facility 1, the largest of the three houses, generated substantially higher Vcs/Ved than test facilities 2 and 3. Test facilities 2 and 3 maintained a larger proportion of Vcs above Ved than test facility 1. Test facility 1 showed 26.5% of the total house length below Ved, while test facilities 2 and 3 had only 20.8% and 17.5%, respectively, of the total house length below Ved. The lower-velocity regions were due to the length of the evaporative cooling pad inlet and the use of tunnel doors, and the exhaust fan placement on the side-walls in test facility 1 created an additional pronounced low-velocity area. Placement of tunnel ventilation fans on the end-wall of the facility, rather than the side-wall, eliminated the low-velocity region at the exhaust end of the facility. Modifications to current practices for broiler production facility construction and evaporative cooling pad inlet installation would be required to minimize the low-velocity region at the inlet end of these facilities. Consideration of house width and physical arrangement of the air inlets, tunnel fans, and internal equipment are critical for improving the uniformity of air velocity in commercial broiler houses

Effect of Measurement Density on Characterizing Air Velocity Distribution in Commercial Broiler Houses

Increasing air velocity of tunnel ventilation systems in commercial broiler facilities improves production efficiency. As a consequence, many housing design specifications require a minimum air velocity in the house. Air velocities are typically assessed with a hand-held anemometer at random locations, rather than systematic traverses. Simultaneous measurement of air velocity at multiple locations in the facility would provide a more accurate estimation of air velocity distribution. The objective of this study was to assess the effect of measurement density on accuracy of estimating air velocity distribution in a tunnel-ventilated broiler production facility. An array of 40 anemometers was placed on a series of transverse cross-sections in a commercial broiler production facility with curtain sidewalls (no birds present) measuring 12.8 × 121.9 m. The house was equipped with ten 121.9 cm exhaust fans. Cross-sectional air velocity measurements were taken along the length of the house in increments of 3.05 m axially. Data were sampled at 1 Hz for 2 min; three 2 min subsamples were obtained at each cross-section. Horizontal plane air velocity distribution maps were generated using 12.19, 6.10, and 3.05 m axial measurement distances between cross-sections at 0.46 m above the litter. Vertical plane air velocity distribution maps were created using 10, 20, and 40 symmetrical sampling points from the original data set. Cross-validation analysis revealed that higher spatial measurement density in the axial direction yielded a higher correlation between observed and predicted values (79%) and lower mean squared prediction error (MSPE; 0.10 m s-1) when compared to decreased sampling densities. Vertical cross-section measurement density comparisons showed a reduction in MSPE and an increase in correlation between observed and predicted values at higher sampling densities in all cases tested excluding one. In the case of improved interpolation results with fewer measurement points, the cross-section demonstrated high variation in air velocity and velocity values being very low. Axial cross-sectional measurement distances of =3.05 m and vertical plane measurement densities of =40 measurement points should be used to accurately characterize air velocity distribution in a 12.8 × 121.9 m broiler production facility. Although more sensors and time are required to collect 40-point cross-sections at 3.05 m, the improved visualization allows better identification of distribution effects caused by equipment placement in the facility

A Not So-Random Walk with Wind: Evaluating Wind Velocity Update Methods in Ground Based Spray Deposition Models

The notion that wind speed and direction can be approximated by adding a random fluctuation to the previous value was investigated. The data were recorded at one meter above a field to simulate conditions that are present at a ground sprayer‘s boom. Variance ratio tests were carried out to test the null hypothesis that wind possesses similar properties to a random walk versus the alternative that wind does not. More specifically, are the random fluctuations auto correlated with one another in time? This process was done to a 10Hz sample and averages of the measured wind data at 0.5, 1, 5, 10, 30, 60, 300, and 600 seconds. It was found that for all tests, except for the 300 and 600 second data samples, the null hypothesis was rejected at greater than 99.9% certainty. This indicates that there is evidence of autocorrelation (rather than randomness) in the measurements of wind speed and direction, associated with each other in time

Air Pollutant Emissions from Confined Animal Buildings (APECAB) Project: Minnesota Data

To address the need for gas, odor, and particulate matter (PM) emission from animal production buildings, funding was secured in the fall of 2001 by a six-state research team for a USDA project entitled Air Pollutants Emissions from Confined Animal Buildings, or APECAB. The main objective of the APECAB project was to quantify long-term (yearly) air pollutant emissions from confined animal buildings and establish methodologies for real time measurement of these emissions and build a database of air emissions for US livestock and poultry buildings. The APECAB study was a collaboration of land-grant universities in Minnesota (lead institution), Indiana, Illinois, Texas, Iowa, and North Carolina. Extensive planning occurred during the first nine months for protocol development and equipment selection and purchase. Data collection began at various times during the fall of 2002 for each of the cooperating universities and ended at various times in 2004. The immediate goal of the study was a 15-month sampling period to assure that long-term emissions from actual animal production buildings were determined. Long-term measurements revealed the variations in air emissions due to seasonal effects, animal growth cycles, diurnal variations, and manure handling systems

Air Pollutant Emissions from Confined Animal Buildings (APECAB) Project: Indiana Data

To address the need for gas, odor, and particulate matter (PM) emission from animal production buildings, funding was secured in the fall of 2001 by a six-state research team for a USDA project entitled Air Pollutants Emissions from Confined Animal Buildings, or APECAB. The main objective of the APECAB project was to quantify long-term (yearly) air pollutant emissions from confined animal buildings and establish methodologies for real time measurement of these emissions and build a database of air emissions for US livestock and poultry buildings. The APECAB study was a collaboration of land-grant universities in Minnesota (lead institution), Indiana, Illinois, Texas, Iowa, and North Carolina. Extensive planning occurred during the first nine months for protocol development and equipment selection and purchase. Data collection began at various times during the fall of 2002 for each of the cooperating universities and ended at various times in 2004. The immediate goal of the study was a 15-month sampling period to assure that long-term emissions from actual animal production buildings were determined. Long-term measurements revealed the variations in air emissions due to seasonal effects, animal growth cycles, diurnal variations, and manure handling systems