CFD modelling of airflow pattern and thermal environment in a commercial manure-belt layer house with tunnel ventilation

Tong, Xinjie; Hong, Suk-Jin; Zhao, Lingying

doi:10.1016/j.biosystemseng.2018.08.008

Cited by 35 publications

(13 citation statements)

References 32 publications

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“…The results from the preliminary sampling were compared using Tukey-Kramer tests, and the comparison showed that PM characteristics and environmental conditions in the northern and southern halves were not statistically significantly different. The exhaust fans were automatically controlled in a symmetric pattern and monitored continuously as part of a separate study at the same poultry production facility (Tong et al, 2018). Throughout the sampling periods, all fans functioned properly with no performance issues or malfunctions observed.…”

Section: Experimental Design and Sampling Planmentioning

confidence: 99%

Spatial and Seasonal Variations of PM Concentration and Size Distribution in Manure-Belt Poultry Layer Houses

Knight¹,

Tong²,

Liu³

et al. 2019

Transactions of the ASABE

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Abstract. Poultry layer houses are a significant source of particulate matter (PM) emissions, which potentially affect worker and animal health. Particulate matter characteristics, such as concentration and size distribution inside layer houses, are critical information for assessment of the potential health risks and development of effective PM mitigation technologies. However, this information and its spatial and seasonal variations are lacking for typical layer facilities. In this study, two TSI DustTrak monitors (DRX 8533) and an Aerodynamic Particle Sizer (APS 3321) were used to measure PM mass concentrations and number-weighted particle size distributions in two typical manure-belt poultry layer houses in Ohio in three seasons: summer, autumn, and winter. Bimodal particle size distributions were consistently observed. The average count median diameters (mean ±SD) were 1.68 ±0.25, 2.16 ±0.31, and 1.87 ±0.07 µm in summer, autumn, and winter, respectively. The average geometric standard deviations of particle size were 2.16 ±0.23, 2.16 ±0.18, and 1.74 ±0.17 in the three seasons, respectively. The average mass concentrations were 67.4 ±54.9, 289.9 ±216.2, and 428.1 ±269.9 µg m-3 for PM2.5; 73.6 ±59.5, 314.6 ±228.9, and 480.8 ±306.5 µg m-3 for PM4; and 118.8 ±99.6, 532.5 ±353.0, and 686.2 ±417.7 µg m-3 for PM10 in the three seasons, respectively. Both statistically significant (p < 0.05) and practically significant (difference of means >20% of smaller value) seasonal variations were observed. Spatial variations were only practically significant for autumn mass concentrations, likely due to external dust infiltration from nearby agricultural activities. The OSHA-mandated permissible exposure limit for respirable PM was not exceeded in any season. Keywords: Air quality, Particulate matter, Poultry housing, Seasonal variation, Spatial variation.

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Section: Experimental Design and Sampling Planmentioning

confidence: 99%

Spatial and Seasonal Variations of PM Concentration and Size Distribution in Manure-Belt Poultry Layer Houses

Knight¹,

Tong²,

Liu³

et al. 2019

Transactions of the ASABE

Self Cite

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“…Several studies have been conducted to analyze and predict the internal environments of livestock houses using numerical models [5][6][7][8][9][10][11][12][13][14][15]. Most previous studies have been conducted for air temperature in broiler houses and pig houses.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network

Lee

Yeo

et al. 2022

Agriculture

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The duck industry ranks sixth as one of the fastest-growing major industries for livestock production in South Korea. However, there are few studies quantitatively predicting the internal thermal and moisture environment of duck houses. In this study, high-accuracy recurrent neural network (RNN) models were used to predict the internal air temperature and relative humidity of mechanically and naturally ventilated duck houses. The models were developed according to the type of duck houses, seasons, and environmental variables by learning the monitoring data of the internal and external environments. The optimal sequence length of learning data for the development of the RNN model was selected as 120 min. As a result of the validation, both air temperature and relative humidity could be accurately predicted within 1% error. In addition, simplified RNN models were additionally developed by learning only from the data of external air temperature, relative humidity, and duck weight, which are relatively easy to acquire at the farms. The accuracy of the simplified RNN models was similar to the basic model for predicting the internal air temperature and relative humidity of duck houses in real time. In the future, for the convergence of information and communications technologies (ICTs) and application of smart farms in duck houses, the RNN models of duck houses developed in this study can be applied to predict and control the internal environments of duck houses using the model predictive control (MPC) technique.

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“…Different temperatures will affect the production activity and the quality of the eggs produced. Egg production is very susceptible to heat stress problems because of concentrated feeding and laying hens are mostly raised in cages with high stocking densities [8]. Killic and Simsek [9] stated that heat stress significantly reduces productivity due to reduced feed intake, reduced feed conversion, higher mortality, reduced egg production, lower egg shell quality, and smaller egg size.…”

Section: Introductionmentioning

confidence: 99%

Production Performance of Layer Strain Hy-Line Brown in Different Cage Locations in Closed House

et al. 2022

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The aim of this study was to determine the production performance of layer hen strain Hy-Line Brown in different cage locations in closed house. The research material was 5,051 laying hens of the Hy-Line strain aged 28 weeks. House system was closed house, cage arranged to form frame A. Hy-Line Brown layer were housed at cage densities of 500 cm2 per hen. Research feed is complete feed code 324 KJ produced by PT. Charoen Pokphand Indonesia. The research method is comparative, comparing the production performance on the location of the cage in zone 1 (inlet), zone 2 (middle), and zone 3 (outlet) of a closed cage. The research sample is 10% of the total population. Observations were carried out on 6 cages which were randomly selected in each cage zone. The average density of cages is 20 birds/m2. Tabulated research data are Hen Day Production (%), egg weight (g), egg mass (kg), and feed conversion. Data analysis is analysis of variance analysis (One-Way ANOVA). Based on the results of the study, there were significant differences in the the production performance of layer hen strain Hy-Line Brown in different cage locations in closed house. The highest production performance is the cage located close to the inlet (Zone 1).

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CFD modelling of airflow pattern and thermal environment in a commercial manure-belt layer house with tunnel ventilation

Cited by 35 publications

References 32 publications

Spatial and Seasonal Variations of PM Concentration and Size Distribution in Manure-Belt Poultry Layer Houses

Spatial and Seasonal Variations of PM Concentration and Size Distribution in Manure-Belt Poultry Layer Houses

Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network

Production Performance of Layer Strain Hy-Line Brown in Different Cage Locations in Closed House

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