An increasing number of large pig farms are being built in multi-floor pig buildings (MFPBs) in China. Currently, the ventilation system of MFPB varies greatly and lacks common standards. This work aims to compare the ventilation performance of three popular MFPB types with different placement of fans using the Computational Fluid Dynamics (CFD) technique. After being validated with field-measured data, the CFD models were extended to simulate the air velocity, air temperature, humidity, and effective temperature of the three MFPBs. The simulation results showed that the ventilation rate of the building with outflowing openings in the endwall and fans installed on the top of the shaft was approximately 25% less than the two buildings with fans installed on each floor. The ventilation rate of each floor increased from the first to the top floor for both buildings with a shaft, while no significant difference was observed in the building without a shaft. Increasing the shaft’s width could mitigate the variation in the ventilation rate of each floor. The effective temperature distribution at the animal level was consistent with the air velocity distribution. Therefore, in terms of the indoor environmental condition, the fans were recommended to be installed separately on each floor.
Accurately measuring the skin temperature of pigs is essential to large-scale pig farming for health monitoring, as well as disease detection and prevention. Infrared thermography (IRT) is a promising technology for the non-invasive measuring of pig skin temperature. However, the distance and angle of view of measurement greatly affect the accuracy of IRT-measured temperature. To improve the accuracy of the measurement, this study starts with evaluating the effects of four parameters on the measurement of skin temperature: horizontal distance, camera height, pig height, and angle of view between the object and the IRT camera. It follows by proposing a mathematical model describing the relationship between the real skin temperature and the four parameters through means of response surface methodology. A correction algorithm is then developed based on the mathematical model to improve the measuring accuracy. In order to evaluate the performance of the correction algorithm, the measured skin temperatures before and after correction are compared with the actual ones. The comparison was carried out in an experimental pig farm with 25 randomly selected pigs. The results show that the mean relative error before the correction was −4.64% and the mean relative error after the correction was −0.70%. This study demonstrates that the new infrared temperature correction method is effective and can benefit skin temperature monitoring for commercial pig farms.
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