Appropriate microclimate conditions in broiler housing are critical for optimizing poultry production and ensuring the health and welfare of the birds. In this study, spatial variabilities of the microclimate in summer and winter seasons in a mechanically ventilated broiler house were modeled using the computational fluid dynamics (CFD) technique. Field measurements of temperature, relative humidity, and airspeeds were conducted in the house to compare the simulated results. The study identified two problems of high temperature in summer, which could result in bird heat stress and stagnant zones in winter, and simulated possible alternative solutions. In summer, if an evaporative cooling pad system was used, a decrease in temperature of approximately 3 °C could be achieved when the mean air temperature rose above 25 °C in the house. In winter, adding four 500-mm circulation fans of 20-m spacing inside the house could eliminate the accumulation of hot and humid air in the stagnant zones in the house. This study demonstrated that CFD is a valuable tool for adequate heating, ventilation, and air conditioning system design in poultry buildings.
This study examined artificial neural networks' (ANNs) applicability in modeling optimum insulation thickness (OIT), annual total net savings (ATS), and reduction of carbon dioxide emissions (RCO2) that result from insulating buildings. Data from insulation markets, economic parameters, fuel prices, and heating degree days (HDDs) were introduced into the model as input variables. To complete the most thorough analysis, three learning algorithms, Levenberg Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) were employed. Five statistical indexes were utilized to evaluate models' performances: determination coefficient (R2), root mean square error (RMSE), standard error of prediction (SEP), RMSE observations' standard deviation ratio (RSR), and average absolute percent relative error (AAPRE). Moreover, visualization techniques were used to assess the similarity between the OIT, ATS, and RCO2 values calculated and predicted. The results obtained clearly show that the LM model outperformed the BR and SCG models in all estimations. Thereafter, the developed ANNs model was validated for different cities. Overall, this model will provide an effective and straightforward guide for people working in the field to improve thermal insulation design, analysis, and implementation worldwide.
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