Modeling and optimization of the effect of broiler diet formulation and conditioning temperature on the physical quality of pellet and production efficiency of poultry feed factory

This study was conducted to investigate the effects of mixer added fat, crude protein and conditioning temperature on the pellet durability index, and electrical energy consumption during feed production using computational modeling tools. A total of 192 broiler feed samples with different levels of mixer added fat and crude protein in feed components and different conditioning temperatures to determine the pellet durability index, modified pellet durability index and electrical energy consumption during feed production were used. Multiple linear regression and artificial neural network were used to analyze data. Both models had the ability to predict the value of the pellet durability index, modified pellet durability index and the electrical energy consumption during feed production; but the prediction accuracy of the artificial neural network model was higher than that of the multiple linear regression model for all three outputs. Optimization was done using the artificial neural network model, and in these calculations, in order to achieve the highest possible level of pellet physical quality and the lowest possible level of electrical energy consumption, the crude protein amount was 20-20.5% and the conditioning temperature was predicted to be 85 C. However, the amount of fat was predicted to be 1% for the highest amount of pellet physical quality and 4% for the lowest amount of electrical energy consumption during production. In practical conditions, this model can help in more accurate prediction of electricity consumption and the quality of produced feed in order to achieve the optimal situation in feed production factories.