odeling and Optimization of the Grinding Cereals to Produce Poultry Feed in the Hammer Mill using Response Surface Method (RSM)

The most costly part of poultry breeding is feeding. Due to the noticeable developments in animal husbandry and agricultural sectors, it is necessary to use the mechanized methods to reduce the casualties, increase the productivity as well as reduce the time and cost in each of these sectors. Reducing the particle size is one of the ways to process cereals which improves the mixing and also the nutritional value of the feed and the quality of the pellet feed. Optimizing the performance of hammer mill with the aim of reducing the size of different materials for poultry feed, would be very beneficial for obtaining the minimum cost of food, maximum quality and capacity. The main objective of this research was to optimize the operational variables, including sieve size, grain moisture content, feed rate and the number of hammers, each of them at three levels, on a hammer mill during the process of poultry food production from wheat, corn, barley and soybean grains.

The seeds used in experiments were wheat (Azar2 variety), corn (Brazilian variety), soybean (Danpars variety) and barley (Aras variety). A laboratory hammer mill was used to perform experiments. The treatments including sieve diameter (2, 2.3and 4.4 mm), grain moisture content (10, 14 and 18%), seed input rate to milling compartments (one-third, two-thirds and fully openness of tank gate) and the number of hammer (12, 18 and 24) were investigated. In order to measure the working capacity of the hammer mill, the required time for milling was recorded. The amount of final milled crop in each experiment was weighed and divided into the needed time for milling. Sieve analysis was used to determine the distribution and dispersion of the milled material which works according to the standard of ASTM E-11-70 Part 41 (Anonymous, 2004). In this study, the effects of input variables were investigated using the response surface method focusing on the central composite design approach to optimize the fineness degree and working capacity of the mill. The Design Expert 8.0.6 software was applied for statistical analysis, modeling and optimization.

The results indicated that sieve size and the number of hammers have been affected by the fineness degree of wheat grains, significantly. In addition, all four factors and interaction effects between sieve size and moisture content and also moisture content and number of hammers influential working capacity at the significant level of 1%. In the case of corn, the influence of moisture content and its interaction with sieve size on grain fineness, and the effect of sieve size, moisture content, feed rate and interactions between sieve size and moisture content and moisture content and feed rate of working capacity were significant at the level of 1%. For barley, moisture content at the level of 1% and interaction between sieve size and moisture content at the probability level of 5% were effective on barley fineness degree. Meanwhile, the moisture content at the level of 1% and sieve size and its interaction with moisture content at the level of 5% influenced working capacity, significantly. Soybeans were not able to respond the required moisture level for the experiments due to their soft and brittle texture, whereas unreliable results were obtained by changing its moisture levels. The best size of sieve holes, grain moisture content, feed rate and the number of hammers were determined to minimize the fineness degree and maximize the working capacity of the hammer mill.

In this research, the response surface method considering a central composite design was used to optimize the operational variables of a hammer mill, including sieve hole size, grain moisture, feed rate and the number of hammer to produce poultry feed with the aim of achieving a minimum fineness degree (more grain crushing) and maximum milling capacity. The results of variance analysis were presented for wheat, corn, barley and soybean. Regression models could represent the relationship between the independent variables and the outputs with high confidence coefficient, and the best values of input variables were determined to optimize grinding operation.