A neural network approach to the selection of feed mix in the feed industry

作者：Pathumnakul Supachai; Piewthongngam Kullapapruk^*; Apichottanakul Arthit

来源：Computers and Electronics in Agriculture, 2009, 68(1): 18-24.

DOI：10.1016/j.compag.2009.04.001

• Artificial neural network
• Feed cost
• Least cost formulation
• Pelleting rate
• Pelleting cost

Due to frequent changes of feed mix, the anticipation of pellet quality becomes a cumbersome task for a mill. This paper suggests that the artificial neural network can be used to predict the production rate and percentage of dust for a particular mill. Applying the suggested method, the potentially costly test production that otherwise required is prevented. Four models based on process parameters (i.e., feeder speed, die size, die thickness and press motor amperage), the proportions of nutrient contents (i.e., protein. fat, fibre, and ash) and the type and proportions of raw materials included in each formula (i.e., corn. rice bran, and soybean) were applied. The data used in this study are the actual data collected from a feed mill in Thailand. The assumption of the first model is that the process parameters and the proportions of nutrient contents in a formula affect the production efficiency and pellet quality. The assumption of the second model is that the process parameters and the proportions of nutrient contents of each main raw material included in a formula affect the production efficiency and pellet quality. The assumption of the third model is that the process parameters and the proportions of main raw materials included in the formula affect the production efficiency and pellet quality. The assumption underlying the fourth model is that only the proportions of main raw materials included in each formula affect the production efficiency and pellet quality. The results show that the process parameters and the percent inclusion of main raw materials (Model 3) give a more accurate prediction of pelleting rate. For the prediction of the dust level. Model I gives the least accurate results, while the results from the other models are not significantly different. The ability to predict the production rate and dust level enhances the mill's capability to incorporate the cost of production during the feed formulation process. Hence, the mill will be able to consider both production and raw material costs simultaneously when deciding on the most economical formula for producing feed.

• 出版日期2009-8