|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|84058||158859||2016||6 صفحه PDF||سفارش دهید||دانلود رایگان|
• Production rate for a poultry feed recipe is predicted.
• Mash feed size, steam temperature, conditioning time and feed rate are used.
• Process parameters are optimized using neural network.
Feed manufacturing faces enormous challenges and with the demand for good quality feed increasing gradually, it becomes essential to improve the processes in a feed mill. This article provides a brief overview of the different processes in feed manufacturing and identifies the critical process parameters. Five critical parameters are identified where the production rate is the output parameter. Mash feed size, steam temperature, conditioning time and feed rate are the input parameters. Artificial neural network is the methodology which is used to optimize the process parameters. Root mean squared error and coefficient of determination and computation time are used as performance measures and it is observed that Polak–Ribiere conjugate gradient backpropagation training function with log sigmoid – pure linear transfer function combination provided good results among the different available alternatives. The process parameters are then optimized using the appropriate ideal settings of neural network parameters. This model is extremely useful for the prediction of production rate for 1 specific recipe in a feed mill.
Journal: Computers and Electronics in Agriculture - Volume 120, January 2016, Pages 1–6