کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
761111 | 1462898 | 2012 | 7 صفحه PDF | دانلود رایگان |
Drying operations are common in food industries. One of the main components in a drying system is the furnace. The furnace operation involves heat–mass transfer and combustion, thus it demands a complex mathematic representation. Since autoregressive methods are simple, and help to simulate rapidly a system, we model a drying furnace of olive pomace via an auto-regression with exogenous variables (ARXs) method. A neural network of radial basic functions (RBFs) defines the ARX experimental relation between the amounts of dry pomace (moisture content of 15%) used like fuel and the temperature of outlet gases. A real industrial furnace is studied to validate the proposed model, which can help to control the drying process.
► We model a real furnace, fuelled with orujo, used to dry olive pomace.
► We apply a radial basic functions–auto-regression with exogenous variables (ARXs–RBFs) method.
► Root-mean-square error and r2 are used to validate the ARX–RBF model.
Journal: Energy Conversion and Management - Volume 64, December 2012, Pages 106–112