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izvor podataka: crosbi

Estimation of the storage properties of rapesseds using an artificial neural network (CROSBI ID 317690)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Voća, Neven ; Pezo, Lato ; Jukić, Željko ; Lončar, Biljana ; Šuput, Danijela ; Krička, Tajana Estimation of the storage properties of rapesseds using an artificial neural network // Industrial crops and products, 187 (2022), A; 115358, 13. doi: 10.1016/j.indcrop.2022.115358

Podaci o odgovornosti

Voća, Neven ; Pezo, Lato ; Jukić, Željko ; Lončar, Biljana ; Šuput, Danijela ; Krička, Tajana

engleski

Estimation of the storage properties of rapesseds using an artificial neural network

Rapeseed losses during storage can lead to undesirable difficulties in oil and biodiesel production. In this paper, three artificial neural networks were created to anticipate the main quality parameters of thirteen rapeseed varieties - cultivars and hybrids (Brassica napus L.) - during drying and storage. The varieties, drying temperature, air velocity and drying time were used as inputs to the artificial neural network model to predict the changes in seed weight and moisture during the drying process. The moisture diffusivity and activation energy of the investigated rapeseed varieties were determined under convective drying. For the experiment, an on-site drying system was used at 40, 60 and 80◦C drying air temperature. Seed oil content, free fatty acids and thousand seed weight were determined after drying at different temperatures and after 12 months of storage under the three different storage conditions. To predict these parameters after storage time, a multilayer perceptron model with three layers (input, hidden and output) for three artificial neural networks (ANNs) was used for modelling using the implemented drying parameters (such as: variety, drying temperature, air velocity and drying time, along with initial oil and free fatty acid content and storage type) were used. The prediction of the developed model was accurate enough for the prediction of the output parameters. The coefficients of determination ranged from 0.965 to 0.998 when predicting the weight and moisture of the rapeseed during the drying process and the oil and free fatty acid content and thousand grain weights after the 12 months storage period.

Matematical modelling ; artificial neural network ; rapeseed ; storage, quality

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Podaci o izdanju

187 (A)

2022.

115358

13

objavljeno

0926-6690

1872-633X

10.1016/j.indcrop.2022.115358

Povezanost rada

Poljoprivreda (agronomija)

Poveznice
Indeksiranost