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

Abstract

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. The effective diffusivity ranged from: 7.947.10 −10 to 1.459.10 −8 m 2 /s (first drying period) and 4.716.10 −10 to 8.611.10 −9 m 2 /s (second drying period). The predicted Arrhenius constant and activation energy ranged from 17.169 to 42.546 kJ/mol (first drying period) and from 31.261 to 50.474 kJ/mol (second drying period). 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. • 13 rapeseed hybrids quality parameters were anticipated during drying and storage. • prediction of drying and storage parameters was performed using neural network model. • rapeseed varieties, drying temperature, air velocity and drying time were ANN inputs. • optimization of drying parameters was achieved through multi-object optimization.