سماء عمید

Energy management is one of the main ways of the efficient use of energy resources. The prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. Required data for study were randomly collected from 70 broiler farms in North West of Iran. The input energies were included human labour, machinery, fuel, feed and electricity and the output produced energies were considered as output variables. The multi-layer perceptron (MLP) and the radial basis function (RBF) neural networks were applied for prediction of output energies of broiler production. According to the comparison results obtained from the indices of the coefficient of determination (R2), root mean square error (RMSE) and the mean absolute error (MAE) performance of the ANN-RBF model better than ANN-MLP model was estimated. In evaluating the effects of inputs on outputs of production, the production of fossil fuel showed the highest sensitivity among the production inputs in both models.

Production systems in bio-Industries rest on resources management and conversion of various forms of energy. This research is investigated and modelled the energy of broiler production in a non-parametric form. The studied samples consist of 70 broiler productions in Ardabil province which were randomly selected from statistical society of the region. In this study, the equivalent energy consumption was estimated in the mentioned industry and the energy indices were calculated, then the equivalent amount of output energy and performance of system were modelled and estimated, using the artificial neural network models. Based on the obtained results, the total equivalent energy of input and output in broiler production were calculated as 153.79 and 27.45 GJ per 1000 birds, respectively. The most consumable input energy in the broiler production of region belongs to the fossil fuel with 61.48% of the total equivalent energy. Based on the results of artificial neural network, the best structure for modelling of energy consumption of broiler production was estimated 5-14-2 structure with five inputs, one hidden layer with 14 neurons and one output layer with two output parameters. The determination coefficient of the best weight combination for estimating the equivalent energy of broiler and manure production for testing data were obtained 93% and 91%, respectively and for validation data were obtained 98% and 95%. In assessing the effectiveness of inputs on the outputs, the fossil fuel showed the highest sensitivity among the production inputs that reveals the needs for revision of the energy resources more than ever.