Modelling energy efficiency in broiler production using multi layer perception artificial neural network approach (Case study: Ardabil province)

Document Type : Research Paper

Authors

1 MS.c Graduated Student, Department of Biosystem Engineering, Faculty of Agricultural Technology and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

2 Associate Professor, Department of Biosystem Engineering, Faculty of Agricultural Technology and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

Abstract

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.

Keywords

References

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Animal Production Research
Volume 5, Issue 2
September 2016
Pages 73-85

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