Considering that damages and forces to the fruit cause quantitative and qualitative changes in the fruit, in this study, the effects of three levels of loading force (wide and thin edges) (15, 30, and 45 N), 2 fixed positions on the Instron fixed jaw (vertical and horizontal), and 3 storage periods on Hayward kiwi were investigated. Experiments were analyzed as a completely randomized factorial design using SAS statistical software and data were analyzed for prediction using a multilayer perceptron artificial neural network. Statistical results showed that weight, volume, and density of kiwi fruit were decreased for loading of wide and thin edges, and according to the results, it can be concluded that weight loss in wide edge loading was more than loading of thin edges. Also, the weight, volume, and density of the fruit decreased significantly when the fruit was extensively loaded. For neural networks the best R value for weight, volume, and density were 0.9992, 0.99840, and 0.997, respectively, and for RMSE which should be the lowest among the networks, 0.22584, 3091.13 and 0.0049, respectively. Overall, it can be stated that the neural network was capable of predicting weight, volume, and density for both types of loading. But for the wide edge, equivalent, geometric, and arithmetic diameters, and for the thin edge of the aspect ratio and rationality coefficient have had a far greater impact on artificial neural network improvement and data prediction. In brief, for loading the thin edge of the network with loading force input, storage period, loading direction, spherical coefficient, spherical coefficient, aspect ratio coefficient, length, width, and thickness (network 2) and for loading wide edge, loading force, storage period, loading direction, equivalent diameter, geometric diameter, arithmetic diameter, length, width, and thickness were the best in terms of accuracy and error.

Given that fossil fuels will end one day and that these types of fuels produce a lot of pollution, each country should look for new ways to generate energy that is needed for its people in proportion to its energy resources. Given that Iran is geographically located in an appropriate region of the earth, it has a great potential for using renewable energy. In this study, sources and uses of Iranians' geothermal energy have been studied, all of which indicate that Iran has a very good potential for electricity production using geothermal energy. According to the information gathered, Iran has one geothermal energy plant in Meshkin shahr city and this plant power with a capacity of 100 megawatts is an active power plant in Iran. Also, the potential of geothermal power generation was verified in Khoy located in Azarbaijan Gharbi province, Sabalan Ardebil province, Sahand in Azarbaijan Sharghi and Damavand in Tehran province Examination verified that around 8.8% of total land in Iran is capable of geothermal energy production.