Energy Consumption and Modeling of output energy with Multilayer Feed-Forward Neural Network for Corn Silage in Iran

In this study, various Artificial Neural Networks (ANNs) were developed to estimate the output energy for corn silage production in Esfahan province, Iran. For this purpose, the data on 65 corn silage production farms in the Esfahan province, were collected and analyzed. The results indicated that total energy input for corn silage production was about 83126 MJ ha–1; machinery (with 38.8 %) and chemical fertilizer (with 24.5 %) were amongst the highest energy inputs for corn silage production. The developed ANN was a multilayer perceptron (MLP) with eight neurons in the input layer (human power, machinery, diesel fuel, chemical fertilizer, water for irrigation, seed, farm manure and pesticides ), one, two, three, four and five hidden layer(s) of various numbers of neurons and one neuron (output energy) in the output layer. The results of ANNs analyze showed that the (8-5-5-1)-MLP, namely, a network having five neurons in the first and second hidden layer was the best-suited model estimating the corn silage output energy. For this topology, MAB, MAE, RMSE and R2 were 0.109, 0.001, 0.0464 and 98%, respectively. The sensitivity analysis of input parameters on output showed that diesel fuel and seeds had the highest and lowest sensitivity on output energy with 0.0984 and 0.0386, respectively. The ANN approach appears to be a suitable method for modeling output energy, fuel consumption, CO2 emission, yield, and energy consumption based on social and technical parameters. This method would open new doors to advances in agriculture and modeling

Investigation of energy inputs and CO2 emission for almond production using sensitivity analysis in Iran

The objective of this study is to examine input–output energy and CO2 emission of almond production in Shahrekord region, Iran. This article presents a comprehensive picture of the current status of energy consumption and some energy indices like energy use efficiency, energy productivity, specific energy and net energy gain. Sensitivity analysis of energy was carried out using the marginal physical productivity (MPP) technique. For this propose data were collected from 29almond farms using a face to face questionnaire. The results revealed that total energy input for almond production was found to be 106.61GJ/ha where the electricity was the major energy consumer (59.58%). The direct energy shared about (50.98%) whereas the indirect energy did (49.02%). Energy use efficiency, energy productivity, and net energy were 0.37, 0.016 kg/MJ, and -67350.16MJ/ha, respectively. The regression results revealed that the contribution of energy inputs on crop yield (except for farmyard manure and water energies) was insignificant. Water energy was the most significant input (0.674) which affects the output level. The results also showed that the impacts of direct, indirect and renewable energies on yield are significant. The GHG emissions were indicated a high CO2 output in diesel fuel consumption

Immobilized nickel hexacyanoferrate nano particles on graphen for effective removal of Cs(I) ions from radionuclide wastes

In the current work synthesis and modification of graphene oxide with Nickel Hexa Ferrocyanide (NiHCF) nanoparticles has been reported. The Graphene oxide- Nickel Hexa Ferrocyanide (GO-NiHCF) was used as an adsorbent to remove Cesium (Cs) ions from a simulated solution. The obtained product was characterized with XRD, SEM, TGA, FTIR, and BET techniques. The SEM images and XRD pattern confirms the successful immobilization of Nickel Hexa Ferrocyanide on graphene oxide sheet. The cesium removal ability of GO-NiHCF was evaluated in batch mode. Effect of various parameters such as pH, initial concentration, contact time, and interferences ions were studied. The results cleared that the maximum adsorption for Cs removal was 240 mg g-1. Equilibrium modeling studies suggest that the data are reasonably and relatively fitted well to the Langmuir adsorption isotherm. Kinetic studies show that sorption process is fairly rapid and the kinetic data are fitted well to the pseudo-second order rate model. This composite offers strong potential in the field of elimination of Cs that requires rapid and complete decontamination

A two-stage DEA model to evaluate sustainability and energy efficiency of tomato production

The aims of this study were to evaluate the sustainability and efficiency of tomato production and to investigate the determinants of inefficiency of tomato farming in Marand region of East Azerbaijan province, Iran. For these purposes, a two-stage methodology was applied, in which for the first time a fractional regression model (FRM) was employed in the second stage of analysis, so that, in the first stage a nonparametric Data Envelopment Analysis (DEA) was used to analyze the efficiencies of tomato production and in the second stage, farm specific variables such as education level, farmersâ age, total land size and use of manure were used in a fractional regression model to explain how these factors influenced efficiency of tomato farming. The results of the first stage showed that there are considerable differences between efficient and inefficient farmers in the studied area, so that the main differences were in the use of chemical fertilizers, biocides and water for irrigation. Also, the results of second stage revealed that farmersâ age, education level and total land size positively affected efficiency in tomato production. So, better use of land, chemical fertilizers, water for irrigation and improving the farmersâ educational levels through literacy campaign and land consolidation would probably increase the efficiency in the long term. Keywords: Data envelopment analysis, Energy efficiency, Fractional regression model, Tomat