Biodiesel is a bio-renewable fuel derived from vegetable oils and animal fats with less environmental pollution than fossil fuels. This research aims to design, fabricate, and evaluate a hydrodynamic reactor for biodiesel production. According to the fluid characteristic, the rotor and stator were designed and the electric engine was chosen. The cavities of the rotor were designed for optimal cavitation. The effect of reaction time and rotational speed were examined to assess the reactor. Speed rotor rotational speed of hydrodynamic cavitation reactors can intensify the transesterification reaction by increasing the occurrence of cavitation in the space between the rotor and the stator. Therefore, to investigate the effect of this variable on biodiesel production efficiency, three levels were selected (2000, 2500, and 3000 rpm). As the rotational speed of the hydrodynamic reactor increases, the cavitation operation increases, and as a result, the conversion percentage rises too. This experiment indicated that the rise of residence time from 30 s to 60 s increases methyl esters yield, but following the time up to 60 s, the methyl esters yield has no significant changes. The results showed that the biodiesel produced from waste oil in the hydrodynamic reactor could be a suitable alternative to diesel. پرونده مقاله

In this paper, to simulate mechanical stress on the cutter bar of the harvesting combine during cutting of wheat stems, first, shearing strength of the stems was measured for various moisture content levels and cutting heights. After that the cutter bar of combine was simulated by Abaqus with the specified conditions. The results of the simulation showed that with increase in moisture content of stem, shear stress on the blade is increased up to 18% due to the viscous damping effect of the moisture of stem. On the other hand, with increase in cutting height, shear stress on the blade is decreased up to 12% because of the accumulation of less mature fibers at the lower levels of stems. پرونده مقاله

This study aimed to evaluate the efficiency of energy consumption and economic analysis of different watermelon cultivation systems in Fars Province of Iran. Watermelon production systems were classified into five systems, namely, custom tillage (group 1), conservation tillage (group 2), traditional planting (group3), semi mechanized planting (group 4), and mechanized planting (group 5). Data were collected from 317 watermelon producers from different parts of the province through face to face interviews. Multi-Layer Perceptron artificial neural networks were used to model the energy flows of watermelon production. The results showed that the greatest energy consumption belonged to mechanized planting system with the value of 81317.72 MJha-1 and with the productivity of 0.61 kgha-1and energy use efficiency of 1.17. Clustering function with three inputs (human resources, machines and diesel fuel) showed that the difference between groups 2 and 4 is more than the other groups. The least energy consumption belonged to the conservative agriculture as78163.86 MJha-1and the energy productivity and energy use efficiency about 0.64 kgha-1 and 1.22, respectively. The results of energy modeling showed that an ANN model with 9-10-1 structure was determined to be optimal for energy flow modeling of this system. Generally, it was concluded that the artificial neural network models can be applicable to prognosticate the energy flows of watermelon production. From an economic point of view, the least net profit belonged to traditional planting with the value of 2618.14$, and the most net return belonged to mechanized planting with the value of 2752.88$/ha. پرونده مقاله

The present study attempts to investigate the potential relationship between input energies, performance production of greenhouse basil, and greenhouse gases emitted from this product. The data were collected from 24 greenhouses using a questionnaire and verbal interaction with farmers. Results of the study showed that the total input energy and total output energy for basil production were 119,852.9 MJ/ha and 61,040 MJ/ha, respectively. The highest rate of energy consumption was related to electricity (52,200 MJ/ha), followed by plastic (23,220 MJ/ha) and chemical fertilizers (13,894 MJ/ha). The energy and productivity indices were estimated at 0.45 and 0.21, respectively, which indicated that the efficiency of energy in the agricultural sector was low. In addition, it was found that the pure energy index and total greenhouse gases emitted from basil production were equal to -722,706.9 and 9,595.6 kg (CO2), respectively. The highest emission of greenhouse gases was attributed to electricity (2,216 kg/CO2). Results of modeling proved that artificial neural networks can predict basil performance and CO2 emissions with a high degree of accuracy (R2=0.99 and MSE= 0.00023). پرونده مقاله