Mansour Saadouninejad; Morteza Almassi; mohammad ghahderijani
Abstract
This study was conducted in order to investigate the factors of sugarcane production (inputs: physical-consumption) and optimization of energy consumption for sugarcane production using a data envelopment analysis in Khuzestan province, Amirkabir Agro-industrial Company Unit. The necessary information ...
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This study was conducted in order to investigate the factors of sugarcane production (inputs: physical-consumption) and optimization of energy consumption for sugarcane production using a data envelopment analysis in Khuzestan province, Amirkabir Agro-industrial Company Unit. The necessary information to conduct this study was collected from Agro-industrial of sugarcane Amirkabir experts through face-to-face interviews and questionnaire completion. According to the results, of the total energy consumed in production, the energy of cuttings and chemical fertilizers had the largest share among the consumed inputs with 59 and 16 (%), respectively. Biocides 3.15 (%), agricultural machines 1.09 (%) and human labor 0.06 (%) were the least energy-consuming inputs among other sugarcane production inputs. According to the results obtained from this research, it was found that two inputs sugarcane cuttings and chemical fertilizer have the highest amount of consumption among all the inputs for production. Also, the results of data coverage analysis showed that nitrogen fertilizer and human labor had the largest share of stored energy with 42 and 60.31 (%). The lowest share of energy stored in production belonged to chemical pesticides and phosphorus fertilizer, respectively. The results showed that reducing the consumption of chemical fertilizers and diesel fuel is important for energy storage and reducing the problem of environmental risk in the region. Saving in diesel fuel is possible by improving the performance of the irrigation pump and using new tractors and soil analysis to improve the use of chemical fertilizers.
E. Taghinejad
Abstract
Parboiling is a hydrothermal treatment and it consists of 3 steps: soaking, steaming and drying of paddy. In this research RSM and central composite design was used for optimization of parboiling indicators (soaking temperature and steaming time). After soaking temperature of (55, 60, 65, 70 and 75 oC) ...
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Parboiling is a hydrothermal treatment and it consists of 3 steps: soaking, steaming and drying of paddy. In this research RSM and central composite design was used for optimization of parboiling indicators (soaking temperature and steaming time). After soaking temperature of (55, 60, 65, 70 and 75 oC) and steaming time of (2, 4, 6, 8 and 10 min), the samples were dried for three days in the shade to a final moisture content of 11±1% (w.b.). Using multiple regression analysis a quadratic polynomial equation was developed for each response. Analysis of variance (ANOVA) was performed to check the adequacy and accuracy of the fitted models. The results showed that the effects of soaking temperature and steaming time were found to be significant (p<0.01) with regards to head rice yield. Results also showed that, soaking temperature and steaming time were the most important variables which affect the head rice yield, respectively. Based on developed modeles, optimum conditions for the maximum head rice yield of (66.67%), were achieved at soaking temperature of 64.93oC and steaming time of 6.67 min.
Mohammad Sharifi; Erfan Khosravani-Moghadam; Payam Hatami
Abstract
One of the most important issues in the management of milk production is to select the best option for each production activity, so that the time and cost of production is minimal and quality is the maximum. Considering the large number of activities and options for each activity usually, approaches ...
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One of the most important issues in the management of milk production is to select the best option for each production activity, so that the time and cost of production is minimal and quality is the maximum. Considering the large number of activities and options for each activity usually, approaches for obtaining a unique solution is difficult. In this regard, assigning weights to time, cost and quality can lead us to the best answer from the answers obtained. Because decision making for future is based on probability, in the present study an attempt is made to solve the cost and time and quality of milk production process, using fuzzy logic numbers for estimating risk management by defining (α) cut for intervals. Then by applying and comparing two algorithms NSGA-II and MOPSO for each activity in the milk production process under affect of different (α) cut, the best way for performing each activity was determined. Results shows that, MOPSO approach was had a more suitable effect than the NSGA-II approach for solving the problem under conditions of uncertainty (α =0) for amount of time and cost, and quality which was calculated for 2180 days atthe cost of 118228.86 Rialsand 46% in genetic algorithm and 2180 days and118224.20 Rialsand 41% in the particle Swarm algorithm respectively. Also by adjusting (α) rate to work conditions, the risk existed in the production process can be managed while performing the process activities, according the procedure that is determined for each activity during the algorithm performance, the least time and cost and highest quality as much as possible can be achieved.
