I. Golpour; P. Ahmadi Moghaddam; A. M. Nikbakht
Abstract
IntroductionSteam generation system is a crucial and essential part of food industries which generates and distributes steam for consumption in domestic production units. Energy analysis based on the first law of thermodynamics was employed as the basic approach to assess energy systems. However, the ...
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IntroductionSteam generation system is a crucial and essential part of food industries which generates and distributes steam for consumption in domestic production units. Energy analysis based on the first law of thermodynamics was employed as the basic approach to assess energy systems. However, the energy approach does not provide information on the degradation of the energy quality occurring within energy systems and is, therefore, insufficient for sustainable design or optimization goals. Nevertheless, exergy analysis based on both the first and second laws of thermodynamics can overcome shortcomings of energy analysis. In the present study, the performance of equipment of the steam generation system in Pakdis’s juice production Company located in Urmia is investigated. Owing to the energy and exergy analyses, the sites with the highest loss of exergy are identified as the critical points of the process.Materials and MethodsIn this study, the steam generation unit of a juice production company located in Urmia, West Azarbaijan province in Iran was exergetically analyzed. Using mass, energy, and exergy balances for each component of the unit, the thermodynamic objective functions including the exergy efficiency, exergy destruction rate, exergy loss rate, and the potential improvement rate were assessed. After data acquisition, energy and exergy analysis of this unit was achieved by solving the related equations with the help of thermodynamic properties along with programming in EES software package.Results and DiscussionThe results showed that the highest exergy efficiency of 98.44% was assigned to the steam distributor (O) of the unit with a potential improvement rate of 1.51 kW and an exergy loss rate of 68.80 kW, as well as the pump (M) before the fourth boiler with an exergy efficiency of 19.69%, had the lowest value of exergy efficiency. The values of 12.55 and 11.93 kW were obtained for the exergy destruction rate and its potential improvement rate, respectively. The highest exergy destruction rate of the unit was for the first boiler with a value of 12391.80 kW, with an efficiency of 19.55% and a potential improvement rate of 10295.26 kW.ConclusionWith regard to the energy and exergy analyses of the steam production system, more than 98% of the exergy destruction rate of the entire steam generation system was assigned to boilers, which had a major contribution to the exergetic efficiency of the system. The highest percentage of potential improvement was related to the first boiler and also the third boiler had the highest exergy loss rate, although the lowest exergy loss rate was the expansion tank of the system. In general, this study demonstrated the importance of exergy analysis for detecting the system components with the highest exergy destruction, which can be a breakthrough to identify these components and provides suitable solutions to improve the overall exergy efficiency of the steam-generating system.
I. Golpour; J. Amiri Parian; R. Amiri Chayjan; J. Khazaei
Abstract
Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify ...
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Identification of rice cultivars is very important in modern agriculture. Texture properties could be used to identify of rice cultivars among of the various factors. The digital images processing can be used as a new approach to extract texture features. The objective of this research was to identify rice cultivars using of texture features with using image processing and back propagation artificial neural networks. To identify rice cultivars, five rice cultivars Fajr, Shiroodi, Neda, Tarom mahalli and Khazar were selected. Finally, 108 textural features were extracted from rice images using gray level co-occurrence matrix. Then cultivar identification was carried out using Back Propagation Artificial Neural Network. After evaluation of the network with one hidden layer using texture features, the highest classification accuracy for paddy cultivars, brown rice and white rice were obtained 92.2%, 97.8% and 98.9%, respectively. After evaluation of the network with two hidden layers, the average accuracy for classification of paddy cultivars was obtained to be 96.67%, for brown rice it was 97.78% and for white rice the classification accuracy was 98.88%. The highest mean classification accuracy acquired for paddy cultivars with 45 features was achieved to be 98.9%, for brown rice cultivars with 11 selected features it was 93.3% and it was 96.7% with 18 selected features for rice cultivars.
