H. Vakili; M. Kiani Deh Kiani; M. Changizian
Abstract
Introduction According to the importance of energy and the impact of this input on the final price of a product, selection of materials and components of poultry saloons is very important. Poultry saloons are divided into two types: open saloons and closed saloons. In closed saloons, the choice ...
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Introduction According to the importance of energy and the impact of this input on the final price of a product, selection of materials and components of poultry saloons is very important. Poultry saloons are divided into two types: open saloons and closed saloons. In closed saloons, the choice of materials and components of the saloon (door, window, etc.) are more sensitive than the open type. Due to the climatic conditions of Khuzestan province, all of the used saloons in this province are almost closed. Poultry farms in Khuzestan province have a lot of cooling load in the warm seasons. If the materials and components of the saloons are not chosen properly, energy losses increase, and as a result, the price of meat increases. Therefore, investigating of heating and cooling loads of saloons and use of suitable components to prevent energy losses is necessary. Energy modeling of saloons and buildings is done by various software (Plast Energy, Design Builder, Trnsys, etc.). One of the most efficient and precise of this software is Carrier. Materials and Methods This study was conducted to calculate heating and cooling loads in different climates of Khuzestan province. In this research, the cities of Izeh, Shoosh, Abadan and Andimeshk were selected as corresponding to different climates in the province. Required data for software was collected in three categories: (a) Weather data (geographic information, location of the saloon, local time zone and local soil specifications), (b) Data about the physical properties of the building (general specifications of the space, internal sources of heat production (personslabors, poultry, equipment), (c) Specifications of walls, floor, windows and doors, ceiling and lighters, (d) Infiltration of air, (e) Systems, and (f) Information on power and fuel consumption. In this research, a rectangular saloon with dimensions of 85×16 meter was considered for all three types of conventional saloons in the province (block, brick and panel), which are the common dimensions and the capacity of these saloons is 20,000 broiler chickens. In this study, Carrier software was used to calculate heating and cooling loads. The results of software were verified by the amount of fuel and power consumption. Results and Discussion The sand and soil floors had the highest cooling load by 48828 and 53012 kJ h-1, respectively, while concrete and mosaic floors had lower cooling load than them. The heating load of these two floors (3906 kJ h-1) was less than that in the sand and soil floors. Concrete floor had better conditions to choose because of the less cost than the mosaic floor. Comparison of heating and cooling loads in different types of walls made of various materials showed that the block wall had the highest heating load of 429356 kJ h-1 and the highest cooling load by 658356 kJ h-1, while the sandwich panel wall had the lowest heating load by 116873 kJ h-1 and the lowest cooling load by 123618 kJ h-1. Three types of doors are commonly used in poultry houses: iron, fiberglass and aluminum. The results showed that the iron and fiberglass doors had the highest and lowest heating and cooling loads, respectively. The investigation of the effect of different types of windows on heating and cooling loads showed that iron and plastic windows had the highest and lowest heating and cooling loads, respectively. The results showed that Irannait ceiling had the highest heating and cooling loads by 371416 kJ h-1 and 787535 kJ h-1, respectively, while the ceiling made of sandwich panel had the lowest heating and cooling loads by 72756 kJ h-1 and 72429 kJ h-1, respectively, because of low heat transfer coefficient. Comparison of heating load of the saloons showed that the block saloon had the highest heating load by 891525 kJ h-1 and the suggested saloon in this study had the lowest heating load by 309068 kJ h-1. The block and suggested saloons also had the highest and lowest cooling loads by 1604828 kJ h-1 and 330795 kJ h-1, respectively. Conclusion The amount of heating and cooling loads for suggested saloon were 29.6% and 18.24% lower than that of brick and block saloons, respectively. The difference in the cost of constructing suggested and brick (the most common saloon in the province) saloons was 28.9 million tomans. By considering the difference in the cost of energy consumption of them (11.726 million tomans), this amount will be compensated after 2 years and 5 months and then will be returned on investment.
S. Haroni; M. J. Sheikhdavoodi; M. Kiani Deh Kiani
Abstract
Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many ...
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Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many countries sugarcane is a renewable source for the biofuel. The efficient use of inputs in agriculture lead to the sustainable production and help to reduce the fossil fuel consumption and greenhouse gases emission and save financial resources. Furthermore, detecting relationship between the energy consumption and the yield is necessary to approach the sustainable agriculture. It is generally accepted that many countries try to reduce their dependence to agricultural crop productions of other countries. The being Independent on agricultural productions lead to take more attention to modern methods and the objective of all these methods is increasing the performance with the efficient use of inputs or optimizing energy consumptions in agricultural systems. Energy modeling is a modern method for farm management that this model can predict yield with using the different amount of inputs. The objective of this study was to predict sugarcane production yield and (greenhouse gas) GHG emissions on the basis of energy inputs. Materials and Methods This study was carried out in Khouzestan province of Iran. Data were collected from 55 plant farms in Debel khazai Agro-Industry using face to face questionnaire method. In this study, the energy used in the sugarcane production has considered for the energy analysis without taking into account the environmental sources of the energy such as radiation, wind, rain, etc. Energy consumption in sugarcane production was calculated based on direct and indirect energy sources including human, diesel fuel, chemical fertilizers, pesticides, machinery, irrigation water, electricity and sugarcane stalk. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. Input energy in agricultural systems includes both direct and indirect energy and renewable and non-renewable forms. Direct energies include human labor, diesel fuel, water for irrigation and electricity and indirect energies consisted of machinery, seed (cultivation of sugarcane has been done with cutting of sugarcane instead of seed), chemical fertilizer. Renewable energies include machinery, sugarcane stalk, chemical fertilizer while non-renewable energy consisted of machinery, chemical fertilizer, electricity and diesel fuel. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. The amounts of GHG emissions from inputs in sugarcane production per hectare were calculated by CO2 emissions coefficient of agricultural inputs. Energy modeling is an attractive subject for engineers and scientists who are concerned about the energy management. In the energy area, many different of models have been applied for modeling future energy. An artificial neural network (ANN) is an artificial intelligence that it can applied as a predictive tool for nonlinear multi parametric. Artificial neural network has been applied successfully in structural engineering modeling ANNs are inspired by biological neural networks. Results and Discussion The total energy used in the farm operations during the sugarcane production and the energy output was 1742883.769 and 111000 MJha_1, respectively. Electricity (52%) and chemical fertilizers (16%) were the most influential factors in the energy consumption. The electricity contribution was the highest due to the low efficiency of energy conversion in electric motors which were used for irrigation in the study area. In some areas, inefficient surface irrigation wastes a lot of water and energy (in forms of electricity). Another reason is that electricity energy equivalent for Iranian electricity production is higher than developed countries because Iran’s electricity grid is highly dependent on fossil fuels, so that 95% of the electrical energy in Iran is generated in thermal power plants using fossil fuels sources. In addition, the electricity transmission system is too old. GHG emissions data analysis indicated that the total GHG emissions was 415337.62 kg ha-1 (CO2eq) kgCO2eq ha-1 in which burning trash with the share of 62% had the highest GHG emission and followed by electricity (32%), respectively. The ANN model with 7-5-15-1 and 5-5-1 structure were the best model for predicting the sugarcane yield and GHG emissions, respectively. The coefficients of determination (R2) of the best topology were 0.98 and 0.99 for the sugarcane yield and GHG emissions, respectively. The values of RMSE for sugarcane production and GHG emission were found to be 0.0037 and 4.52×10-6, respectively. Conclusion The statistical parameters of R2 and RMSE demonstrated that the proposed artificial neural networks results have best accuracy and can predict the yield and GHG emission. It is generally showed that artificial neural networks have good potential to predict the yield of the sugarcane production.
S. Abbasi; H. Bahrami; B. Ghobadian; M. Kiani Deh Kiani
Abstract
Introduction The extensive use of diesel engines in agricultural activities and transportation, led to the emergence of serious challenges in providing and evaluating alternative fuels from different sources in addition to the chemical properties close to diesel fuel, including properties such as renewable, ...
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Introduction The extensive use of diesel engines in agricultural activities and transportation, led to the emergence of serious challenges in providing and evaluating alternative fuels from different sources in addition to the chemical properties close to diesel fuel, including properties such as renewable, inexpensive and have fewer emissions. Biodiesel is one of the alternative fuels. Many studies have been carried out on the use of biodiesel in pure form or blended with diesel fuel about combustion, performance and emission parameters of engines. One of the parameters that have been less discussed is energy balance. In providing alternative fuels, biodiesel from waste cooking oil due to its low cost compared with biodiesel from plant oils, is the promising option. The properties of biodiesel and diesel fuels, in general, show many similarities, and therefore, biodiesel is rated as a realistic fuel as an alternative to diesel. The conversion of waste cooking oil into methyl esters through the transesterification process approximately reduces the molecular weight to one-third, reduces the viscosity by about one-seventh, reduces the flash point slightly and increases the volatility marginally, and reduces pour point considerably (Demirbas, 2009). In this study, effect of different percentages of biodiesel from waste cooking oil were investigated. Energy distribution study identify the energy losses ways in order to find the reduction solutions of them. Materials and Methods Renewable fuel used in this study consists of biodiesel produced from waste cooking oil by transesterification process (Table 1). Five diesel-biodiesel fuel blends with values of 0, 12, 22, 32 and 42 percent of biodiesel that are signs for B0, B12, B22, B32 and B42, respectively. The test engine was a diesel engine, single-cylinder, four-stroke, compression ignition and aircooled, series 3LD510 in the laboratory of renewable energies of agricultural faculty, Tarbiat Modarres University. The engine is connected to a dynamometer and after reaching steady state conditions data were obtained (Fig. 1). In thermal balance study, combustion process merely as a process intended to free up energy fuel and the first law of thermodynamics is used (Koochak et al., 2000). The energy contained in fuel converted to useful and losses energies by combustion. Useful energy measured by dynamometer as brake power and losses energy including exhaust emission, cooling system losses and uncontrollable energy losses. Variance analysis of all engine energy balance done by split plot design based on a completely randomized design and the means were compared with each other using Duncan test at 5% probability. Results and Discussion Results showed that, in general, biodiesel use has a significant impact on all components of energy balance. Of total energy from fuel combustion, the share of energy losses to form of exhaust emissions the maximum value in all percentages allocated to biodiesel (Average 51.715 percent) with the maximum and minimum amount of B42 (55.982 percent) and B0 (46.481 percent), respectively (Fig. 2). Also, fuel blend with 12% biodiesel was diagnosed the best blend because of having the most useful power, having the lowest energy losses through the exhaust and cooling system. Conclusion Using biodiesel produced from waste cooking oil by transesterification process, lead to increase the useful power. The addition of biodiesel to pure diesel cause to significant reduction in the waste energy due to friction. In higher amounts of biodiesel increase energy losses especially through the exhaust and cooling system due to higher viscosity.
