Journal of Agricultural Machinery

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 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.

Abstract Introduction
Diesterol is a new specific term which denotes a mixture of fossil diesel fuel (D), vegetable oil methyl ester called biodiesel (B) and plant derived ethanol (E). Recently, much attention has been paid to the development of alternative fuels in order to meet the emission standards and to reduce the dependency on fossil fuel. Biodiesel and ethanol have been considered as major alternative fuels, as they are derived from renewable sources. These fuels are well oxygenated and therefore have a great potential to reduce emissions. Biodiesel is an oxygenated diesel fuel made from vegetable and animal fats by conversion of the triglyceride fats into esters via transesterification.

Materials and Methods
The engine test bed consisted of a diesel engine, a dynamometer, a gas analyzer and a fuel tank. The control bench also consisted of control units, data logger and a PC. Engine was loaded by a ferromagnetism dynamometer of 400 kW capacity and load was measured with spring balance. The experiments were designed using a statistical tool known as Design of Experiments (DoE) based on central composite rotatable design (CCRD) of response surface methodology (RSM) and the optimum points were found using RSM. Four experimental variables in the present study including the operating parameters, load and speed and the added volume of biodiesel and ethanol in one liter of diesel fuel were considered to be effective factors on the brake power and torque. Designs that can fit as a model must have at least three different levels in each variable. This is satisfied by central composite rotatable designs (CCRD), which have five levels per variable. The most successful and best among the designs is the central composite design which is accomplished by adding two experimental points along each coordinate axis at opposite sides of the origin and at a distance equal to the semi diagonal of the hyper cube of the factorial design and new extreme values (low and high) for each factor added in this design. In the present work, the response surface methodology based on desirability approach is used for the optimization of experiment parameters (load, speed, biodiesel and ethanol volume) for the measured properties of response (brake power and torque). The optimization analysis was carried out using SAS 9.2 software, where each response is transformed into a dimensionless desirability value (d) and it ranges between
d = 0, which suggests that the response is completely unacceptable, and d = 1, which suggests that the response is more desirable.

Results and Discussion
The resultant quadratic models of the response surface methodology were helpful to predict the response parameters including the performance characteristics of engine and further to identify the significant interactions between the input factors on the responses. By increasing the amount of biodiesel, the brake power is reduced compared to diesel fuel. This is due to two factors: the first is concerned with the percentage of biodiesel in the fuel mix because of the low calorific value of biodiesel compared to diesel fuel, calorific value fuel mixture is reduced. On the other hand, due to the high viscosity of biodiesel than diesel fuel combined with an increase in these enhanced features and fuel atomization when spraying will be difficult. It is generally desirable outcome of these two factors have prevented the ignition and brake power somewhat reduced. Increasing the volume percent biodiesel fuel mixture to the engine braking torque is reduced diesel fuel engines in all working conditions. The reason for this decline is the low calorific value of biodiesel compared to diesel fuel. Also, by increasing the concentration of ethanol in the fuel mix engine braking torque is reduced. The reason for this decline in addition to the low calorific value of ethanol compared to diesel fuel may be related to cetane number and low latent heat of vaporization of ethanol.

Conclusion
The results depicted that low percentages of biodiesel and bioethanol into synthetic fuel also somewhat have same power and torque but increasing biodiesel and ethanol contents into synthetic fuel reduced power and torque. The maximum brake power (79 kW) occurred for the pure diesel fuel (equivalent to D100B0E0) at 2800 rpm and full load (100%) and the most brake power (325 N.m) occurred for the pure diesel fuel (equivalent to D100B0E0) at 1630 rpm and full load (100%).

Abstract Introduction
Life cycle assessment of food products is an appropriate method to understand the energy consumption and production of environmental burdens. Dairy production process has considerable effect on climate change in various ways, and the scale of these effects depends on the practices of dairy industry, dairy farmers and feed growers. This study examined the life cycle of production of dairy products in Kermanshah city. For this purpose, the whole life was divided in two sections: production of raw milk in dairy farm and dairy products in dairy industry. In each section the energy consumption patterns and environmental burdens were evaluated. Based on the results, the consumed energy in dairy farm was 6286.29 MJ for amount of produced milk in month. Also animal feed was the greatest energy consumer with the value of 45.12% that the maximum amount of this value was for concentrate. The minimum consumption of energy was for the machinery with 0.92 MJ in a month. Results of life cycle assessment of dairy products showed that in dairy industry raw milk input causes most of impact categories especially land use, carcinogens and acidification. In dairy farms, concentrate was effective more than 90% in production of impact categories included: land use and carcinogens. Using digesters for production biogas and solar water heaters in dairy farm can decrease fossil recourses.

Materials and Methods
Based on ISO 14044, standards provide an overview of the steps of an LCA: (1) Goal and Scope Definition; (2) Life Cycle Inventory Analysis; (3) Life Cycle Impact Assessment; and (4) Interpretation (ISO, 2006). In this study there were two sub-systems in the production line: dairy farm sub-system (1) and dairy factory sub-system (2). In the sub-system related to the dairy farm, the main product was milk. Determination of inputs and outputs in each sub-system, energy consumption, transportation and emissions to air and water as well as waste treatment are the requirements of LCI. However each of them has several components. These components are different in both sub-systems. All the detailed data about energy equivalent in dairy farm is shown in Table 1. More detailed data about inventories description of two sub-systems are shown in Tables 3 and 4. The SimaPro 7.3.2 was used for analyzing the collected data for calculating environmental burdens (Pré Consultants, 2012).

Results and Discussion
Based on the developed models with SimaPro software for dairy products in the factory, various emissions were generated including emissions into the air, soil and water. The most prevalent emissions are summarized in Table 7. In warm season about half of the milk is processed into drinking yoghurt. Since water is one half of the component of this product so more amount of drinking yoghurt can be achieved with lower energy consumption (about 50%). Furthermore, these results indicated that the magnitude of fossil fuels was much greater than all others. It was followed by land use and respiratory inorganics. The most amount of the consumption of the fossil fuels was the production of energy requirements for heating systems at boilers and tractors in dairy factory and farm, respectively. Also the transportation of raw milk to the dairy industry was another source of the pollution. Also the energy consumption pattern in the dairy farm revealed that the concentrate have high contribution in energy consumption.

Conclusion
Results of the energy consumption pattern showed that the animal feed was the greatest energy consumer with value of 45.12% and followed by electricity (36%). Energy consumption index for the fossil fuel was calculated about 3.8 that is higher than the global index. Production of raw milk in dairy farm is responsible in the production of impact categories especially land use, carcinogenic and acidification with contribution of 97.6%, 78%, and 63%, respectively. Also the amount of CO₂-eq was estimated 2.71 kg for the production of 1kg ECM in cold seasons.

Abstract Introduction
Considering the low cost of the wind power production and its relatively good compatibility with the environment, wind farms have shown extensive growth in the past few years. Considering the importance of using the wind power and its advantages, the careful planning is needed to identify the available generation potentials in a region or a country to facilitate its increased use. By the end of 2009, the capacity of wind turbines installed in the wind farms of Iran was 92 MW, which demonstrates the significant potential for additional wind farms in the country and suggests investments in the wind power industry are likely cost effective. The main purpose of this research is to assess the potential of wind power for the city of Pars Abad in northwestern Iran.

Materials and Methods
In order to measure wind power density and wind energy potential, wind speed data collected every 3 hours at a height of 30 m above the ground for 11 consecutive years are analyzed; the data are provided by the Iranian Meteorological Organization and are used in the assessment of electricity production potential in the area chosen for the wind turbines installation. To determine the wind energy potential at a site and estimate the energy output from this site, statistical functions like probability functions are used. There are many probability functions but the Weibull distribution function is usually considered the most useful function for wind speed data analysis due to its simplicity and good accuracy. The Weibull probability density function is defined with two parameters of k and c as follows:
(1) f (v) = k/(c ) 〖( v/c )〗^(k-1) exp (- 〖( v/c )〗^k )
After calculating the Weibull function parameters, status of a location for wind energy potential can be assessed. A good way to assess the available wind resources is by calculation of the wind power density. This parameter indicates how much energy can be converted to electricity at a site and can be calculated as follows:
(2) P/A=1/2 ρc^3 Г ( (k+3)/k)
Wind energy density expresses the wind power density for a given time period T.The wind energy density for a definite site and in a given time period (one month or one year) (T) can be calculated as:
(3) E/A=1/2 ρc^3 Г ((k+3)/k) T
Results and Discussion
In this study, wind speed data collected in Parsabad, Iran, over a ten-year period (2005-2015) are analyzed, and the Weibull distribution parameters c and k, average wind speed, and average wind power and wind energy densities are determined.
According to Table 1, the minimum and maximum standard deviations of the average monthly indicators during 11 years in November and July are 0.63 and 2.51, respectively, and the minimum and maximum wind speeds of the average monthly indicators during 11 years in November and June are 2.09 ms^-1 and 4.87 ms^-1, respectively. The average annual Weibull scale parameter (c) is 3.84 while the average annual Weibull shape parameter (k) is 2.61. The average annual wind power density (P/A) during 11 years is 45 Wm^-2, while the average annual wind energy density (E/A) during 11 years is 389 kWhm^-2/year. Pars Abad in terms of generation potential of wind energy and based on quantitative classification for wind resource is located in weak to average region.

Conclusion
Pars Abad with an average wind power density of 45 Wm^-2 and average wind speed of 3.41 ms^-1 is not a good candidate for wind power plants and it is just suitable for off-grid electrical and mechanical applications such as charging batteries and pumping water for agricultural and livestock uses.

Abstract Introduction
The compaction of soil by agricultural equipment has become a matter of increasing concern because compaction of arable lands may reduce crop growth and yield, and it also has environmental impacts. In nature, soils could be compacted due to its own weights, external loads and internal forces as a result of wetting and drying processes. Soil compaction in sugarcane fields usually occurs due to mechanized harvesting operations by using heavy machinery in wet soils. Adding plant residues to the soil can improve soil structure. To improve soil physical quality of sugarcane fields, it might be suggested to add the bagasse and filter cake, which are the by-products of the sugar industry, to the soils.
When a soil has been compacted by field traffic or has settled owing to natural forces, a threshold stress is believed to exist such that loadings inducing lower than the threshold cause little additional compaction, whilst loadings inducing greater stresses than the threshold cause much additional compaction. This threshold is called pre-compaction stress (σpc). The σpc is considered as an index of soil compactibility, the maximum pressure a soil has experienced in the past (i.e. soil management history), and the maximum major principal stress a soil can resist without major plastic deformation and compaction. Therefore, the main objective of this study was to investigate the effects of wetting and drying cycles, soil water content, residues type and percent on stress at compaction threshold (σpc).
Materials and Methods
In this research, the effect of adding sugarcane residues (i.e., bagasse and filter cake) with two different rates (1 and 2%) on pre-compaction stress (σpc) in a silty clay loam soil which was prepared at two relative water contents of 0.9PL (PL= plastic limit, moist) and 1.1PL (wet) with or without wetting and drying cycles. This study was conducted using a factorial experiment in a completely randomized design with three replications. A composite disturbed sample of topsoil (0–200 mm deep) of a silty clay loam soil was collected from Isfahan province (32 31.530 N; 51 49.40E) in center of Iran. The mean annual precipitation and temperature of the region are about 160 mm and 16 C, respectively. Sugarcane residues (bagasse and filter cake) were obtained from the sugarcane fields in Ahvaz, Khuzestan province (Iran). The samples were air-dried and passed through a 2-mm sieve. Soil treated by bagasse and filter cake in different rates was poured and knocked lightly into cylinders with diameter and height of 25 and 8 cm, respectively. Large air-dry disturbed soil samples were prepared and some of them were exposed to five wetting and drying cycles. Finally, the soil surface was covered by a plastic sheet and was left overnight in the laboratory (for 24 hours) to enable the moisture to equilibrate. The loading tests were performed the next day. The pre-compaction stress was determined by plate sinkage test (PST). The loading test for PST was performed using CBR apparatus. The compression for PST was continuous at the same constant displacement rate of the CBR (i.e. 1 mm min^-1). Determination of the σpc was done using Casagrande’s graphical estimation procedure (Casagrande, 1936) in a program written in MatLab software.
Results and Discussion
The results showed that σpc was significantly decreased by adding residues to the soil at both water contents, and with/without wetting and drying process. For untreated treatments (control), the σpc decreased with increasing water content. Although σpc decreased with adding the residues to the soil, however, the effect of residue types and percentages and soil water content on σpc was not significant for the soil samples treated with residues.
Conclusion
In order to prevent re-compaction of the soil and improve its structure, it is suggested that traffic control system with permanent routes for the movement of machinery to be used in sugar cane plantations and the residues (after desalination) to be added into strips that are placed under cultivation.

Abstract Introduction
No use of advanced mechanization and weakness in post harvesting technology are the main reasons of agricultural losses. Some of these wastes (agricultural losses) are related to crop growing conditions in field and the remaining to processing of sugar in mill. The most useful priority setting methods for agricultural projects are the Analytic Hierarchy Process (AHP). So, this study presents an introduction of application manner of the AHP as a mostly common method of setting agricultural projects priorities. The purpose of this work is studying the sugarcane loss during production process using AHP in Khuzestan province.
Materials and Methods
The resources of sugarcane waste have been defined based on expert’s opinions. A questionnaire and personal interviews have formed the basis of this research. The study was applied to a panel of qualified informants made up of thirty-two experts. Those interviewed were distributed in Sugarcane Development and By-products Company in 2015-2016. Then, with using the analytical hierarchy process, a questionnaire was designed for defining the weight and importance of parameters effecting on sugarcane waste. For this method of evaluation, three main criteria considered, were yield criteria, cost criteria and income criteria. Criteria and prioritizing of them was done by questionnaire and interview with sophisticated experts. This technique determined and ranked the importance of sugarcane waste resources based on attributing relative weights to factors with respect to comments provided in the questionnaires. Analytical Hierarchy Process was done by using of software (Expert choice) and the inconsistency rate on expert judgments was investigated.
Results and Discussion
How to use agricultural implements and machinery during planting and harvesting of sugarcane, can increase or decrease the volume of waste. In planting period, the losses mainly consists of loss of setts during cutting them by machine, injury the setts by biological and physical agents, loss of growth in sett field, unsuitable sett covering and replanting the gaps. During cultivation period the losses include late in field harvesting and so late in regrows the cane, unsuitable ratooning and use of cultivator, varying the size of the furrows and ricks in around the field and destroyed the stubbles during rationing. In harvesting the losses easily seen and mainly associated by efficiency of harvester machines. Billets loss of the fleet in the transmission roads toward mill and late in harvest the burnet cane and then transport to mill are main sources of quantities and qualities of losses. The Expert Choice software performed well in conjunction with the panel of experts for choosing the criteria and assigning weights under the AHP methodology. According to the results, effective parameters on sugarcane waste consist of caused by harvesting, transportation, industry, planting, preserve operations, ratooning and land preparation. Weight of effective criteria (yield, cost and income) on losses of sugarcane obtained from paired comparison in the experts’ view which has been calculated with Expert choice software. The result of this survey by AHP techniques showed that yield criteria had the most and income criteria had the least importance for expert in sugarcane production. In this stage of research, alternatives of paired comparison relative to criteria was separately formed and information of questionnaire which relates to paired comparison of criteria was obtained. Between effective parameters on losses of sugarcane, harvesting with 0.243 weighted average was the most effective factor and transportation with 0.187 weighted average, industry with 0.179 weighted average, planting with 0.156 weighted average, preserve operations with 0.109 weighted average, ratooning with 0.071 weighted average, and land preparation with 0.055 weighted average was later, respectively (Inconsistence Rate =0.04). The results are examined by monitoring sensitivity analysis while changing the criteria priorities. Since different judgments are made on comparison of criteria, we use sensitivity analysis in order to provide stability and consistence of analysis. With increasing or decreasing of the criteria, we will conclude that ratio of other indices will not change.
Conclusion
This paper looks at AHP as a tool used in Sugarcane Agro-Industries to help in decision making. Results show that criteria studied in this research can help prioritizing of loss resources during sugarcane production process. According to the results, effective parameters on sugarcane waste consist of caused by harvesting, transportation, industry, planting, preserve operations, ratooning and land preparation.

Abstract Introduction
Agricultural productions has been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO₂ emissions coming from agriculture. Identification and assessment of environmental impact in the production system will be leading to achieve the goals of sustainable development, which would be achieved by life cycle assessment. To find the relationship between inputs and outputs of a production process, artificial intelligence (AI) has drawn more attention rather than mathematical models to find the relationships between input and output variables by training, and produce results without any prior assumptions. The aims of this study were to life cycle assessment (LCA) of Alfalfa production flow and prediction of GWP (global warming potential) per ha produced alfalfa (kg CO₂ eq.(ha alfalfa)^-1) with respect to inputs using ANFIS.
Materials and Methods
The sample size was calculated by using the Cochran method, to be equals 75, then the data were collected from 75 alfalfa farms in Bukan Township in Western Azerbaijan province using face to face questionnaire method. Functional unit and system boundary were determined one hectare of alfalfa and the farm gate, respectively. Inventory data in this study was three parts, included: consumed inputs in the alfalfa production, farm direct emissions from crop production and indirect emissions related to inputs processing stage. Direct Emissions from alfalfa cultivation include emissions to air, water and soil from the field. Data for the production of used inputs and calculation of direct emission were taken from the EcoInvent®3.0 database available in simapro8.2.3.0 software and World Food LCA Database (WFLD). Primary data along with calculated direct emissions were imported into and analyzed with the SimaPro8.2.3.0 software. The impact-evaluation method used was the CML-IA baseline V3.02 / World 2000. Damage assessment is a relatively new step in impact assessment. The purpose of damage assessment is to combine a number of impact category indicators into a damage category (also called area of protection). To assess the damage in this study, IMPACT 2002+ V2.12 / IMPACT 2002+ method was used. ANFIS is a multilayer feed-forward network which is applying to map an input space to an output space using a combination of neural network learning algorithms and fuzzy reasoning. In order to enable a system to deal with cognitive uncertainties in a manner more like humans, neural networks have been engaged with fuzzy logic, creating a new terminology called ‘‘neuro-fuzzy method. An ANFIS is used to map input characteristics to input membership functions (MFs), input MF to a set of if-then rules, rules to a set of output characteristics, output characteristics to output MFs, and the output MFs to a single valued output or a decision associated with the output. The main restriction of the ANFIS model is related to the number of input variables. If ANFIS inputs exceed five, the computational time and rule numbers will increase, so ANFIS will not be able to model output with respect to inputs. In this study, the number of inputs were ten, including machinery, diesel fuel, nitrogen, phosphate, electricity, water for irrigation, labor, pesticides, Manure and seed and GWP was as the model output signal. To solve this problem and employ all input variables, we proposed clustering input parameters to four groups. Correspondingly, the proposed model was developed using seven ANFIS sub-networks. To obtain the best results several modifications were made in the structure of ANFIS networks, and some parameters were calculated to compare the results of different models. Making a comparison between different topologies the employment of some indicators was a pivotal to get a good vision of various the structures, such as the correlation coefficient (R), Mean Square Error (MSE) and Root Mean Square Error (RMSE). In addition, for checking comparison between experimental and modeled data, the t-test was performed. The null hypothesis was equality of data average. To develop ANFIS models, MATLAB software (R2015a) was used.
Results and Discussion
Impact categories including Global warming potential (GWP), eutrophication potential (EP), human toxicity potential (HTP), terrestrial ecotoxicity potential (TEP), oxidant formation potential (OFP), acidification potential (AP), Abiotic depletion (AD) and Abiotic depletion (fossil fuels) were calculated as 13373 kg CO₂ eq, 19.78 kg PO4-2 eq, 2054 kg 1,4-DCB eq, 38.7 kg 1,4-DCB eq, 3.84 kg Ethylene eq, 90.64 kg SO₂ eq, 0.015 kg Sb eq and 205169 MJ, respectively. The results of damage assessment of alfalfa production revealed that electricity in three categories, human health damage, climate change and ecosystem quality had maximum role, but in the resources damage category was the largest share of damage related direct emissions. The value of the climate change was calculated as 13373 kg CO₂ eq. The best structure was including five ANFIS network in the first layer, two network in the second layer and a network in output layer. Values of R, MSE and RMSE for the final ANFIS in k-fold model were 0.983, 0.107 and 0.327 and in C-means model were 0.999, 0.007 and 0.082, respectively. The p-value in t-test was 0.9987 that indicates non-significant difference between the mean of modeling and experimental data. Coefficient of determination (R²) between actual and predicted GWP based on the best k-fold and C-means models were 0.994 and 0.99, respectively. The coefficient of determination for these index demonstrated the suitability of the developed network for prediction of GWP of alfalfa production in the studied area.
Conclusion
Based on the results of this study, to reduce the emissions, electricity consumption should be reduced. Adapting of electro pumps power with the well depth and the amount of required water taken for field will be a possible solution to reduce the use of electricity in order to trigger of electro pumps and thus reducing of emissions related to it. In some situations, the type of mineral fertilizer is the main determinant of emissions at the whole farm level and changing the type of fertilizer could significantly reduce the environmental impact. Comparison of GWP modeling results using two methods of k-fold and C-means revealed that C-means method has higher accuracy in prediction of GWP. Also the high quantities for the determination coefficient related to both modeling methods demonstrates high correlation between actual and predicted data.

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, 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.

Abstract Introduction
Anaerobic digestion (AD) is a process of breaking down organic matter, such as manure, in the absence of oxygen by concerted action of various groups of anaerobic bacteria. The AD process generates biogas, an important renewable energy source that is composed mostly of methane (CH₄), and carbon dioxide (CO₂) which can be used as an energy source. Biogas originates from biogenic material and is therefore a type of biofuel. Enhancement of biogas production from cattle dung or animal wastes by co-digesting with crop residues like sugarcane stalk, maize stalks, rice straw, cotton stalks, wheat straw, water hyacinth, onion waste and oil palm fronds as well as with liquid waste effluent such as palm oil mill effluent. Nevertheless, the search for cost effective and environmentally friendly methods of enhancing biogas generation (i.e. biogas yield) still needs to be further investigated. Many workers have studied the reaction kinetics of biogas production and developed kinetic models for the anaerobic digestion process. Objective of this study is to investigate the effect of biological additive using of organic loading rate (OLR) in biogas production from cow dung. In addition, cumulative biogas production was simulated using logistic growth model, and modified Gompertz models, respectively.
Materials and Methods
The study was performed in 2015-2016 at the agricultural research center of Ardabil Province, Moghan (39.39 °N, 48.88° E). Fresh cow manure used for this research was collected from the research farm of the Institute for Animal Breeding and Animal Husbandry, Moghan. It was kept in 30 l containers at ambient temperature until fed to the reactors. In this study, experiments were conducted to investigate the biogas production from anaerobic digestion of cow manure (CM) with effect of organic loading rate (OLR) at mesophilic temperature (35°C±2) in a long time experiment with completely stirred tank reactor (CSTR) under semi continuously feeding. The complete-mix, pilot-scale digester with working volume of 180 l operated at different organic feeding rates of 2 and 3 kg VS. (m^-3.d^-1). the biogas produced was measured daily by water displacement method and its composition was measured by gas chromatograph. Total solids (TS), volatile solids (VS), pH and etc. were determined according to the APHA Standard Methods. The biogas production kinetics for the description and evaluation of methanogens was carried out by fitting the experimental data of biogas production to various kinetic equations. In addition, Specific cumulative biogas production was simulated using logistic kinetic model exponential Rise to Maximum and modified Gompertz kinetic model.
Results and Discussion
The experimental protocol was defined to examine the effect of the change in the organic loading rate on the efficiency of biogas production and to report on its steady-state performance. The biogas produced had methane composition of 58- 62% and biogas production efficiency 0.204 and 0.242 m3 biogas (kg VS input) for 2 and 3 kg VS.(m^-3.d^-1), respectively. The reactor showed stable performance with VS reduction of around 64 and 53% during loading rate of 2 and 3 kg VS.(m^-3.d^-1), respectively. Other studies showed similar results. Modified Gompertz and logistic plot equation was employed to model the biogas production at different organic feeding rates. The equation gave a good approximation of the biogas yield potential (P) and correlation coefficient (R²) over 0.99.
Conclusion
The performance of anaerobic digestion of cow dung for biogas production using a completely stirred tank reactor was successfully examined with two different organic loading rate (OLR) under semi continuously feeding regime in mesophilic temperature range at (35°C±2). The methane content of 58- 62% and actual biogas yield of 0.204 and 0.242 m3 biogas.(kg VS input-1) were observed for 2 and 3 kg VS. (m^-3.d^-1), respectively. The modeling results suggested Modified Gompertz plot and Logistic growth plot both had higher correlation for simulating cumulative biogas production. Therefore, arising from the increasing environmental concern and prevailing wastes management crises, optimizing biogas production by 2 kg VS. (m^-3.d^-1) represents a viable and sustainable energy option.

Abstract Introduction
Biodiesel is Fatty Acid Methyl Esters (FAME) which is used as a renewable fuel in diesel engines. Extraction of lipid from various flora sources, including Sunflower, Palm, Canola or animal oils, with a Trans-Esterification reaction between alcohol and Triglyceride (TG), leads to production of Biodiesel and Glycerin.
The production cost of biodiesel is so important that is now considered as the greatest obstacle during scale-up process.
In this research, a model-type of biodiesel production unit (using vegetable oil source), was designed by Aspen HYSYS V7.2 software, then a great deal of the attempt was employed to optimize the overall yield against the processing parameters including: mass and energy consumption load, as well as some technical discussion regarding associated apparatuses.
Materials and Methods
Process Design
The simulation was carried out using Aspen HYSYS V7.2 employing Triolein (as TG), Oleic acid (as Free Fatty Acid (FFA)), and Oleat as biodiesel. Avoiding side-stream reactions as well as trans-esterification, the FFA content was taken to a mere 0.05% (%mass). Feed stream was considered as product of NaOH-catalyzed bi-reactor system operating at 60˚C and 1 atm with the overall conversion of 70% using two series reactors.
The ratio of TG to Alcohol is 1:3, however, owing to establish an appropriate reactor performance; this ratio was applied as 1:6 practically. The design was mainly intended to produce 480 m3d^-1 biodiesel with mass concentration of 99.65%.
Methanol was used in this investigation due to low cost, accessibility and handling considerations.
NRTL was taken as the Equation of State (EOS) for the process and should be used PRSV equation in the decanter.
Thermal Integration
Energy consumption was taken into account as basis of optimization in this study. Table 2 demonstrates the thermal characteristics of all streams consist of source and down-streams, while outlet stream like glycerol streams were neglected to be considered. HR-1, HR-2, HD1-1, HD2-1 and HD3-1 represent cooling water leaving reactors and condensers respectively which input cooling water temperature to utility was 25˚C. Cp also indicates the thermal capacity of each line which can be calculated by multiplying mass flow rate in specific heat capacity.
In order to calculate interval temperature, as the next step, the inlet and outlet temperatures of hot flow must be diffracted from the half of minimum approach temperature of exchangers; and the inlet and outlet cold temperatures should be summed with the half of minimum of approach temperature of exchangers. Interval enthalpy can also be calculated using following equation:
ΔH interval= ΔT interval [Cp Cold-Cp Hot]
Minimum approach temperature (ΔTmin) was also taken as 10°C in the following calculations. Results are shown in Table 3.
Results and Discussion
Mass Integration
Feed stream after reaching 60˚C and 1 atm entered into first reactor. Feed streams reacted in Reac.1, and effluent after cooling to 25˚C flowed to Sep.1. Unreacted oil sent to Reac.1 and effluent of this reactor after cooling to 25 ˚C entered into Sep. 2. Products of Reac.2 including glycerin, methanol, biodiesel and oil were conveyed to Sep.2 (25˚C) for separation of ester and glycerin. The light phase (Ester) was directed to a recycle distillation column (Dist.1) with R=1.5 and 6 trays to obtain extra-pure methanol from biodiesel. Second effluents from Sep.1 and Sep.2 including large quantities of methanol and glycerin were conveyed to second distillation tower (Dist.2) with 5 tray and R=1.5 in order to purify methanol recovery and obtain glycerin purity up to 99.63%. Due to declining expenditure, methanol recycled back to the beginning of process as a feed; while glycerin was sent out to downstream as by-product.
Effluent exited from Dist.2 flowed to Sep.3 to improve purity and remove any residual catalysts (NaOH) via HCl reaction. HCl and catalyst entered with identical molar flow and reacted with 95% conversion.
The cold and hot energy required for the whole processes were calculated: 18860 kW and 17330 kW respectively.
Heat Integration
According to Table 3 network required hot and cold energy were found to be zero and 17146.6 kW respectively; where the number “zero” indicates hot streams are able to provide energy needed of cold stream. Care should be taken that the exchanger approach temperature should not be less than the minimum selected approach temperature (ΔTmin).
Applying the new system in the process, cold and hot energy reduced to 17018 kW and 16670 kW respectively.
According to Figure 2, HEX-8 outlet stream temperature reached 291.8 ˚C after heat transfer. On the other hand, required temperature and heat of distillation tower’s re-boiler were 187.6 ˚C and 1858 kW respectively; therefore this could be used as energy source for the second distillation tower’s re-boilers. The output stream of the 3rd distillation tower virtual exchanger (SHD 3-out) was also important; this stream temperature was 565 ℃ that could be used to provide energy in the 1st distillation column re-boiler.
Finally cold energy and hot energy reduced by 19.6% and 38% reaching 15160 kW and 10990 kW respectively. Input and output streams of the process data and the main process flow diagram of the biodiesel process production are shown in table 4 and fig.3.
Conclusion
Using stream recycle and mass integration methanol, unreacted oil and feed oil consumption reduced up to 60.6%, 70% and 9% respectively. Consequently, due to energy integration by exchanger network, cold and hot energy was reduced by 19.6% and 38% respectively. This integration increases the number of exchangers and pumps power due to the integration target, because the mass and heat integration targets are just reducing the mass and heat consumption. As can be seen from table 5, the number/capacity of used facilities increased in some cases as a result of application of integration method; this item can be optimized depending on economic and operating data and changing the final target to reduce overall cost, for this purpose can be used other methods such as genetic algorithms.

Abstract Introduction
Winter wheat is an important, well-adapted grain crop under dryland condition of the northwest of Iran. Soil water is the most limiting resource for crop growth in dryland areas. Therefore, farmers need to use crop residues and minimum tillage to control the soil erosion and effectively store and to use the limited precipitation received for crop production.
Crop rotation and tillage system could affect crop yield due to their effects on water conservation and soil chemical and physical properties. Galantini et al., (2000) studied the effect of crop rotation on wheat productivity in the Pampean semi-arid region of Argentina and found that a wheat–vetch (Vicia sativa L.) rotation resulted in higher yield and protein content, and greater yield components than the other rotations.Payne et al. (2000) stated that where precipitation amount is marginal (400 mm), dry field pea offers a potential alternative to summer fallowing. The purpose of this study was to identify the optimal tillage system for increasing crop productivity in a vetch–wheat rotation in dryland farming of the northwest of Iran.

Materials and Methods
The field experiment was carried out from 2010 to 2014 at the Dryland Agricultural Research Station (latitude37° 12´N; longitude 46◦20´E; 1730 m a.s.l.), 25 km east of Maragheh, East Azerbaijan Province, Iran. The long-term (10 years) average precipitation, temperature and relative humidity of the station are 336.5 mm, 9.4 ◦C and 47.5%, respectively. The soil (Fine Mixed, Mesic, Vertic Calcixerepts, USDA system; Calcisols, FAO system) at the study site had a clay loam texture in the 0–15 cm surface layer and a clay texture in the 15–80 cm depth.
This study was conducted in vetch (Vicia pannonica)- wheat (Triticum aestivum L.) rotation. The experiment was arranged in a randomized complete block design with four replications. The tillage treatments consisted of (1) conventional tillage: moldboard plowing followed by one pass of a disk harrow (CT); (2) reduced tillage:chisel packer (CH); (3) minimum tillage: Stubble mulch cultivator (MT); and (4) no-till (NT) with retained previous crop residue.
At beginning prior to the tillage operation, only wheat stubble was present on the soil surface. A uniform tillage treatment was applied to all plots using a chisel packer in October. A shallow tillage was also performed using a tandem disk harrow just prior to winter vetch planting. In the second, third, fourth and fifth years, the tillage treatments for the vetch and wheat planting were similar. A winter wheat cultivar (Azar 2) was sown 6 cm depth at a rate of 350 seeds per square meter with an Alvand conventional and Baldan NT 250 no-till drill. Vetch cultivar Golsefied was drilled 8 cm depth at a seeding rate of 85 kg ha⁻¹ using Alvand drill.
The following parameters were measured: heads of wheat per square meter, 1000-kernel weight, kernels per head, head length, plant height, and wheat grain yield. Grain yield was obtained with a plot combine harvester. The dry matter content was determined and yield corrected to a standard moisture content of 130 g kg⁻¹.
Rain use efficiency (RUE) was calculated by dividing dry weight of grain yield by growing season precipitation.
Soil water content and dry bulk density were measured gravimetrically (drying method, w/w) in cropping seasons.

Results and Discussion
Conservation tillage treatments resulted in water saving in soil layers. In both stages of soil sampling, the most soil moisture variability to initial state was observed in plots which planted as no-tillage. The moisture variability of no-tillage system was 23.4% higher than that of conventional tillage system at 10-20 cm soil layer in flowering stage of wheat.
Effect of treatments on soil bulk density in different soil depths illustrated that conservation tillage can reduce soil bulk density during four years.
According to the results of this study the overall infiltration in no-tillage was 1.58 times more than that of conventional tillage system.
Yields under no-tillage and reduced tillage were higher (4% and 6% respectively) than conventional tillage. Grain yields under direct drilling were similar to those obtained using the reduced-tillage (Chisel packer) system.

Conclusion
Based on the results of a 4-year field study on a dryland production system in the northwestern cold continental climate of Iran, minimum- or no-till winter wheat crop production in a vetch–wheat rotation were the most efficient soil management practice from the standpoint of grain yield production and rain use efficiency. Overall, in this study, the no-tillage treatment is proposed as the best treatment in terms of grain and biomass yields and mechanical properties of soil.

Abstract Introduction
Increasing the area of absorber plate between the flowed air through the duct can be accomplished by corrugating the absorber plate or by using the artificial roughness underside of the absorber plate as the commercial methods for enhancing the thermohydraulic performance of the flat plate solar air heaters. Evaluation of this requires the construction of separated solar air heater which is costly and time consuming. The constructed solar flat-plate collector simulator can be a sufficient solution for obtaining the heat transfer and thermodynamic parameters for evaluating the absorber plate. The inclined broken roughness was chosen as the optimum roughness which is surrounded by three aluminum smooth walls.
Materials and Methods
The duct for both smooth and roughened plate have been constructed based on the ASHRAE 93-2010 standard. In order to achieve a fully thermal and hydraulic developed flow, the plenum is constructed. The centrifugal fan is considered by applying the required air volume at the pressure drop obtained by the duct, plenum and the orifice meter. The TSI velocity-meter 8355 is used to measure the velocity of air crossing through the pipe connected to the centrifugal fan. The micro manometer Kimo CPE310-s with the resolution of 0.1 Pa is used to measure the pressure drop across the test section of the smooth and roughened duct. The LM35 sensors are used to measure the absorber plate and air temperature through the test section. Obtained parameters are used to calculate the Nusselt number and friction factor across the test section for smooth and roughened absorber plate. The Nusselt number and friction factor parameters which is obtained for smooth absorber plate based on experimental set-up, is compared with Dittus-Bolter and Blasius equations, respectively, for validating the simulator. By calculating the Nusselt number and friction factor, Stanton number is obtained based on the equation (6), and thermohydraulic coefficient is calculated by the equation (5) for the desired roughness.
Results and Discussion
Pressure drop for smooth duct is obtained to be 20 Pa. Maximum velocity crossed through the plenum is calculated by the equation (8). Thereafter, pressure drop for plenum by considering the maximum velocity in equation (7), is obtained to be 1.16 Pa. The same procedure for maximum velocity which is crossed through the orifice meter is obtained by the equation (10) and then the pressure drop for orifice meter is calculated equal to 243 Pa by considering the velocity in equation (9). Total pressure is given by the equation (11) to be 246.16 Pa. The required power for centrifugal fan is obtained equal to 105 W from equations (12), (13) and (14), respectively. Both aforementioned Nusselt number variations with Reynolds number were monotonously increased by increasing the Reynolds number. The gained RMSE and coefficient of determination between the Nusselt numbers are 0.0566 and 0.6944, respectively. The obtained RMSE and coefficient of determination between the friction factors are 0.0004 and 0.6814, respectively. The low value of the RSME and high value of the R2 analysis for both Nusselt number and friction factor shows that there is a good agreement between the experimental data and empirical correlations. Fig. 8 demonstrates that the thermohydraulic coefficient is decreasing as the Reynolds number increased. The effect of friction factor related to the Stanton number is shown up more effective by increasing the Reynolds number. It should be noted that the same procedure is conducted for Han's experiment where the thermohydraulic performance is decreased as the Reynolds number increased. The maximum magnitude of the thermohydraulic performance was achieved at minimum 3149 Reynolds number.
Conclusion
The flat-plate solar collector simulator was designed based on the ASHRAE 93-2010 standard which consists of the centrifugal fan, chosen based on the required air volume by considering the pressure drop in the duct, plenum and orifice meter. The experiment was conducted between 3149 to 19247 Reynolds numbers. The good agreement between the comparison of the Nusselt number and friction factor obtained by the experiment for smooth duct was achieved by the Dittus-Bolter and Blasius equations, respectively, to validate the simulator. The obtained thermohydraulic coefficient for optimized roughness surrounded by three smooth walls was lower than the former investigated roughnesses at each Reynolds number

Abstract Introduction
Vibrations include a wide range of engineering sciences and discuss from different aspects. One of the aspects is related to various types of engines vibrations, which are often used as power sources in agriculture. The created vibrations can cause lack of comfort and reduce effective work and have bad influence on the health and safety. One of the important parameters of the diesel engine that has the ability to create vibration and knocking is the type of fuel. In this study, the effects of different blends of biodiesel, bioethanol and diesel on the engine vibration were investigated. As a result, a blend of fuels such as synthetic fuel that creates less vibration engine can be identified and introduced.
Materials and Methods
In this study, canola oil and methanol alcohol with purity of 99.99% and the molar ratio of 6:1 and sodium hydroxide catalyst with 1% by weight of oil were used for biodiesel production. Reactor configurations include: maintaining the temperature at 50 ° C, the reaction time of 5 minutes and the intensity of mixing (8000 rpm), and pump flow, 0.83 liters per minute. A Massey Ferguson (MF) 285 tractor with single differential (2WD), built in 2012 at Tractor factory of Iran was used for the experiment. To measure the engine vibration signals, an oscillator with model of VM120 British MONITRAN was used. Vibration signals were measured at three levels of engine speed (2000, 1600, 1000 rpm) in three directions (X, Y, Z). The analysis performed by two methods in this study: statistical data analysis and data analysis using Adaptive neuro-fuzzy inference system (ANFIS).
Statistical analysis of data: a factorial experiment of 10×3 based on completely randomized design with three replications was used in each direction of X, Y and Z that conducted separately. Data were compiled and analyzed by SPSS 19 software. Ten levels of fuel were including of biodiesel (5, 15 and 25%) and bioethanol (2, 4 and 6%), and diesel fuel.
Data analysis by ANFIS: ANFIS is the combination of fuzzy systems and artificial neural network so that it has both benefits. This system is useful to solve the complex non-linear problems in agricultural engineering applications such as systems involved in the soil, plant and air. ANFIS by linguistic concepts can establish and inference non-linear relationship between inputs and outputs. In this research, modeling was generally performed by Toolbox of ANFIS and coding in MATLAB too. Five important and effective factors in modeling were optimized until the best ANFIS model is obtained. The five factors were: type of input fuzzy sets, the number of input fuzzy sets, fuzzy set of output, methods of optimization and the number of epochs.
Results and Discussion
Based on the total vibration acceleration values for different fuels in different rpm, pure diesel (B5E4D91) had the highest vibration and the lowest vibration was seen in the mixed fuel of B25E4D71. Based on the results, two combined fuel of (B25E2D73, B25E4D71) have the lowest vibration and highest amount of biodiesel fuel (25%). After them, three combined fuels of (B5E2D83, B5E4D81, and B5E6D79) have created more vibration and the lowest amount of biodiesel fuel in this study (5%) has created the greatest amount of vibration. With increasing engine speed, the number of combustion courses and piston shock per unit of time increases. As a result, the engine body vibration increases. The results are consistent with results from other researchers.

Conclusion
In this study, motor vibration of MF285 tractors, by replacing a portion of diesel fuel with biodiesel produced from canola oil and bioethanol, was investigated. In the beginning, necessary biodiesel fuel was produced by research reactor in biodiesel workshop, and then different percentages of diesel and bio-ethanol were mixed to biodiesel and ten combined fuels were created. Finally the effect of different fuel combinations and different engine rotational speeds on the tractor engine vibrations was studied based on a factorial randomized complete block design and then analyzed and modeled by ANFIS. The results showed that the vibration of pure diesel fuel had the highest vibration. Also, with increasing biodiesel fuel blends, the amount of vibration reduced significantly. Increase in engine speed had direct effect on increasing the amount of vibration. Also by increasing the percent of bioethanol from 0 to 4%, the amount of vibration was reduced then vibration value increased by raising the percent of bioethanol. After modeling and analyzing, our results showed that the best fuel in terms of having the lowest vibration motor was B25E4D71.

Abstract Introduction
Nowadays, due to higher environmental pollution and decreasing fossil fuels many countries make decisions to use renewable fuels and restrict using of fossil fuels. Renewable fuels generally produce from biological sources. Biodiesel is an alternative diesel fuel derived from the transesterification of vegetable oils, animal fats, or waste frying oils. Considering the differences between diesel and biodiesel fuels, engine condition should be modified based on the fuel or fuel blends to achieve optimum performance. One of the simplest and yet the most widely used models is the thermodynamic model. After verification of the data obtained by model with experimental data it is possible to generalize the extracted data to an unlimited number of functional conditions or unlimited number of fuel types which saves time and reduces costs for experimental engine tests. Using the second law of thermodynamics, it is possible to calculate and analyze the exergy of the engine.4

Materials and Methods
In this work, the zero-dimensional model was used to account for internal energy variations, pressure work, heat transfer losses to the solid walls and heat release. The applied assumptions include:
The cylinder mixture temperature, pressure and composition were assumed uniform throughout the cylinder. Furthermore, the one-zone thermodynamic model assumes instantaneous mixing between the burned and unburned gases. The cylinder gases were assumed to behave as an ideal gas mixture, Gas properties, include enthalpy, internal energy modeled using polynomial equations associated with temperature.
In this research, the equations 1 to 20 were used in Fortran programming language. The results of incylinder pressure obtained by the model were validated by the results of experimental test of OM314 engine. Then the effects of injection timing on Energy and Exergy of the engine were analyzed for B20 fuel.

Results and Discussion
Comparing the results of the model with the experimental data shows that there was a good agreement between the model and experimental results. The results showed that advancing fuel injection timing increases the peak cylinder pressure. When fuel injecting occurs before the standard injection timing, the pressure and temperature of the charged air in the cylinder is less than that of the fuel when it is injected at standard injection timing. Thus, ignition delay of the injected fuel extends further. As a consequence, the reaction between fuel and air improves, which prepares a good mixture for burning. When the combustion starts, the rate of heat release increases in the premixed or rapid combustion phase of the combustion process due to the suitability of the mixture of air and fuel and hence the peak pressure of cylinder increases. When the injection timing is retarded, the fuel is injected into charged air that has a high temperature and pressure. Thus, in the injection timing of 10 degrees before top dead center, the maximum of incylinder pressure and temperature are reduced compared to the standard injection timing. By retarding the fuel injection into the cylinder, the indicator availability, the heat loss availability by heat transfer from cylinder walls and irreversibility are increased and by advancing the fuel injection into the cylinder, the indicator availability, the heat loss availability by heat transfer from the cylinder walls and irreversibility are reduced. High temperature will increase the produced entropy, so by advancing the injection timing the produced entropy will increase while the retarding injection timing reduces the produced entropy. Exergy and energy efficiencies increased by advancing the injection timing. At 2000 rpm the total availability and heat loss availability by heat transfer was increased compared to 1200 and 1600 rpm.

Conclusion
The proposed model was able to predict the pressure and temperature of the cylinder at different injection timings. By advancing the fuel injection timing energy and exergy efficiency and heat loss availability by heat transfer was increased. At 2000 rpm the total availability and heat loss availability by heat transfer was increased.

Abstract Introduction
As reported by Sabetghadam (2005), 53.4, 36.3, 1.1, 8.9, and 0.2% of total energy consumption in Iran consisted of oil products, natural gas, coal, electricity energy, and modern energy sources, respectively. The modern energies included solar, biomass, wind and nuclear. The energy mix has been evolving towards clean energies. From 1966–2005, the contribution of natural gas increased from 1.3% to 36.3% and the contribution of electricity was doubled from 4.1% to 8.9%. The share of oil products in domestic consumption has dropped from 84.3% to 53.4%.
Iran has abundant renewable energy resources, including solar energy, wind power, geothermal energy, and biomass, as well as the ability to manufacture the relatively labor-intensive systems that harness these. By developing such energy sources developing countries can reduce their dependence on oil and natural gas, creating energy portfolios that are less vulnerable to price rises. In many circumstances, these investments can be less expensive than fossil fuel energy systems. Over the past ten years some researches on solar and biomass energy have resulted in development and the establishment of a few small- and medium-scale electricity generation plants, powered via solar and biomass energy. There has also been the development of digesters to increase biogas production. Renewable energy is new to Iran and there is a long way to go. Except for the few afore mentioned projects, small-scale technologies to bring power to remote villages have a better chance of being adopted than those implemented at the national level.
Materials and Methods
In this research the amount of generated methane (methane content of biogas %) from co-digestion of municipal sewage, kitchen waste, and cow dung was measured in 7 different combinations (treatment). Two important parameters affecting methane production such as volatile solid (VS) and total solid (TS) were measured according to Method 1684 and CEN/TS 15148. Furthermore some environmental conditions such as temperature, pH, EC and some of the most important elements of desired substrate such as amount of C, N, P, K, and SO42- were determined. pH using pH-meter and EC using EC-meter, C using titration method according to Rongping et al. (2010) and N using Kjeldahl apparatus, P using Spectrophotometer, K using Flame photometer, and SO42- using weighting were determined according to Standard Method for the Examination of Water and Wastewater.
Methane (CH4) was determined using a multi-function gas detector brand GMI Ltd model GT-42. Its detection ranges were 0–10000 ppm (parts per million), 0–100 % LEL (lower explosive limit), and 0–100 % VOL (volume) in temperature limit -20–50 °C and 0–95% R.H (relative humidity).
Results and Discussion
The mean amount of methane contents of biogas during the co-digestion of the substrates for all 7 treatments reported in table 1 were 4363.25, 875.13, 169.13, 3424.38, 2911.88, 2714.38, and 193.5 ppm, respectively. Methane contents obtained from municipal waste was the highest among the substrates and after that the combination of 1:1:1 of the substrates was more than the others. The methane content was low in the first seven days of digestion, and thereafter rapidly increased over 85% within 22 days. Totally the highest methane contents of treatments were during 30–35 days of digestion which was agreed with other researches. This can be shown in Fig. 5 that the highest methane content was 10000 ppm and appertain to treatment 1.
The results showed that TS and VS of kitchen waste were lower than the other substrates. These findings agreed with Chen et al. (2010) researches in which had been reported the commercial kitchen waste has lower TS and VS contents, possibly because the commercial kitchen waste stream contains food with higher moisture contents such as fruits. Furthermore it can be shown that after digestion, the amount of TS of municipal waste, cow manure, and kitchen waste decreased 46, 57, and 46% respectively, while amount of VS of these substrates decreased 82, 92, and 85%, respectively. The results were similar to Chen et al. (2010) results. They reported that between 58 and 99% of the VS were degraded to methane and carbon dioxide under most feed concentrations. The obtained methane significantly correlated with VS, TS at level of 5 %. The pH of the substrate nearly was constant during the digestion. The results showed that the treatments with more municipal waste had more VS and TS while the treatments with more cow dung had more C/N.
Some mathematical models were made between the properties and generated methane. The best empirical model which can estimate amount of generated methane using the properties was a polynomial function. The function coefficients were determined for each parameter by normalizing them. Finally the results show that the model made using difference of VS and TS before and after of digestion had the most accuracy among the models (R²=0.897, RMSE=630, SSE=4.76e+06).
Conclusion
The results of conducted methane fermentation study on physico-chemical properties of substrates including municipal waste, kitchen waste and cow dung reviled that VS, TS, C/N, P, K, and SO42- affect biogas and methane production. However the correlation between methane contents with VS and TS was more than the other properties and the methane estimation models made using the VS and TS was more accurate than the other models.

Abstract Introduction
Electrical performance of solar cells decreases with increasing cell temperature, basically because of growth of the internal charge carrier recombination rates, caused by increased carrier concentrations. Hybrid Photovoltaic/thermal (PVT) systems produce electrical and thermal energy simultaneously. PVT solar collectors convert the heat generated in the solar cells to low temperature useful heat energy and so they provide a lower working temperature for solar cells which subsequently leads to a higher electrical efficiency.
Recently, in Iran, the reforming government policy in subsidy and increasing fossil fuels price led to growing an interest in use of renewable energies for residual and industrial applications. In spite of this, the PV power generator investment is not economically feasible, so far. Hybrid PVT devices are well known as an alternative method to improve energy performance and therefore economic feasibility of the conventional PV systems. The aim of this study is to investigate the performance of a PVT solar water heater in four different cities of Iran using TRNSYS program.

Materials and Methods
The designed PVT solar water system consists of two separate water flow circuits namely closed cycle and open circuit. The closed cycle circuit was comprised of a solar PVT collector (with nominal power of 880 W and area of 5.6 m²), a heat exchanger in the tank (with volume of 300 L), a pump and connecting pipes. The water stream in the collector absorbs the heat accumulated in the solar cells and delivers it to the water in the tank though the heat exchanger. An on/off controller system was used to activate the pump when the collector outlet temperature was higher than that of the tank in the closed cycle circuit. The water in the open circuit, comes from city water at low temperature, enters in the lower part of the storage tank where the heat transfer occurs between the two separate circuits. An auxiliary heater, connected to the tank outlet, rises the fluid temperature to the set point.
The performance of the designed system has been investigated in different cities (including Tabriz, Tehran, Kerman and Bandar-Abbas) during 4 seasons of year using Transient System Simulation (TRNSYS) program. The performance parameters included electrical and thermal energy generation and solar fraction. Solar fraction, which expresses the share of energy supplied by solar radiation on the collector in total thermal energy consumption, was obtained from equation 1.

Results and Discussion
The results showed that the average daily electricity generation in the cities for summer and winter were 4.65 and 2.67 kWh day-1, respectively. The annual electricity generation of the designed system is almost constant in the various cities. In winter, in spite of lower solar intensity and sunny hours, lower average temperature of solar cells in Kerman leads to a slightly better electrical performance than Bandar-Abbas. The highest cell temperatures, in Bandar-Abbas between 12 noon and 1pm, were found to be 33, 37, 31 and 25 ^oC in spring, summer, autumn and winter, respectively.
Thermal energy generation was significantly different at various cities and seasons. In winter, the designed system provides a little fraction (below 10 percent) of thermal demands in Tabriz and Tehran. This is mainly because of the low ambient temperature and solar intensity. The PVT system had a maximum average thermal energy of 16 kWh day^-1 and solar fraction of 0.5 which were observed in Bandar-Abbas. Tabriz, because of the lowest ambient temperature, had the least thermal energy generation and solar fraction. The maximum average solar fraction obtained in summer was about 60% while its lowest value in winter was 24%.

Conclusion
In the present study, a hybrid PVT solar water heater with nominal power of 880 W was proposed for application in Iran. The system was comprised of a PVT solar water collector, an auxiliary heater, a pump and connecting tubes. Technical feasibility of applying the proposed system in different cities was investigated using TRNSYS program. The results are summarized as follows:
The annual electricity generation of the designed system was almost constant in the various cities.
The highest and lowest values of average electricity generation in summer and winter were determined to be 4.65 and 2.67 kWh day^-1, respectively.
The PVT system had the maximum average thermal energy of 16 kWh day^-1 and solar fraction of 50%, which was observed in Bandar-Abbas.

Abstract Introduction
Biofuels are considered as one of the largest sources of renewable fuels or replacement of fossil fuels. Combustion of plant-based fuels is the indirect use of solar energy. Biofuels significantly have less pollution than other fossil fuels and can easily generate from residual plant material. Waste and residues of foods and wastewater can also be a good source for biofuel production.
Transesterification method (one of biodiesel production methods) is the most common forms to produce mono-alkyl esters from vegetable oil and animal fats. The procedure aims are reduction the oil viscosity during the reaction between triglycerides and alcohol in the presence of a catalyst or without it. In this study, the method of transesterification with alkaline catalysts is used that it is the most common and most commercial biodiesel production method.
In this study, configurations of made hydrodynamic cavitation reactor were studied to measure biodiesel fuel quality and enhanced device performance with optimum condition. The Design Expert software and response surface methodology were used to get this purpose.
Materials and Methods
Transesterification method was used in this study. The procedure aims were reduction of the oil viscosity during the reaction between triglycerides and alcohol in the presence of a catalyst or without it. Materials needed in the production of biodiesel transesterification method include: vegetable oil, alcohol and catalysts. The used oil in the production of biodiesel was sunflower oil, which was used 0.6 liters per each test in the production process base on titration method. Methanol with purity of 99.8 percent and the molar ratio of 6:1 to oil was used based on titration equation and according to the results of other researchers. The used catalyst in continuous production process was high-purity sodium hydroxide (99%) that it is one of alkaline catalysts. Weight of hydroxide was 1% of the used oil weight in the reaction.
Response surface methodology: Three important settings of reactor were considered to optimize reactor performance, which include: inlet flow to reactor, reactor rotational speed and the fluid cycle time in the system. Each set was considered at three levels. The factorial design was used to the analysis without any repeat, there will be 27 situations that because of the cost of analysis per sample by GC, practically not possible to do it. Therefore, response surface methodology was used by Design Expert software. In the other words, after defining the number of variables and their boundaries, software determined the number of necessary tests and the value of the relevant variables.
Results and Discussion
Three parameters include the inlet flow to reactor, reactor rotational speed and the fluid cycle time in the system were considered as input variables and performance of reactor as outcome in analyzing of extracted data from the reactor and GC by Design Expert software. The results of tests and optimization by software indicated that in 3.51 minutes as retention time of the raw material of biodiesel fuel in the system, the method of transesterification reaction had more than 88% Methyl ester and this represents an improvement in reaction time of biodiesel production. This method has very low retention time rather than biodiesel fuel production in conventional batch reactors that it takes 20 minutes to more than one hour.
Conclusion
According to the researches, efficiency of biodiesel fuel production in hydrodynamic cavitation reactors is higher than ultrasonic reactors so in this study, the settings of hydrodynamic reactor were investigated so that the settings were optimized in production of biodiesel fuel. Sunflower oil was used in this research. The molar ratio of Methanol to oil was 6 to 1 and sodium hydroxide as a catalyst was used. Three important settings of reactor were considered which include: inlet flow to reactor, reactor rotational speed and the fluid cycle time in the system. The results were analyzed by gas chromatography. The results showed that at 8447 rpm of reactor speed, inlet flow of reactor at 0.86 liters per minute and 1.02 minute of circulation time, the best performance of reactor were created. The flash point, kinematic viscosity and density of biodiesel in this study were 172 °C, 2.4 square millimeters per second and 861 kg per cubic meter, respectively. Maximum and minimum performances of hydrodynamic cavitation reactor in biodiesel production were 6.19 and 1.13 mg kJ^-1, respectively.

Abstract Introduction
The significant of solar energy as a renewable energy source, clean and without damage to the environment, for the production of electricity and heat is of great importance. Furthermore, due to the oil crisis as well as reducing the cost of home heating by 70%, solar energy in the past two decades has been a favorite of many researchers. Solar collectors are devices for collecting solar radiant energy through which this energy is converted into heat and then heat is transferred to a fluid (usually air or water). Therefore, a key component in performance improvement of solar heating system is a solar collector optimization under different testing conditions. However, estimation of output parameters under different testing conditions is costly, time consuming and mostly impossible. As a result, smart use of neural networks as well as CFD (computational fluid dynamics) to predict the properties with which desired output would have been acquired is valuable. To the best of our knowledge, there are no any studies that compare experimental results with CFD and ANN.
Materials and Methods
A corrugated galvanized iron sheet of 2 m length, 1 m wide and 0.5 mm in thickness was used as an absorber plate for absorbing the incident solar radiation (Fig. 1 and 2). Corrugations in absorber were caused turbulent air and improved heat transfer coefficient.
Computational fluid dynamics
K-ε turbulence model was used for simulation. The following assumptions are made in the analysis.
(1) Air is a continuous medium and incompressible.
(2) The flow is steady and possesses have turbulent flow characteristics, due to the high velocity of flow.
(3) The thermal-physical properties of the absorber sheet and the absorber tube are constant with respect to the operating temperature.
(4) The bottom side of the absorber tube and the absorber plate are assumed to be adiabatic.
Artificial neural network
In this research a one-hidden-layer feed-forward network based on the back propagation learning rule was used to simulate the output temperature of a solar collector. The number of neurons within the hidden layer varied from 1 to 20. The hyperbolic tan- sigmoid and pure-line were used as the transfer function in the hidden layer and output layer, respectively. Minimization of error was achieved using the Levenberg-Marquardt algorithm. To carry out the aforementioned steps, the dataset (105 observations) was split into training (70 observations), and test (35 observations) data. Training sets used to develop models included air velocity, solar radiation, time of the day, ambient moisture and temperature values as inputs with an associated temperature of the collector as outputs. The aim of every training algorithm is to reduce this global error by adjusting the weights and biases.
Results and Discussion
Compare experimental results with ANN
The performance of the three-layer ANN for the prediction of output temperature of flat-plate solar collector by the Levenberg–Marquardt training algorithm was illustrated in Fig. 4. ANN predicted output temperatures with R² and RMSE of 0.92 and 1.23, respectively. Furthermore, the maximum error in prediction of output temperature of solar collector was 3.3 K. These results are in agreement with Tripathy and Kumar, (2009) those who have predicted the output temperatures of food product in the solar drier using ANN with and RMSE of 0.95 and 0.77, respectively.
Compare experimental results with CFD simulation
Fig. 6 shows that over the starting length of the absorber plate, there is a variation of the velocity profile which is caused by sharp geometry and it leads to some recirculation of the air in this part of absorber plate. After this part of boundary layers, flow is fully developed and velocity profile becomes smoother and constant.
Fig. 8 shows that the predicted temperature was within the experimentally measured temperature. The highest differences between simulated and experimental temperatures were around -2.4K to 4.6K for different time periods. The temperature differences of 4K were reported by Selmi et al. (2008). This disagreement is due possibly to the fact that there are unknown experimental inputs such as turbulence intensity, radiative heat loss from the absorber sheet to the surroundings, Leakage, and measurement tool errors which were not accounted in the model simulations. These losses by radiation are significant at high irradiation levels. This result agrees with studies done in Badache et al. (2012).
Thickness of absorber plate and radiation loss, in CFD model, does not take into consideration. For this reason maximum output temperature is seen in maximum radiation which is 12 p.m. While in real condition, it takes some time for absorber plate to get to its maximum temperature.
Moreover, the numerical temperature is smaller than the real temperature after 12 p.m. This may occur because of the thickness of metal which keeping the absorbed heat and losing it after awhile. Generally there is a time step hysteresis for the numerical temperature.
Conclusion
According to this study it can be concluded that the ANN operates better than CFD to predict the output temperature operation. However, ANN method does not give any information about the prediction of temperature distribution and velocity profiles in the solar collector. Although prediction accuracy of the CFD method is less than ANN method, but the provided information on the velocity and temperature profile of the solar collector is still valuable.

Abstract Introduction
The lignocelluloses materials have high potential for producing various types of biofuels. These materials include various parts of plants, especially leaves and stems that are left without a specific usage after annual pruning. These residues can be used through slow or fast pyrolysis process for production of liquid and gaseous biofuels. The slow pyrolysis is taking place at temperatures below 500°C while fast pyrolysis process takes place at a temperature above 700°C. Various studies on production of biofuels from plant residues have shown that the temperature, heating rate and the resident time of pyrolysis process are the main factors that affect the final product quality. At present time, in Iran, there are more than 360 thousands hectares of pistachio growing fields which annually produce over 215 thousands metric tons residues which are mainly leaves and stems. The main objective of this study was to measure the heating properties of the powders prepared from the leaves and the stem of pistachio trees. These properties include higher heating value (HHV), lower heating value (LHV) and thermal gravimetric analysis (TGA) of the powders. Then the powders were separately pyrolysed and the kinetic of the pyrolysis process for producing charcoal from them was investigated.
Materials and Methods
In this research, leaves and stems of pistachio trees were initially analyzed to determine their chemical constituents including moisture content, volatile compounds, carbon (C), hydrogen (H), nitrogen (N), sulfur (S) and oxygen (O) content. Using these constituents the height heating value and low heating value for the leaves and the stems were determined. The thermal gravimetric analysis (TGA) of the powders was made to select a proper heating temperature for pyrolysis of the powders. In each experiment about 10 g of powder powders were pyrolyzed to produce char. Based on TGA results, the pyrolysis experiments were performed at 350, 400, 450 and 500°C with 30 minutes residence time. The instantaneous amount (in decimal) of the produced gas (M) and char (Ms) as a function of time (t) was modeled using the following equations:


For each experiment B is a constant value and is represented by:

Where Ea is the activation energy, R is universal gas constant, T is the temperature of the experiment and A is the pre-exponential constant. By having M or Ms at different times (t), the parameters of A, B and Ea were estimated using the curve fitting tool box of the MATLAB® software.
Results and Discussion
The results of chemical analysis indicated that the leaves powders contained 1.5% N, 42.1% C, 5.5% H, 0.4% S and 48.3% O while the stem samples contained 0.5% N, 46.5% C, 6.1% H, 0.2% S and 44.6% O. Higher amount of carbon and hydrogen in the stem leaves indicates that the stem should have higher energy content. In fact, the calculated high and low heating values for leaves were 17.23 and 16.03 MJ.kg-1, and for the stems were 18.91 and 17.59 MJ.kg-1, respectively which comply with the predicted results from chemical analysis of the powders. The TGA test results indicated that the initial weight loss took place up to 270°C for the stems powder and up to 220°C for leaves powders. This weight loss was due to loss of moisture and volatile compounds. The actual degradation temperature for the stem powders ranged from 300 to 500°C while for the leaves was from 350 to 600°C. The results of pyrolysis experiments indicated that the pyrolysis of stems took place faster than leaves. The pyrolysis time was 10 to 15 min for leaves and 5 to 10 min for stems. The resulting char for pyrolysis of stem was 30% and for stems were 40% of the original materials. The kinetic of pyrolysis was modeled using one-step global model for production of char and gas. The experimental data were fitted to the used model with high degrees of accuracy (R2>0.99). The model parameters, namely activation energy and frequency factors were 10.70 kJ.mol-1, and 0.047 s-1 for stems and 21.72 kJ.mol-1 and 0.312 s-1, respectively.
Conclusions
In general, both HHV and LHV of the stems were higher than those of leaves due to higher carbon content of the stems. The TG curves indicated the pyrolysis time of stems was shorter than that of leaves. The leaves yielded 40% char while the stem yielded 30%.

Abstract Introduction
Biodiesel is a promising renewable substitute source of fuel produced from tree born oils, vegetable based oils, fats of animals and even waste cooking oil, has been identified as one of the key solutions for the alarming global twin problems of fossil fuel depletion and environmental degradation. One of the sources for biodiesel production is mastic which is often grown in mountains. Its kernel contains 55% oil which makes it as a valuable renewable resource for biodiesel production. The objective of this research was to study of the feasibility of biodiesel production from Atlas mastic oil using ultrasonic system and optimization of the process using Response surface methodology.
Materials and Methods
In order to supply the required oil for the biodiesel production process, the oil should be prepared before the reaction. Hence, the purified oil was methylated using Metcalf et al (1996) method, and the prepared sample was injected into Gas Chromatography device to determine fatty acids profile and molecular weight of the used oil. An ultrasonic processor (Hielscher Model UP400S, USA.) was used to perform the transesterification reaction.
All the experiments were replicated three times to determine the variability of the results and to assess the experimental errors. The reported values are the average of the individual runs. The different operating parameters used in the present work, to optimize the extent of conversion of Atlas pistache oil, include methanol to oil molar ratio (4:1, 5:1 ,6:1), amplitude (24.1, 62.5 100%), pulse (24.1, 62.5 100%), reaction time (3, 6, 9 min).
Results and Discussion
Results of analyses showed that the independent variables, namely molar ratio, vibration amplitude, pulse and reaction time had significant effects on the amount of produced methyl ester.
By increasing the amplitude and pulse, the methyl ester content increased. Increase in amplitude and pulse cause to increase the mixing effect and physical interface. Increasing the ratio of ultrasonic working time to its idling time caused to an increase in the conversion percent. Because the treating time of the samples by ultrasound in limit time durations is increased, while this increase becomes lower at higher ratios. This is due to the fact that the initial vibrative shock acted on the samples after ultrasonic restarting, finds an identical effect with uniform wave. However, the idling phase of ultrasound caused a decrease in the amount of consumed energy. Similar results have been reported by Chand et al. (2010) for the effect of pulse on conversion percent of methyl ester. Trend of reaction time and molar ratio were different with trend of amplitude and molar ratio on methyl ester content so that they were divided to two sections. It should be mentioned that the increase in biodiesel yield because of molar ratio has some limitations. If the ratio is increased more than a certain extent, biodiesel conversion percent will decrease. The main reason for this result can be related to the amount of methanol increase in the mixture, which leads to more dissolution of glycerin and alcohol in biodiesel which considerably influences its purity.
Optimization was carried out based on Response Surface Methodology (RSM) using Design Experts software. The obtained results from optimization were as follow: 5.45 molar ratio, 0.89 amplitude, 0.71 pulse and 5.99 minutes of time. The conversion percentage obtained as 94.96. It is worthy to note that the experiment was iterated at suggested point by the optimization software and the conversion percent was 94.02. As well as 34792.37 J at the obtained point to be acceptable (1%) difference from the model.
Conclusions
The increase in the ultrasound amplitude resulted in an increase in the conversion percentage which tends to ascend. Also, the increase of reaction time by 5 to 7 minutes increased the conversion percentage, following which is the descend trend. The obtained results from optimization were as follow: 5.45 molar ratio, 0.89 amplitude, 0.71 pulse and 5.99 minutes of time. The conversion percentage and consumed energy obtained as 94.96 and 32421.5 J, respectively. It is worthy to note that the experiment was iterated at suggested point by the optimization software and the conversion percent was 94.02.

Abstract Introduction
Drying is one of the most common methods for storing food and agricultural products. During drying process, free water that causes the growth of microorganisms and spoilage of products is removed from the product. There are several methods for drying of agricultural products. one of the most important methods of investment is drying by using sunlight. Iran is situated at 25- 43oE longitude and mean solar radiation is about 4.9 kwh.m-2.d-1. Because of the proper solar radiations in 95% of the agricultural areas in Iran, solar drying is widely used for drying of fruits and vegetables. The use of solar dryer causes saving in energy consumption and processing costs for drying of products in farms and gardens. Several researchers investigated heat transfer and heat flow in dryers. Selection of appropriate method was carried out for drying of agricultural products using heat pump. Experiments were done and mathematical relationships were estimated to obtain correlation parameters between Reynolds number and Nusselt number for the three cases of solar dryer (cabinet, indirect and combination).The best working conditions were determined for three types of solar collectors (flat, finned and corrugated). In this study, the process of heat transfer and heat transfer coefficient of a solar dryer with and without rotation of absorber plate was compared.
Materials and Methods
The experiments were conducted in Azarshahr, East Azarbayjan province, Iran in September 2014. Newton's law of thermodynamic was used to analyze the working condition of solar absorber. For this purpose the absorber plate was divided into four equal parts. According to the thermal equations and related boundary conditions as well as the relationship between heat transfer coefficient and the temperature gradient, equation 1 for the Nusselet number obtained:
1
Beside the relationship between Nusselt number and heat transfer coefficient is defined as equation 2:
2
Finally variation of total heat flow over the time at different surfaces of the collector is determined by using equation 3:
3
Two cases (solar panel with rotation and without rotation) were considered for testing. Data measuring was carried out for 9 hours from 8 to 17. The fluid flow rate was 0.0185m3.s-1. The dryer was installed in an environment with air temperature of 31.6 oC and 31.8 oC, with the air velocity of 0.58 m.s-1 and 0.54 m.s-1 and with the relative air humidity of about 21%and 21.5% at the first and second days, respectively. The dryer had an automatic temperature controller to fix the air temperature with an accuracy of ±0.1 oC. An anemometer Yk-2005AM model was used to regulate the required air velocity. The output data of the thermocouples was recorded by a digital thermometer (DL-9601A, Lutron) that was connected to a computer using RS232 cable and recorded the temperature at required point every an hour. The relative humidity of the ambient was measured every hour with a digital hygrometer (HT.3600, Taiwan), accuracy of 3%. By assembling controlling system with a DC motor, a precious photocell and a proper mechanism, the frame would rotate by the sun and followed solar radiation, therefore more solar energy produced in solar panel.
Results and Discussion
The results of the experiments showed that the heat transfer process increased in both cases from the early morning and reached to its maximum value around 12 to 14 o’clock. The trend was more homogeneous in the dryer by absorber plate without rotation due to the decline of the heat accumulation. The mean temperature rise in the solar dryer without rotation was 37oC and in the solar dryer with rotation was 54oC. Because of the rotation of solar plate, variations of solar radiation were low. Therefore, by rotation of the solar dryer panel the temperature rise was 27oC. The values of heat transfer coefficient in the solar dryer with rotation were decreased by the time. This reduction in the hours before noon is more than after noon. This is due to the reduction of the temperature gradient in the solar absorber plate. Also the results showed that heat transfer coefficient in the lower levels (S1 and S2) is more than higher levels (S3 and S4). Variations of the heat flow for the solar dryer with rotation is more than the other. Because in the first one, the absorber plate was followed the solar radiation and generated heat in the plate increases and the fan does not have the ability to discharge the generated heat. The total amount of heat transfer in absorber plate with rotation was 36.1% higher than the absorber plate without rotation. To increase the heat transfer from the dryer, design of the system to change air flow rate by increasing temperature, can increase the efficiency of the dryer.
Conclusions
In this study the performance of the absorber plate in a solar dryer in two cases with rotation and without rotation were compared. The results showed that by rotation of the solar absorber plate the output temperature of the collector rises about 27oC. Thermal fluctuation in the rotation solar plate is lower than the solar plate without rotation.

Abstract Introduction
Seventy million tons of agricultural crops are produced from 18 million hectares of agricultural lands in Iran every year. Since 80% of the crops (wt. basis) ends up as residues, therefore, about 50 million tons of crop residues are generated annually the majority of which is burnt on field leading to vast emissions of greenhouse gases (GHG) due to the incomplete combustion process. These residues could potentially be transformed into heat energy directly by adopting a burning process or indirectly by first transforming them into secondary fuel as hydrogen, bio-methane, methanol or ethanol.
Materials and Methods
The present study was conducted using, wheat and rapeseed straws dried at ambient temperature co-digested with fresh cow dung while the total solid content and detention time were kept constant. To conduct the Anaerobic Digestion (AD) experiments, cylinder reactors (13 L) were constructed and placed in a water bath equipped with a heater and sensor to maintain the temperature at 35±2 oC. The biogas produced in the digester was investigated by measuring the displacement of the water in a measuring tube connected to the reactor. Gas samples were obtained from the sampling port and were analyzed gas chromatograph. The temperature for detector, injector and oven were 170, 110 and 50 oC respectively. Before the test, the first CH4 and CO2 net gases, peaks corresponding percentage was determined with respect to the retention time of the area. Then sample was compared with standard gas and samples gas percentage was determined. The residues were mechanically pretreated using a mill in order to increase the availability of the biomass to enzymes. After the pre-treatment, the material (<2 mm) was mixed with a different proportion of fresh cow dung, Initial Total Solids (TS) content in the reactor was adjusted at 9%. Factors such as PH, Volatile Solids (VS) were determined by the standard method.
Results and Discussion
A decrease in the process pH was observed in the first few days of the digestion and this is due to high volatile fatty acid (VFA) formation. These results were compatible with sanaee moghadam et al. (2013). The results obtained showed that, the highest rate of VS reduction belonged to rapeseed residues at 52.22%.The lowest rate of VS reduction attributed to wheat residues at 36.79%. The rapeseed residues with 311.45 Lit.kg-1 VS had the highest accumulated methane followed by wheat straw with 167.69.28 L.Kg-1 VS in probability level of 5%. The average percentages of methane production for rapeseed straw and wheat straw during the 140 days experiment under mesophilic condition were 66% and 55%, respectively. Production of methane had delay and started after 46th day. Much reason may be possible. Inoculums used in this study were only fresh cattle dung. The mixture of fresh cattle dung and effluent of anaerobic digester or fresh rumen fluid may be decrease retention time and increase biogas production. According results of Budiyono the rumen fluid inoculated to biodigester significantly affected the biogas production. Rumen fluid inoculums caused biogas production rate and efficiency increase more than two times in compare to manure substrate without rumen fluid inoculums (Budyono et al., 2010). The other reason was pretreatment. This study applied just mechanical pretreatment. According to Cecilia studies, different pretreatment combined with mechanical pretreatment decrease retention time and increase biogas production efficiency (Cecilia et al, 2013). However, Zhang et al. claimed that it is hard to say which method is the best because each has its own strong point and weak point. Yet, until now, none of the pretreatment technologies has found a real breakthrough.
Conclusions
According to this study, rapeseed residues had the highest level of methane production in comparison with wheat residues. The rapeseed residues combine with cattle dung had suitable potential to methane production. The 140 days, Biomaethane Potential (BMP) of rapeseed residues combine with cattle manure had 311. 45 Lit/kg vs. add. Moreover, it had high percentage of VS content reduction (52.22%). The high retention time was observed (140 day). One reason was lack of suitable inoculums and pretreatment. Furthermore, the lingo-cellulose nature of the crop residues, lower will be the biodegrade ability. Furthermore, the anaerobic co-digestion of rapeseed straw with cattle manure is feasible for production of methane.

Abstract Introduction
While the world's population is growing, agricultural production is still based on the use of limited and non-renewable resources. In addition to the scarcity of resources, their continuous using over the long term, causes the widespread pollution, loss of soil fertility and low agricultural production capacity, eventually. The main causes of the increase in energy consumption include the increase of world population, limited arable land, low price of fuel and fertilizer and noted increased levels of human life. Attention to limited resources and adverse effects resulting from the appropriate use of different energy sources on human health and the environment, has been required examining the energy consumption patterns and the flows of energy in the agricultural sector. Checking the share of input and output energy in different agricultural ecosystems, with attention to the type of product and the type of materials used in production, can help to identifying defects and play a fundamental role in the sustainable production, optimization of economic system, maintaining reserves of fossil fuels and mitigate air pollution. With this approach, in this present study energy consumption and energy indices for tomato production in Khorasan Razavi were studied.
Materials and Methods
The energy efficiency of units was analyzed using the stochastic frontier technique (SFA). Energy inputs from two perspectives have also been divided. In the first view of energy inputs, including inputs that have a direct energy (DE) and indirect energy (IDE). The second approach as well includes inputs that have renewable energy (RE) and non renewable energy (NRE).The data for this study was collected through interviews and completing 156 questionnaires using two-stage random sampling from tomato producer of Khorasan Razavi province in 2012.
Results and Discussion
The results showed that the energy consumption for tomato production in Khorasan razavi province of Iran were 43.2 GJha-1. Water for Irrigation was attributed the greatest share of energy inputs (30%). The average amount of diesel fuel consumption was 152 lha-1, Human resources and machinery were 987 hha-1 and 44.6 hha-1 respectively. The average amount of water needed for irrigation was 12,596 m3ha-1. Average energy output of the system was determined to be 35.3 GJha-1. The share of different forms of energy inputs such as direct energy was 53.9 %, indirect energy was46.1renewable energy was 50.5, and renewable energy was49.5%. According to the results, the share of indirect energy was higher than direct energy and the share of renewable energy was higher than renewable energy. Also the result of the study revealed that energy productivity and efficiency in the investigated units were 0.68 and 0.82MJha-1, respectively. The results show that the Cobb-Douglas function to calculate the efficiency has more consistency and adaptation with the data. In other words, Cobb-Douglas function is superior to the translog function. Average of technical efficiency was calculated 57%.
Conclusions
The results indicated that although a significant percentage of the investigated farms are inefficient, farmers with higher acreage have favorable energy consumption and technical efficiency of these farms was higher than that the other ones. Considering the obtained results, the main drawback associated with the technical efficiency of energy use and production of tomato in Khorasan Razavi is inappropriate use of inputs due to mismanagement, lack of information and also the small size of the farms. Based on the results the better management in the use of inputs and the enlargement of the size of agricultural land can improve energy efficiency in the region. Also, for improving the measures of energy flows in growing tomatoes, determining the appropriate amount of fertilizer (particularly phosphates) to grow tomatoes, conducting classes and printing the brochures for farmers to implement correct procedures in the use of inputs and the use of machines, correction of the system to reduce water consumption and cultivation of new varieties of tomato seeds in the region are recommended.

Abstract Introduction: Depletion of fossil fuels and environmental degradation are two major problems faced by the world. Today fossil fuels take up to 80% of the primary energy consumed in the world, of which 58% is consumed by the transport sector alone (Mard et al., 2012). The combustion products cause global warming, which is caused of emissions like carbon monoxide (CO), sulfur dioxide (SO2) and nitrogen oxides (NOX). Thus it is essential that low emission alternative fuels to be developed for useing in diesel engines. Many researchers have concluded that biodiesel holds promise as an alternative fuel for diesel engines. Biodiesel is oxygenated, biodegradable, non-toxic, and environmentally friendly (Qi et al., 2010). Materials and Methods: In this study transesterification method was used to produce biodiesel, because of its simplicity in biodiesel production process and holding the highest conversion efficiency. Transesterification of poultry fat oil and the properties of the fuels: Fatty acid methyl ester of poultry fat oil was prepared by transesterification of oil with methanol in the presence of KOH as catalyst. The fuel properties of poultry fat oil methyl ester and diesel fuel were determined. These properties are presented in Table 1. Tests of engine performance and emissions: After securing the qualitative characteristics of produced biodiesel, different biodiesel fuels of 5%, 10%, 15%, and 20% blended with diesel fuel were prepared. A schematic diagram of the engine setup is shown in Fig.1. The MF-399 tractor engine was used in the tests. The basic specifications of the engine are shown in Table 3. The engine was loaded with an electromagnetic dynamometer. The Σ5 model dynamometer manufactured by NJ-FROMENT was used to measure the power and the torque of the tractor engine. The speed range and capacity of this device are shown in Table 2. A FTO Flow Meter, manufactured by American FLOWTECH Company, was used to measure the fuel consumption (Fig.3). Its measuring range is 37-1537 ml min-1. Results and Discussion: The engine performance was evaluated in terms of engine power, engine torque and specific fuel consumption at different engine speeds. The variation of engine torques with B5, B10, B15, B20 and diesel fuel are presented in Fig. 4. The engine torque for biodiesel blends was more than that by diesel fuel only. The mean engine torques for B5, B10, B15 and B20 were 2.5%, 2.8%, 3%, and 3.5% higher than that by only diesel, respectively. This is due to the better combustion of biodiesel compared to diesel fuel. The variation of engine powers with B5, B10, B15, B20 and diesel fuel are presented in Fig. 5. The engine powers for biodiesel blends were more than that by diesel fuel. The mean engine powers for B5, B10, B15 and B20 were higher than that by diesel by 2.5%, 3%, 3.5%, and 4%, respectively. This is because of good combustion of biodiesel resulted from higher oxygen content. The mean specific fuel consumptions for B5, B10, B15 and B20 were higher than diesel fuel about 4.1%, 7%, 8.8%, and 2%, respectively (Fig. 8). The density of biodiesel was higher than that of diesel fuel, which means the same fuel consumption on volume basis results in higher specific fuel consumption in case of biodiesel. Conclusions: The values of viscosity, density and flash point of poultry fat oil biodiesel were found to be closely matched with ASTM D-6751 standard specifications. Viscosity and density of biodiesel were found more than those for diesel. The calorific value of biodiesel was found to be lower than that of diesel. Poultry fat oil biodiesel cannot be used as a neat diesel fuel in cold weather conditions due to its relatively low cloud point. Preheating and lowering freezing point is required to eliminate this problem. The engine performance with poultry fat oil biodiesel and its blends are comparable with those of pure diesel fuel. Results indicated that B20 blend had the best performance and the lowest specific fuel consumption.

Abstract Introduction: Tractors are considered as the main power generators in mechanized agriculture. Hence, the experts and engineers in tractor manufacturing of the country, are required to focus on developing and designing new features in tractor manufacturing. This must be, of course, paralleled with the economic aspects. Achieving this goal, Iran Tractor Manufactories Co., (ITMCO) has designed and developed tractors equipped with turbochargers. This has been performed on ITM800 & ITM485 models, according to world standards. The turbocharger system, with harnessing of lost energy in engine output fumes, compresses the air entering the engine and more air enters the cylinder. This will cause the engine to burn fuel more efficiently and thus produce more power.
Materials and Methods: This study has been carried out on ITM485 & ITM800 tractors (with turbocharger system) and ITM285 & ITM475 tractors (without turbocharger system) to assure the improvement of engine performance and compare them employing OECD world standards. Experiments were performed in the concrete runway of Tabriz Tractor Manufacturing Company. For experiments, a dynamometer was used to measure the traction force between two tractors, a measuring unit for fuel, a thermometer unit and a timer to measure the quantities of fuel consumption, drawbar force and power. For drawbar traction test, each of the tested tractors pulled the rear tractor in different gears and the dynamometer between these 2 tractors recorded the tractors traction force by data loggers. To measure tractors fuel consumption, a measuring unit of fuel (VDO - EDM 1404) was used that calculated the flow rate in the path of fuel from the fuel tank to the engine and the return path from the engine to the fuel tank and showed the quantity of fuel consumption in liters per hour digitally.
Results and Discussion: In comparison of traction power and force of tractors with turbochargers and without turbochargers in different gears, the results of variance analysis showed that the effect of tractor was significant. Traction power and force at tractors with turbochargers ITM485 and ITM800 and without turbocharger ITM475 have a significant difference in the level of one percent. Tukey post hoc test results also indicate that traction power and force in tractors with turbochargers ITM485 and ITM800 are significantly more than the tractor without turbocharger ITM475. The gear effect is also significant. Traction power and force in different gears have significant difference at the probability of one percent. Tukey post hoc test results indicate that power quantity is highest in the gears: (1+H, 2*H, 1*H, 3+L) and minimum in the gears: (1*L, 1+L, 2*L), (* Turtle and + Rabbit). But Tukey post hoc test results indicate that traction force quantity is highest in the gears: (1*L, 2*L, 1+L) and minimum in the gears: (2*H, 1+H). In the comparison of specific fuel consumption of tractors with turbochargers and without turbochargers in different gears, the results of variance analysis showed that the effect of tractor was significant. The amount of specific fuel consumption at tractors with turbochargers ITM485 and ITM800 and without turbocharger ITM475 has a significant difference in the level of one percent. Tukey post hoc test results also indicate that specific fuel consumption quantity in tractors with turbochargers ITM485 and ITM800 in the level of one percent is significantly less than the tractor without turbocharger ITM475. The gear effect is also significant. The specific fuel consumption quantity in different gears has significant difference at the probability of one percent. Tukey post hoc test results indicate that specific fuel consumption quantity is highest in the gears: (1*L, 1+L, 2*L) and minimum in the gears: (1+H, 2*H, 1*H).
Conclusions: The tests were performed on tractor drawbar traction. Results of variance analysis in this experiment on a concrete surface, indicated that the calculated traction power and force of ITM485 and ITM800 tractors (with turbocharger system) were higher than the ITM475 & ITM285 tractors (without turbocharger) and this difference was significant at the one percent level of probability. Meanwhile specific fuel consumption in the ITM485 and ITM800 tractors (with turbocharger system) was lower than that of the ITM475 & ITM285 tractors (without turbocharger) and this difference was significant at the one percent level of probability. This will lead to significant savings in fuel consumption.