Bioenergy
J. Rezaeifar; A. Rohani; M. A. Ebrahimi-Nik
Abstract
In the quest for enhanced anaerobic digestion (AD) performance and stability, iron-based additives as micro-nutrients and drinking water treatment sludge (DWTS) emerge as key players. This study investigates the kinetics of methane production during AD of dairy manure, incorporating varying concentrations ...
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In the quest for enhanced anaerobic digestion (AD) performance and stability, iron-based additives as micro-nutrients and drinking water treatment sludge (DWTS) emerge as key players. This study investigates the kinetics of methane production during AD of dairy manure, incorporating varying concentrations of Fe and Fe3O4 (10, 20, and 30 mg L-1) and DWTS (6, 12, and 18 mg L-1). Leveraging an extensive library of non-linear regression (NLR) models, 26 candidates were scrutinized and eight emerged as robust predictors for the entire methane production process. The Michaelis-Menten model stood out as the superior choice, unraveling the kinetics of dairy manure AD with the specified additives. Fascinatingly, the findings revealed that different levels of DWTS showcased the highest methane production, while Fe3O420 and Fe3O430 recorded the lowest levels. Notably, DWTS6 demonstrated approximately 34% and 42% higher methane production compared to Fe20 and Fe3O430, respectively, establishing it as the most effective treatment. Additionally, DWTS12 exhibited the highest rate of methane production, reaching an impressive 147.6 cc on the 6th day. Emphasizing the practical implications, this research underscores the applicability of the proposed model for analyzing other parameters and optimizing AD performance. By delving into the potential of iron-based additives and DWTS, this study opens doors to revolutionizing methane production from dairy manure and advancing sustainable waste management practices.
Image Processing
S. Abdanan Mehdizadeh
Abstract
IntroductionAdopting new technologies for crop growth has the characteristics of improving disaster resistance and stress tolerance, ensuring stable yields, and improving product quality. Currently, the cultivation of seed trays relies on huge labor power, and further mechanization is needed to increase ...
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IntroductionAdopting new technologies for crop growth has the characteristics of improving disaster resistance and stress tolerance, ensuring stable yields, and improving product quality. Currently, the cultivation of seed trays relies on huge labor power, and further mechanization is needed to increase production. However, there are some problems in this operation, such as the difficulty of improving the speed of a single machine, seedling deficiency detection, automatic planting, and controlling the quality, which need to be solved urgently. To solve these problems, there are already some meaningful attempts. Si et al. (2012) applied a photoelectric sensor to a vegetable transplanter, which can measure the distance between seedlings and the movement speed of seedlings in a seedling guide tube, to prevent omission transplantation. Yang et al. (2018) designed a seedling separation device with reciprocating movement of the seedling cup for rice transplanting. Tests show that the structure of the mechanical parts of the seedling separation device meets the requirements of seed movement. The optimization of the control system can improve the positioning accuracy according to requirements and achieve the purpose of automatic seedling division. Chen et al. (2020) designed and tested of soft-pot-tray automatic embedding system for a light-economical pot seedling nursery machine. The experimental results showed that the embedded-hard-tray automatic lowering mechanism was reliable and stable as the tray placement success rate was greater than 99%. The successful tray embedding rate was 100% and the seed exposure rate was less than 1% with a linear velocity of the conveyor belt of 0.92 m s-1. The experiment findings agreed well with the analytical results.Despite the sharp decline in Iran's water resources and growing population, the need to produce food and agricultural products is greater than ever. In the past, most seeds were planted directly into the soil, and many water resources, especially groundwater, were used for direct seed sowing and plant germination. One way to reduce the consumption of water, fertilizers, and pesticides is to plant seedlings instead of direct seed sowing. Therefore, the purpose of this study was dynamic modeling and fabrication of seed planting systems in seedling trays.Material and MethodsIn this experiment, Flores sugar beet seeds (Maribo company, Denmark) were used. The seedling trays had dimensions of 29.5*60 cm with openings and holes of 5.5 and 4 cm, respectively. To plant seeds in seedling trays, first, a planter arm was modeled and its position was obtained at any time. Then, based on dynamic modeling, the arm was constructed and a capacitive proximity sensor (CR30-15AC, China) and IR infrared proximity sensor (E18-D80NK, China) were used to find the location of seedling trays on the input conveyor and position of discharging arm, respectively. To achieve a stable and effective control system, a micro-controller-based circuit was developed to signal the planting system. The seed planting operation was performed in the seedling tray according to the coordinates which were provided through the image processing method. The planting system was evaluated at two levels of forward speed (5 and 10 cm s-1). Moreover, a smartphone program was implemented to monitor the operation of the planting system.Results and DiscussionThe planting system was assessed for sugar beet seeds using two levels of forward speed (5 and 10 cm s-1). The nominal capacity of this planter ranged from 3579 to 4613 cells per hour, with a miss and multiple implantation indices of 0.03% and 8.17%, respectively, in 3000 cells. Due to its planting accuracy, speed, and low energy consumption (25.56 watt-hours), this system has the potential to replace manual seeding in seedling trays.ConclusionIn the present study, a seed-sowing system for planting seedling trays was designed, constructed, and evaluated based on dynamic modeling. In the developed system, unlike previous research, planting location detection was conducted through image processing. Additionally, a smartphone program was established to monitor the operation of the planting system without interfering with its performance. This study demonstrates that image processing can successfully detect planting locations and can effectively improve efficiency over time for major producers.
Design and Construction
A. Mohammadi; K. Kheiralipour; B. Ghamari; A. Jahanbakhshi; R. Shahidi
Abstract
IntroductionThe permissible exposure time to vibration for the operator is one of the key factors in maintaining the operator's health while optimizing machinery and equipment. The tractor studied was the ITM475, manufactured in Iran. The purpose of this study was to calculate the operator's permissible ...
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IntroductionThe permissible exposure time to vibration for the operator is one of the key factors in maintaining the operator's health while optimizing machinery and equipment. The tractor studied was the ITM475, manufactured in Iran. The purpose of this study was to calculate the operator's permissible vibration exposure time while using the tractor to ensure the driver can maintain good bodily health.Materials and MethodsIn this study, experiments were conducted using a 3-axis vibration meter based on the ISO 2631 standard. The obtained data were analyzed through a factorial experiment using 18 treatments and 3 replications. The factors studied were engine rotation speed (at three levels of 1000, 1500, and 2000 rpm), road type (dirt and asphalt), and gear position (at three levels of 1, 2, and 3).Results and DiscussionVarious total vibration models were obtained for the tractor, and their determination coefficient varied from 90.11% for gear No. 3 on an asphalt road to 100% for gear No. 1 on an asphalt road and gear No. 2 on a dirt road. The maximum whole-body vibration, and consequently the minimum permissible exposure time, was observed for gear No. 3 at an engine rotation speed of 2000 rpm on a dirt road, which was 1.49 and 1.16 hours, respectively.ConclusionThe maximum whole-body vibration experienced during an 8-hour tractor-driving session was measured at 0.85 m s-2. It is important to note that the permissible exposure time decreases as vibration levels increase, and it reaches a limit of 1.16 hours. To ensure drivers adhere to these permissible exposure times across various driving conditions, measures must be implemented to reduce tractor vibration and minimize its transmission to the driver. By reducing overall tractor vibration and minimizing its impact on the driver, it becomes possible to increase the permissible exposure time for drivers.
Agricultural systems engineering (greenhouse, fish farming, mushroom production)
M. Ghonimy; M. Morcos; A. Badr
Abstract
In this study a mathematical analysis for estimating the performance rate "RP" of wheel type trenching machine was studied. The mathematical analysis quantifies the analysis and resulted in an equation. This mathematical equation was checked under different operating conditions. The practical study of ...
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In this study a mathematical analysis for estimating the performance rate "RP" of wheel type trenching machine was studied. The mathematical analysis quantifies the analysis and resulted in an equation. This mathematical equation was checked under different operating conditions. The practical study of the performance rate showed that the deviation of the theoretical performance rate from the actual performance rate ranged from 5 to 7% for the 60.4 and 90.5 cm trench depth, respectively. The machine field efficiency also ranged between 43 and 50.1% for 90.5 cm and 60.4 cm trench depth respectively.
Modeling
A. Niazi; H. Golpira; H. Samimi Akhijahani
Abstract
IntroductionOne of the biggest problems in growing legumes like peas is harvesting these types of crops. During the machine harvesting process the harvest loss is very high. Therefore, in most parts of Iran chickpea harvested by hand and this is very tedious. Based on the literature review there are ...
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IntroductionOne of the biggest problems in growing legumes like peas is harvesting these types of crops. During the machine harvesting process the harvest loss is very high. Therefore, in most parts of Iran chickpea harvested by hand and this is very tedious. Based on the literature review there are different types of harvesting machines which designed, constructed and optimized by Miller et al., 1990; Golpira, 2015; Shahbazi, 2011; Jalali and Abdi, 2014; Mahamodi, 2016. But using different varieties of chickpea in mountainous areas has limited the use of harvesting mechanisms. The purpose of this study is mechanization of the harvesting process of chickpea with low losses and suitable performance. Moreover the optimization process of lowering the weight of the header was carried out by modeling of software.Materials and MethodsTo reduce the amount of chickpea losses from the reel, a perforated plate with defined holes was installed in the header, where the separated chickpea pods fell behind the plate without returning to the farm. By using the plate in the header of the chickpea harvesting machine and by changing the harvesting height at the three levels of 10, 15 and 20 cm and the distance of the cutter at three levels of 3, 5 and 7 mm, the performance of the machine was evaluated. The experiments were carried out with Caboli variety cultivated in Kurdistan province, which is proper for mountainous areas without regular watering condition in three replications. The plants were placed in a fiber, wooden plate considering farm conditions. In addition, the header was modeled statically and dynamically under the influence of the external forces applied to the header using Ansys and Abaqus software. Based on the actual data, the validity of the applied model was determined and according to the verification results the optimization of the header was performed considering minimal weight (to reduce energy consumption).Results and DiscussionThe evaluation results of the performance of header showed that the effects of using perforated plate and the height of the header for harvesting on the chickpea harvesting and losses are significant at the level of 1% and 5%, respectively, and the interaction between perforated plate and the header height on the chickpea loss is significant at 5%. Using a perforated plate in the harvesting machine increases the amounts of chickpea collected from the farm increases. In this condition the chickpea pods separated from the plant and passed through the plate. With the separation of the stems, due to the proper wear that exists between the plate and the reel, the pods are properly separated and pass through the perforated plate. Moreover, the chickpea loss is higher for the system without perforated plate. The effect of the distance between the reel and header plate is affects the remaining chickpea on the plate. By increasing the distance from 5 mm to 7 mm the amount of harvested had a considerable effect. The best method of harvesting chickpeas is at the kinematic index of 1.5 with perforated plate, the harvesting height of 15 cm and the distance of 5 mm. According to modeling processes of the reel and the results of the static analysis, the minimum and maximum stress values were recorded about 3.31 MPa and 6.50 MPa (based on the von misses criteria), respectively, which is very small compared to the yield stress of the reel constructed with St-37. Also, the results of the dynamic analysis of the reel showed that the maximum von misses stress occurred with increasing the kinematic index. The maximum stress for kinematic index of 1, 1.5 and 2 was observed about 32.2, 40.1 and 52.72 MPa, respectively. The results of 3D model validation showed that the applied model with Abaqus software (R2>0.9264) was able to predict the amount of stress in different parts of the reel.ConclusionIn this study, the changes were made on the chickpea harvesting machine to get the proper performance and increasing machine efficiency. A perforated plate was used to prevent pea’s losses. The best condition for the harvesting process is obtained with the harvesting height of 15 cm and the distance of 5 mm. By using 3D modeling of the reel weight was reduced about 10%.
Precision Farming
F. Nadernejad; D. M. Imani; M. R. Rasouli
Abstract
IntroductionSugarcane is a strategic agricultural product and increasing productivity and self-sufficiency in its production is of special importance. The most important product of sugarcane is sugar. Various factors like climatic and management conditions affect the yield of sugarcane and recoverable ...
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IntroductionSugarcane is a strategic agricultural product and increasing productivity and self-sufficiency in its production is of special importance. The most important product of sugarcane is sugar. Various factors like climatic and management conditions affect the yield of sugarcane and recoverable sugar. Crop yield forecasting is one of the most important topics in precision agriculture, which is used to estimate yield, match product supply with demand and manage product to increase productivity. The purpose of this study is to predict and model the factors affecting sugar extracted from sugarcane (recoverable sugar) in the farms of Amir-Kabir sugarcane agro-industry Company of Khuzestan province using machine learning methods.Materials and MethodsTo conduct this study, data from the agro-industrial company Amir-Kabir in the province of Khuzestan from 2010 to 2017 were used. This data has 3223 records which include four sets of data: climate, soil, crop and farm management. This data includes continuous and discrete variables. Discrete variables include production management, soil type, farm, variety, age (cane class), the month of harvest and times irrigation. Continuous variables include area, chemical fertilizer consumption, water consumption per hectare, total water consumption, drain, crop season duration, yield (cane yield) soil EC, purity, time interval drying off to crop harvest, precipitation, min and max temperature, min and max relative humidity, wind speed and evaporation. The recoverable sugar variable is considered as the target variable and is divided into two classes, values greater than or equal to 9 are in the optimal class and less than 9 are in the undesirable class. The other variables are considered as predictor variables. For modeling using the Holdout method the data were randomly divided into two independent sets, a training set and a test set. 70% of the data which includes 2256 records were used for training and 30% of the data which includes 967 records were used for testing. The modeling of this study was performed with the Python programming language version 3.8.6 in the Jupyter notebook environment. Random Forest, Adaboost, XGBoost and SVM (support vector machine) algorithms were used for modeling.Results and DiscussionTo evaluate the models, metrics of accuracy, precision, recall, f1 score and k-fold cross validation were used. The XGBoost model with 94.8% accuracy on the training set and the Adaboost model with 92.4% accuracy on the test set, are the best models. Based on precision and recall metrics Adaboost model with 87% precision and SVM model with 87% recall have better performance than the other models. Based on Repeated 10-fold stratified cross validation using two repeats the SVM model with 92.3% accuracy is the best model. The variables of purity, time interval drying off to crop harvest and crop season duration are the most important variables in predicting the recoverable sugar.ConclusionIn this study a new approach based on machine learning methods for predicting recoverable sugar from sugarcane was presented. The most important innovation of this study is the simultaneous consideration of management and climatic factors, along with other factors such as soil and crop characteristics for modeling and classification the recoverable sugar percentage from sugarcane. The results show that the performance of all models is acceptable and machine learning methods and ensemble learning algorithms can be used to predict crop yield. The results of this study and the analysis of the rules obtained from the set of decision trees made in the random forest model can be used for managers of different agro-industries in determining appropriate strategies and preparing the conditions to achieve optimal production.For future research as well as policy making and decision making Amir-Kabir sugarcane agro-industry Company the following suggestions are offered: more samples can be used to obtain more reliable results. Also can be used Deep learning methods, time series analysis and image processing. Use of IOT equipment to collect and real-time processing data on Amir-Kabir sugarcane agro-industry farms.
Bioenergy
M. Eshaghi Pireh; M. Gholami Par-Shokohi; D. Mohammad Zamani
Abstract
IntroductionBiodiesel is an eco-friendly renewable alternate fuel and is made from transesterification of vegetable oils and animal fat. The use of biodiesel fuel as a strategy to conserve energy and reduce emissions is becoming increasingly important in engines. Biodiesel fuels increase NOx emissions ...
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IntroductionBiodiesel is an eco-friendly renewable alternate fuel and is made from transesterification of vegetable oils and animal fat. The use of biodiesel fuel as a strategy to conserve energy and reduce emissions is becoming increasingly important in engines. Biodiesel fuels increase NOx emissions in the engines. Compensate for the negative effect, the use of particles additive can be a reliable solution. In this study, the state of heat balance in a single-cylinder, four-stroke diesel engine with different fuel combinations with DXBYGZ formula (X % diesel fuel, Y % biodiesel mass, and Z ppm graphene oxide nanoparticles), has been studied experimentally.Materials and MethodsGraphene nanoparticles in three levels of 30, 60, and 90 ppm were mixed with biodiesel produced from cooking waste oil by transesterification method with volume percentages of 5 and 20% and pure diesel was used. The test engine was a diesel engine, single-cylinder, four-stroke, compression ignition, and water cooling, in the laboratory of renewable energies of agricultural faculty, Moghadas Ardabili University. The engine is connected to a dynamometer and data were obtained after reaching steady state conditions. In thermal balance study, the combustion process merely as a process intended to free up energy fuel, and the first law of thermodynamics is used. The energy contained in the fuel is converted to useful and losses energies by combustion. Useful energy measured by dynamometer as brake power and losses energy including exhaust emission and cooling system losses. Variance analysis of all engine energy balance was done by split-plot design based on a completely randomized design and the means were compared with each other using the Duncan test at 5% probability.Results and DiscussionThe results showed that by adding 60 ppm of graphene oxide and 20% biodiesel to diesel fuel, the useful output power is reduced to a minimum and is reduced by about 5.52%. The results of the model evaluation of useful power, exhaust emissions, and thermal losses in the cooling system showed that the exponential model had a better fit. By adding biodiesel and graphene oxide nanoparticles to diesel fuel, the useful power was reduced. In order to achieve the maximum useful output power and with the priority of adding biodiesel to a high amount, the fuel composition of D80B20G90 had relatively better conditions. By adding 30 ppm of graphene to pure diesel fuel, the equivalent power of exhaust fumes was reduced to a minimum of about 18.5%. In general, heat loss through the cooling system in pure diesel fuel (D100) was lower than other fuel compounds. Pure diesel fuel was recognized as the best fuel mixture due to having the highest useful power, and lowest energy losses in the form of exhaust fumes and through cooling.ConclusionBy adding graphene oxide to pure diesel fuel, the useful output power was reduced to a minimum. With the increase of biodiesel to diesel fuel, the amount of power of the cooling system also increased. By adding graphene oxide to pure diesel fuel, the equivalent power of the exhaust fumes was reduced. Heat loss through the cooling system increased with the increase of nano-graphene and biodiesel.
Modeling
M. Sami; A. Akram; M. Sharifi
Abstract
IntroductionThe need to develop alternative energy sources especially renewable energy has become increasingly apparent with the incident of fuel shortages and escalating energy prices in recent years. With the advent of renewable energy, various studies have been conducted to investigate the potential ...
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IntroductionThe need to develop alternative energy sources especially renewable energy has become increasingly apparent with the incident of fuel shortages and escalating energy prices in recent years. With the advent of renewable energy, various studies have been conducted to investigate the potential of biogas production from agricultural waste. Considering the importance of retention time and methane production potential for designing industrial digesters, many studies on potential analysis and modeling of the digestion process of different products have been carried out by various researchers. These studies are valuable for the design and implementation of anaerobic digesters. Apple is one of the most popular fruits in many parts of the world and is widely cultivated in many temperate regions of the world. Considering the large volume of apple waste in Iran, this study was designed based on potential evaluation and modeling of biogas production from apple pulp.Materials and MethodsIn order to measure the potential of biogas production from apple pomace, a number of lab-scale digesters with a capacity of 600 ml and a working capacity of 400-500 ml were made. pH and C/N ratio were modified by adding NaOH and urea solution, respectively. Three different temperature treatments including psychrophilic (ambient temperature), mesophilic (37ºC), and thermophilic (47ºC) were applied to the substrate. Used pomace samples were collected from the output of an apple juice factory in southern Isfahan province, Iran. Anaerobic Biodegradability (ABD) was obtained by dividing the experimental methane production potential (BMP) obtained from the experimental results on the theoretical methane production potential. Three most common kinetic models of Gompertz, Logistic, and Richards were used to predict and stimulate the cumulative methane production of treatments.Results and DiscussionUnder ambient temperature, the digestive process took a longer time, and the time of maximum dilly biogas production was considerably more than the other two treatments. Statistically, production time and peak time of this treatment was higher than the other two treatments at 1% significance level. Maximum daily biogas production in the ambient treatment was observed on day 37th with a volume of 6.99 g-VS-1 ml, while maximum daily biogas production in the treatments of 37 °C and 47 °C were observed on days 22th (20.16 ml g-VS-1) and 20th (25.57 ml g-VS-1), respectively. In all three treatments, daily biogas production increased sharply in the first incubation days and after that reduced and then production increased again. In mesophilic and thermophilic treatments, the production of biogas modestly stopped after 35 days, but under the ambient temperature, the process of production continued after 55 days. The methane concentration of biogas in the psychrophilic treatment was significantly lower than the other two treatments at 1% level. Two treatments of 37°C and 45°C have a significant difference in methane yield at 1% level. Nevertheless, the production of biogas in two treatments was not statistically different. In all three treatments, the lowest pH was recorded after 7 days of production and the highest pH was recorded on days 34-40. All three kinetic equations were able to simulate the methane production process with high precision, although the results of the Logistic model provided higher accuracy. In the treatment 47 °C, the efficiency of the studied equations was higher than other treatments and models were able to predict the production process with higher accuracy. Results of the experiment show the high biochemical methane production potential of apple pomace (473.17 ml g-VS-1), which under laboratory condition of this study up to 63.9% of this potential (302.70 ml g-VS-1) was obtained. ConclusionThis study results are valuable for the design and implementation of industrial digesters. The results indicate the apple pomace has a high potential for the production of methane and its biodegradability is high. Apart from pH that is acidic, other apple pulp factors are appropriate for the activity of methanogenic bacteria. In terms of nutrients, apple pomace is also a good environment for the growth of anaerobic bacteria.
M. Hamdani; M. Taki; M. Rahnama; A. Rohani; M. Rahmati-Joneidabad
Abstract
IntroductionControlling greenhouse microclimate not only influences the growth of plants, but is also critical in the spread of diseases inside the greenhouse. The microclimate parameters are inside air, roof, crop and soil temperature, relative humidity, light intensity, and carbon dioxide concentration. ...
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IntroductionControlling greenhouse microclimate not only influences the growth of plants, but is also critical in the spread of diseases inside the greenhouse. The microclimate parameters are inside air, roof, crop and soil temperature, relative humidity, light intensity, and carbon dioxide concentration. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models and also artificial neural networks (ANNs) are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Usually thermal simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. So the main objective of this paper is comparison between two types of artificial neural networks (MLP and RBF) for prediction 4 inside variables in an even-span glass greenhouse and help the development of simulation science in estimating the inside variables of intelligent greenhouses.Materials and MethodsIn this research, different sensors were used for collecting the temperature, solar, humidity and wind data. These sensors were used in different positions inside the greenhouse. After collecting the data, two types of ANNs were used with LM and Br training algorithms for prediction the inside variables in an even-span glass greenhouse in Mollasani, Ahvaz. MLP is a feed-forward layered network with one input layer, one output layer, and some hidden layers. Every node computes a weighted sum of its inputs and passes the sum through a soft nonlinearity. The soft nonlinearity or activity function of neurons should be non-decreasing and differentiable. One type of ANN is the radial basis function (RBF) neural network which uses radial basis functions as activation functions. An RBF has a single hidden layer. Each node of the hidden layer has a parameter vector called center. This center is used to compare with the network input vector to produce a radially symmetrical response. Responses of the hidden layer are scaled by the connection weights of the output layer and then combined to produce the network output. There are many types of cross-validation, such as repeated random sub-sampling validation, K-fold cross-validation, K×2 cross-validation, leave-one-out cross-validation and so on. In this study, we pick up K-fold cross- validation for selecting parameters of model. The K-fold cross-validation is a technique of dividing the original sample randomly into K sub-samples. Different performance criteria have been used in literature to assess model’s predictive ability. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2) are selected to evaluate the forecast accuracy of the models in this study.Results and Discussion The results of neural networks optimization models with different networks, dependent on the initial random values of the synaptic weights. So, the results in general will not be the same in two different trials even if the same training data have been used. So in this research K-fold cross validation was used and different data samples were made for train and test of ANN models. The results showed that trainlm for both of MLP and RBF models has the lower error than trainbr. Also MLP and RBF were trained with 40 and 80% of total data and results indicated that RBF has the lowest sensitivity to the size data. Comparison between RBF and MLP model showed that, RBF has the lowest error for prediction all the inside variables in greenhouse (Ta, Tp, Tri, Rha). In this paper, we tried to show the fact that innovative methods are simple and more accurate than physical heat and mass transfer method to predict the environment changes. Furthermore, this method can use to predict other changes in greenhouse such as final yield, evapotranspiration, humidity, cracking on the fruit, CO2 emission and so on. So the future research will focus on the other soft computing models such as ANFIS, GPR, Time Series and … to select the best one for modeling and finally online control of greenhouse in all climate and different environment.ConclusionThis research presents a comparison between two models of Artificial Neural Network (RBF-MLP) to predict 4 inside variables (Ta, Tp, Tri, Rha) in an even-span glass greenhouse. Comparison of the models indicated that RBF has lower error. The range of RMSE and MAPE factors for RBF model to predict all inside variables were between 0.25-0.55 and 0.60-1.10, respectively. Besides the results showed that RBF model can estimate all the inside variables with small size of data for training. Such forecasts can be used by farmers as an appropriate advanced notice for changes in temperatures. Thus, they can apply preventative measures to avoid damage caused by extreme temperatures. More specifically, predicting a greenhouse temperature can not only provide a basis for greenhouse environmental management decisions that can reduce the planting risks, but also could be as a basic research for the feedback-feed-forward type of climate control strategy.
H. R. Gazor; O. R. Roustapour; R. Jahanian
Abstract
Introduction Long drying time and high energy consumption are the big problems in paddy drying using conventional batch type dryer. Besides, non-uniformity occurs in paddy rice dried and low milling quality. Paddy is over dried in lower layers and broken kernel chance increased in milling process. Using ...
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Introduction Long drying time and high energy consumption are the big problems in paddy drying using conventional batch type dryer. Besides, non-uniformity occurs in paddy rice dried and low milling quality. Paddy is over dried in lower layers and broken kernel chance increased in milling process. Using of a new pattern for warm air causes to better air passing through the paddy bulk and uniformity of drying. Computational fluid dynamics (CFD) is a good method for modeling of air passing in dryers in order to find better air condition in paddy drying process. The aim of this research was investigation on common and porch patterns applied for air entrance to paddy bulk in a dryer in order to optimize air channel conditions in a conventional paddy dryer. Materials and Methods In this study, optimization of air flow was investigated in a batch type paddy dryer using computational fluid dynamics (CFD). Two patterns as conventional and porch (reverse V type) patterns were applied for air entrance to paddy bulk in the dryer as conventional and porch (reverse V type) patterns. Experimental examination were done using a laboratory batch type dryer with chargeable air flow pattern in 50 °C for drying paddy (Tarom-Hashmei Var.). Numerical simulation of air velocity and pressure drop in porous media of paddy in the dryer was achieved by employing computational fluid dynamics method and Fluent software. Air velocity pattern and temperature changes in bulk of paddy were investigated in different time of solution including 20, 100, 1000, 1800, 3600 and 7200 seconds for both patterns. Results and Discussion Considering air flow and temperature as constant, the results showed the porch type pattern has better performance than the conventional pattern for air passing in the dryer. The velocity vortex was higher in all parts of the channel in the porch scheme. Air velocity uniformed decreased from beginning to end area in the conventional pattern, but in the porch type pattern, air velocity was more in the end of the duct than beginning area. Pressure drop was about 10 percent in the conventional pattern than porch pattern. At the end of the air channel, this variation inversed due to contact of the air with the end wall and pressure drop in this part of the chamber of porch scheme was higher than the conventional one. Improvement of air flow in paddy occurred in low and middle layers in the porch type pattern and there was no difference between two air passing patterns in top layers. Validation of modeling showed that temperature disturbance of the porch model was more uniform than the conventional model and difference between temperatures of model and experiments was about 2 to 3 °C. Conclusion The research concluded that using of the porch type pattern had better performance than the conventional pattern for air passing in the dryer but it is needs to more supplementary research to find the best height and angle in the paddy dryer. Porch type pattern causes to more speed and uniformity of air among of paddy than the conventional pattern. This improvement observed in low and middle layers of the paddy bulk. Validation of temperature data showed that the difference between experimental and modeled data was 4 to 6 percent and this difference was higher in the conventional pattern than the porch pattern. According to the results of this research, Porch pattern can be recommended to use in the conventional batch type dryer.
A. Pasban; M. Mohebbi; H. Sadrnia; S. A. Shahidi
Abstract
Introduction Convective air drying is one of the oldest and most popular drying methods. Designing and controlling the convective air drying needs the mathematical description of the moisture transfer during the drying process, known as drying kinetics. Fick’s second law of diffusion can be used ...
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Introduction Convective air drying is one of the oldest and most popular drying methods. Designing and controlling the convective air drying needs the mathematical description of the moisture transfer during the drying process, known as drying kinetics. Fick’s second law of diffusion can be used for modelling the moisture distribution inside the moist object during drying process. Mathematical modeling of drying process is a very important tool, as it contributes to understand better moisture distributions inside the product which helps designing, improving and controlling drying operation in the food industry. Implementation of the partial differential equations subject to the correspondent initial and boundary conditions is one of the main methods of mathematical modeling to describe the physical phenomena such as moisture transfer during drying. In the recent decades, considerable number of research works have been devoted to numerical solution of mass transfer phenomena during convective drying of food products by using the common numerical solution such as FDMs, FEMs and FVMs. The spectral collocation (pseudospectral) methods is a powerful tool for the numerical solutions of smooth PDEs like mass transfer equations. Pseudospectral methods are able to achieve the high precision with using a small number of discretization points compared to FDMs and FEMs and with low computational time and computer memory. The objective of present research is to simulate the mass transfer phenomena in one dimension during convective drying of apple slices. The validation of the presented numerical model was done by comparing experimental drying data taken from Kaya et al. (2007) and Zarein et al. (2013). For more confirming the numerical approach, a numerical example with the exact solution is provided and the related errors were evaluated. Materials and Methods Estimation of mass transfer coefficients The convective mass transfer coefficient in the surface of the apple slice was obtained according to the relationship presented by Paitil (1988) and Janjai et al. (2008). (1) Estimation of effective moisture diffusivity coefficient Fick’s second law of diffusion was applied to obtain the effective moisture diffusivity coefficient of the apple slices. The analytical solution of this equation can be written as follows (Crank, 1975): (2) In this study, we consider the Pseudospectral methods for solving 1D mass transfer equation. In order to develop the model, the following common assumptions are considered: negligible heat changes during drying process, moisture is transferred inside the slices by diffusion, one-dimensional mass transfer in apple slices, non-shrinkage and non-deformation of the slice. Results and Discussion In the field of numerical analysis, the main advantage of pseudospectral methods compared to others such as FDMs and FEMs are exponential convergence and sufficient accuracy (Sun et al., 2012). The values of parameters and coefficients of mathematical model are summarized in Table 1. The comparisons between the predicted average moisture content and the experimental data are shown in Fig. 1 & 2. It can be seen, the numerical results are in good agreement with the experimental data. The values of the correlation coefficient and the root mean square error from comparison of numerical result with experimental data taken from Zarein et al. (2013) and Kaya et al. (2007) were 0.9996, 0.0729 and 0.997, 0.1561 respectively. Moreover, the running time for solving 1D mass transfer equations was about 3 seconds. This result is the evident that the presented model is successful for predicting the moisture content history during drying process. Moreover, by using the considered numerical method the approximate solutions of defined numerical example for different discretizing points was evaluated and the associated error history are shown in Figure 3. It can be seen that the values of errors are very low and about 10-3 and 10-5, that confirms the high accuracy, robustness and efficiency of the suggested numerical approach. Conclusion Spectral collocation (pseudospectral) method is presented to solve mass transfer equation in one dimensional in during convective drying process approximately. The model was validated by the reported experimental data from convective drying of apple slices. Also, a numerical example, which had an exact solution in a closed form, was provided to illustrate the high accuracy of the proposed method. The results of statistical computations (r and RMSE) and numerical example showed the efficiency, applicability and robustness of the presented approach.
S. Haroni; M. J. Sheikhdavoodi; M. Kiani Deh Kiani
Abstract
Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many ...
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Introduction One of the most important sources of the sugar production is sugarcane.Sugar is one of the eight human food sources (wheat, rice, corn, sugar, cattle, sorghum, millet and cassava). Also sugarcane is mainly used for livestock feed, electricity generation, fiber and fertilizer and in many countries sugarcane is a renewable source for the biofuel. The efficient use of inputs in agriculture lead to the sustainable production and help to reduce the fossil fuel consumption and greenhouse gases emission and save financial resources. Furthermore, detecting relationship between the energy consumption and the yield is necessary to approach the sustainable agriculture. It is generally accepted that many countries try to reduce their dependence to agricultural crop productions of other countries. The being Independent on agricultural productions lead to take more attention to modern methods and the objective of all these methods is increasing the performance with the efficient use of inputs or optimizing energy consumptions in agricultural systems. Energy modeling is a modern method for farm management that this model can predict yield with using the different amount of inputs. The objective of this study was to predict sugarcane production yield and (greenhouse gas) GHG emissions on the basis of energy inputs. Materials and Methods This study was carried out in Khouzestan province of Iran. Data were collected from 55 plant farms in Debel khazai Agro-Industry using face to face questionnaire method. In this study, the energy used in the sugarcane production has considered for the energy analysis without taking into account the environmental sources of the energy such as radiation, wind, rain, etc. Energy consumption in sugarcane production was calculated based on direct and indirect energy sources including human, diesel fuel, chemical fertilizers, pesticides, machinery, irrigation water, electricity and sugarcane stalk. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. Input energy in agricultural systems includes both direct and indirect energy and renewable and non-renewable forms. Direct energies include human labor, diesel fuel, water for irrigation and electricity and indirect energies consisted of machinery, seed (cultivation of sugarcane has been done with cutting of sugarcane instead of seed), chemical fertilizer. Renewable energies include machinery, sugarcane stalk, chemical fertilizer while non-renewable energy consisted of machinery, chemical fertilizer, electricity and diesel fuel. Energy values were calculated by multiplying inputs and outputs per hectare by their coefficients of energy equivalents. The amounts of GHG emissions from inputs in sugarcane production per hectare were calculated by CO2 emissions coefficient of agricultural inputs. Energy modeling is an attractive subject for engineers and scientists who are concerned about the energy management. In the energy area, many different of models have been applied for modeling future energy. An artificial neural network (ANN) is an artificial intelligence that it can applied as a predictive tool for nonlinear multi parametric. Artificial neural network has been applied successfully in structural engineering modeling ANNs are inspired by biological neural networks. Results and Discussion The total energy used in the farm operations during the sugarcane production and the energy output was 1742883.769 and 111000 MJha_1, respectively. Electricity (52%) and chemical fertilizers (16%) were the most influential factors in the energy consumption. The electricity contribution was the highest due to the low efficiency of energy conversion in electric motors which were used for irrigation in the study area. In some areas, inefficient surface irrigation wastes a lot of water and energy (in forms of electricity). Another reason is that electricity energy equivalent for Iranian electricity production is higher than developed countries because Iran’s electricity grid is highly dependent on fossil fuels, so that 95% of the electrical energy in Iran is generated in thermal power plants using fossil fuels sources. In addition, the electricity transmission system is too old. GHG emissions data analysis indicated that the total GHG emissions was 415337.62 kg ha-1 (CO2eq) kgCO2eq ha-1 in which burning trash with the share of 62% had the highest GHG emission and followed by electricity (32%), respectively. The ANN model with 7-5-15-1 and 5-5-1 structure were the best model for predicting the sugarcane yield and GHG emissions, respectively. The coefficients of determination (R2) of the best topology were 0.98 and 0.99 for the sugarcane yield and GHG emissions, respectively. The values of RMSE for sugarcane production and GHG emission were found to be 0.0037 and 4.52×10-6, respectively. Conclusion The statistical parameters of R2 and RMSE demonstrated that the proposed artificial neural networks results have best accuracy and can predict the yield and GHG emission. It is generally showed that artificial neural networks have good potential to predict the yield of the sugarcane production.
R. Karmulla Chaab; S. H. Karparvarfard; M. Edalat; H. Rahmanian- Koushkaki
Abstract
Introduction One of the problems which considered in recent years for grain harvesting is loss of wheat during production until consumption and tenders the offers for prevention of its especially in harvesting times by combine harvesting machine. Grain harvesting combines are good examples of an operation ...
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Introduction One of the problems which considered in recent years for grain harvesting is loss of wheat during production until consumption and tenders the offers for prevention of its especially in harvesting times by combine harvesting machine. Grain harvesting combines are good examples of an operation where a compromise must be made. One would expect increased costs because of natural loss before harvesting, because of cutter bar loss, because of threshing loss, because of greater losses over the sieve and because of the reduced forward speed necessary to permit the through put material to feed passed the cylinder. The ability to recognize and evaluate compromise solutions and be able to predict the loosed grain is a valuable trait of the harvesting machine manager. By understanding the detailed operation of machines, be able to check their performance, and then arrive at adjustments or operating producers which produce the greatest economic return. Voicu et al. (2007) predicted the grain loss in cleaning part of the combine harvester by using the laboratory simulator based on dimensional analysis method. The obtained model was capable to predict the grain loss perfectly. Soleimani and Kasraei (2012) designed and developed a header simulator to optimize the combine header in rapeseed harvesting. Parameters of interest were: forward speed, cutter bar speed and reel index. The results showed that all the factors were significant in 5% probability. Also in the case of forward speed was 2 km h-1, cutter bar speed was 1400 rpm and reel index was 1.5, the grain loss had minimum quantity. The main purpose of this research was to develop an equation for predicting grain loss in combine header simulator. Modeling of the header grain loss was conducted using dimensional analysis approach. Effective factors on grain loss in combine header unit were: forward speed, reel speed and cutter bar height. Materials and Methods For studying the effective parameters on head loss in grain combine harvester, a header simulator with the following components was built in Biosystems Engineering Department of Shiraz University. Reel unit The reel size was 120 cm length and 100 cm diameter. This reel was removed from an old combine header and installed on a fixed bed. For changing the rotational speed of the reel, an electrical inverter (N50-007SF, Korea) was used. Cutter bar unit The cutter bar length was 120 cm. Knifes were installed on this section. Reciprocating motion was transmitted to the cutter bar through a slider crank attached to a variable speed electric motor (1.5kw, 1400 rpm, Poland). The motor was fixed on the bed. Feeder unit This section was consisted of a rail and a virtual ground. This ground was a tray that the wheat stems were staying on it manually. The rail was the path of virtual ground. Treatments consisted of three levels of rotational speed of reel (21, 25 and 30 rpm), three levels of forward speed of virtual ground (2, 3 and 4 km h-1), three levels of cutter bar height (15, 25 and 35 cm) and three replications. In other words, 81 tests were done. The basis of choosing levels of treatments was combine harvester manuals and driver’s experiences. The dependent variable (H.L) was calculated as below: (1) Where L.G is the mass of loss grains and H.G is the mass of harvested grains. Results and Discussion Generally results of ANOVA test showed that the cutter bar height, rotational speed of reel and forward speed had significant effect on head loss. Also interaction of rotational speed and forward speed, cutter bar height and forward speed had significant effect on head loss. These findings were based on Soleimani and Kasraei (2012) research. Therefore, the cutter bar height, rotational speed of reel and forward speed were three independent parameters on head loss as a dependent parameter. By results of laboratory data, the equation for predicting grain loss by header simulator was obtained. Conclusion The statistical results of F- test in 5% probability showed that there were no significant difference between measured and predicted amounts for laboratory data.
Modeling
Gh. Shahgholi; H. Ghafouri Chiyaneh; T. Mesri Gundoshmian
Abstract
Introduction Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The ...
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Introduction Soil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The soil destruction may be as surface deformation or as subsurface compaction. Any way machine traffic destructs soil structure and as result has unfavorable effect on the yield. Hence, soil compaction recognition and its management are very important. In general, soil compaction is the most destructive effect of machine traffic. Modeling of ecological systems by conventional modeling methods due to the multitude effective parameters has always been challenging. Artificial intelligence and soft computing methods due to their simplicity, high precision in simulation of complex and nonlinear processes are highly regarded. The purpose of this research was the modeling of soil compaction system affected by soil moisture content, the tractor forward velocity and soil depth by multilayer perceptron neural network. Materials and Methods In order to carry out the field experiments, a tractor MF285 which was equipped with a three-tilt moldboard plough was used. Experiments were conducted at the Agricultural research field of University of Mohaghegh Ardabili in five levels of moisture content of 11, 14, 16, 19 and 22%, forward velocity of 1, 2, 3, 4 and 5 km.h-1, and soil depths of 20, 25, 30, 35 and 40 cm as a randomized complete block design with three replications. In this study, perceptron neural network with five neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was designed and trained. Results and Discussion Field experiments showed three main factors were significant on the bulk density (P<0.01). The mutual effect of moisture on depth and mutual binary effect of moisture on velocity and depth on velocity were significant (P<0.01). Mutual triplet effect of moisture on velocity on depth was significant (P<0.05). Maximum bulk density of 1362 kg/m3 was obtained at the highest moisture of 22% and the lowest forward velocity of 1 km/h at the depth of 20 cm. Whilst the minimum value of 1234.5 kg/m3 was related to the moisture, forward velocity and depth of 11%, 5 km/h and depth of 40 cm, respectively. Compaction increased as soil moisture content increased up to 22% which was critical moisture. In contrast, soil compaction decreased as the tractor velocity and soil depth increased. A comparison of neural network output and experimental results indicated a high determination coefficient of R2 = 0.99 between them. Also, the mean square error of the model was 0.174, in addition, mean absolute percentage error of the system (MAPE) was equal to %0.29 which showed high accuracy of neural network to model soil compaction.ConclusionIt was concluded that soil compaction increased as soil moisture content increased up to a critical value. Increasing soil moisture act as lubricant and soil layers compacted together. Hence knowledge of soil moisture can help us to manage soil compaction during agricultural operations. Increasing the tractor forward velocity reduced soil compaction. However, agricultural operations should be conducted at certain speeds to carry out the duty properly and increasing speed more that value decreases the efficiency of work.Neural network of MLP with 5 neurons in hidden layer and sigmoid function in middle layer and one neuron with linear transfer function was found the most accurate and precise in prediction of the soil bulk density. A high determination coefficient of R2 = 0.99 was found between measured and predicted values.
O. Ghaderpour; Sh. Rafiee; M. Sharifi
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 ...
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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.
Modeling
J. Taghinazhad; R. Abdi; M. Adl
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 ...
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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.
N. Gholamrezaei; K. Qaderi; K. Jafari Naeimi
Abstract
Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent ...
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Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent methods are applied in a lot of fields such as classification, pattern recognition, prediction and modeling of processes. To detect and classify several agricultural crops, a research was conducted based on texture and color feature. The highest classification accuracy for vegetables, grains and fruits with using artificial neural network were 80%, 86% and 70%. In this research, the ability to Multilayer Perceptron (MLP) Neural Network in predicting energy consumption, temperature and humidity in different coordinate placement of electronic control unit sensors in the poultry house environment was examined. Materials and Methods The experiments were conducted in a poultry unit (3000 pieces) that is located in Fars province, Marvdasht city, Ramjerd town, with dimensions of 32 meters long, 7 meters wide and 2.2 meters height. To determine the appropriate placement of the sensor, 60 different points in terms of length, width and height in poultry were selected. Initially, the data was divided into two datasets. 80 percent of total data as a training set and 20 percent of total data as a test set. From180 observations, 144 data were used to train network and 36 data were used to test the process. There are several criteria for evaluating predictive models that they are mainly based according to the difference between the predicted outputs and actual outputs. To evaluate the performance of the model, two statistical indexes, mean squared error (MSE) and the coefficient of determination (R²) were used. Results and Discussions In this study, to train artificial neural network for predicting the temperature, humidity and energy consumption, the trainlm algorithm (Levenberg-Marquardt) was used. To simulate temperature, humidity and energy consumption, networks were trained with two and three layers, respectively. Network with two layers with10 neurons in the hidden layer and one neuron in the output layer with (R²) equal to 0.96 and (MSE) equal to 0.00912, was given the best result for predicting temperature. For humidity electronic sensors, results showed that network with three layers with the 10 neurons in the first hidden layer, 20 neurons in the second hidden layer and one neuron in the output layer with (R²) equal to 0.8 and (MSE) equal to 0.00783 was the best for predicting humidity. Finally, network with two layers with 10 neurons in the first hidden layer, 10 neurons in the second hidden layer and one neuron in the output layer was selected as the optimal structure for predicting energy consumption. For this topology, (R²) and MSE were determined to 0.98 and 0.00114, respectively. Linear and multivariate regression for the parameters affecting temperature, humidity and energy consumption of electronic sensors was determined by the STATGR software. Correlation coefficients indicated that parameters such as length, height and width of the electronic control sensors placed in the poultry hall justified 82% of the temperature changes, 61% of the humidity changes and 92% of the energy consumption changes. Therefore, comparing with correlation coefficients obtained from the neural network models, the highest correlation coefficient was related to energy parameter and the lowest correlation was linked to humidity parameter. Conclusion The results of the study indicated the high performance for predicting temperature, humidity and energy consumption. The networks hadthree inputs including length, width and height of electronic sensor positions and an output for temperature, humidity and energy consumption. For training networks the multiple layer perceptron (MLP) with error back propagation learning algorithm (BP) was used. Functions activity for all networks in hidden layers were tangentsigmoid and in the output layer, linear (purelin). Comparing the results of artificial neural network and logistic regression model showed that artificial neural network model with correlation coefficients of 0.98 (energy), 0.96 (temperature) and 0.8 (humidity) provided closer data to the actual data compared with regression models with correlation coefficients of 0.92, 0.82 and 0.61 for the energy, temperature and humidity respectively.
R. Rostami Baroji; S. S. Seiiedlou Heris; J. Dehghannya
Abstract
Introduction Drying foods, fruits and vegetables is a suitable method to reduce post-harvest losses of the crops. Drying is considered as a simultaneous heat and mass transfer process. Various physical, chemical and nutritional changes occur during drying of foods and are affected by a number of internal ...
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Introduction Drying foods, fruits and vegetables is a suitable method to reduce post-harvest losses of the crops. Drying is considered as a simultaneous heat and mass transfer process. Various physical, chemical and nutritional changes occur during drying of foods and are affected by a number of internal and external heat and mass transfer parameters. External parameters may include temperature, velocity and relative humidity of the drying medium (air), while internal parameters may include density, permeability, porosity, sorption–desorption characteristics and thermo physical properties of the material being dried. In this regard, understanding the heat and mass transfer in the product will help to improve drying process parameters and hence the quality. The mathematical model that reflects the drying process physics is a complex model. Particularly because of the process of convection drying of materials with high initial water content, boundary conditions should be assumed in the model describing heat and mass transfer. Ruiz-López and García-Alvarado (2007) proposed a model that provides a simple mathematical description for food drying kinetics and considered both shrinkage and a moisture dependent diffusivity. Food temperature was considered constant. The objectives of this work are: (a) to develop a mathematical model for simulating simultaneous moisture transport and heat transfer of pretreated carrot sample; (b) to study numerically the effect of the air drying conditions and pretreated on the drying of carrot and (c) to calculate the density and effective diffusion coefficients of carrot under various conditions. Materials and Methods In order to compare experimental and numerical analysis results, a laboratory scale convection dryer was used for experimental work. Cylindrical samples before entering the dryer were pretreated with ultrasound at frequency of 28 kHz for 10 min and microwave at 1 W g-1 power for 15 min. Experimental results of moisture evolution and volume changes during drying were used to estimate moisture diffusivity and product density. Transient three-dimensional simulation of heat and mass transfer was performed with a set of initial and boundary conditions using the finite element method. The effect of the aforementioned pretreatments was applied in terms of the modified effective moisture diffusion coefficient in the heat and mass transfer equations. Results and Discussion The effect of the ultrasonic pretreatment on drying was mainly observed during the air-drying stage where a significant increase in water effective diffusivity was found. Ultrasonic waves can cause a rapid series of alternative compressions and expansions, in a similar way to a sponge when it is squeezed and released repeatedly (sponge effect). Microwave pretreatment reduced the initial moisture content and slightly increased the coefficient. The values of moisture diffusivity found in this study was in the order of - m2 s-1 which is typical value for drying of agricultural product (Zielinska and Markowski, 2010). Comparison of the experimental and predicted moisture and temperature profiles showed that the model could predict the heat and mass transfer phenomena with good accuracy. In this section, some simulation results are presented. The simulated moisture contents in the center and on the surface during drying showed that moisture content on the surface decreases rapidly for a short time due to the evaporation during precooling. Then it starts to increase because of the moisture diffusion from the layers under the surface towards. The temperature inside the object increases with an increase in the drying time since the temperature of the drying air is higher than that of the object. As a result of these transient and non-uniform temperature distributions, the moisture diffusivity which depends on the moisture will vary and in turn the rate of the moisture diffusion inside the object. As seen in the figure, the distributions appear not to be symmetrical. Higher temperature and moisture gradients are obtained at the side wall due to the upstream of the drying air. Conclusion A theoretical analysis of pretreated and non-pretreated carrot drying process was presented. The main innovation introduced by this study was represented by the model formulation. This, in fact, simulated the simultaneous three dimensional heat and moisture transfer accounting for the variation of both air and food physical properties as functions of local values of temperature and moisture content. Moisture diffusivities of pretreated and non-pretreated carrot have been determined experimentally and moisture diffusivities of pretreated and non-pretreated carrot were found to increase with using of ultrasound pretreated. The effect of the aforementioned pretreatments was applied in terms of the modified effective moisture diffusion coefficient in the heat and mass transfer equations. Comparison of the experimental and predicted moisture and temperature profiles showed that the model could predict the heat and mass transfer phenomena with good accuracy. The model can be used as a proper tool in the design optimization and the optimal determination of the dryer performance parameters.
M. Taki; Y. Ajabshirchi; S. F. Ranjbar; A. Rohani; M. Matloobi
Abstract
Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. Predicting ...
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Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Some works were done in past to 2015 year to simulation and predict the inside variables in different greenhouse structures. Usually simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. The main objective of this paper is comparison between heat transfer and regression models to evaluate them to predict inside air and roof temperature in a semi-solar greenhouse in Tabriz University. Materials and Methods In this study, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (geographical location of 38°10′ N and 46°18′ E with elevation of 1364 m above the sea level). In this research, shape and orientation of the greenhouse, selected between some greenhouses common shapes and according to receive maximum solar radiation whole the year. Also internal thermal screen and cement north wall was used to store and prevent of heat lost during the cold period of year. So we called this structure, ‘semi-solar’ greenhouse. It was covered with glass (4 mm thickness). It occupies a surface of approximately 15.36 m2 and 26.4 m3. The orientation of this greenhouse was East–West and perpendicular to the direction of the wind prevailing. To measure the temperature and the relative humidity of the air, soil and roof inside and outside the greenhouse, the SHT 11 sensors were used. The accuracy of the measurement of temperature was ±0.4% at 20 °C and the precision measurement of the moisture was ±3% for a clear sky. We used these sensors in soil, on the roof (inside greenhouse) and in the air of greenhouse and outside to measure the temperature and relative humidity. At a 1 m height above the ground outside the greenhouse, we used a pyranometre type TES 1333. Its sensitivity was proportional to the cosine of the incidence angle of the radiation. It is a measure of global radiation of the spectral band solar in the 400–1110 nm. Its measurement accuracy was approximately ±5%. Some heat transfer models used to predict the inside and roof temperature are according to equation (1) and (5): Results and Discussion Results showed that solar radiation on the roof of semi-solar greenhouse was higher after noon so this shape can receive high amounts of solar energy during a day. From statistical point of view, both desired and predicted test data have been analyzed to determine whether there are statistically significant differences between them. The null hypothesis assumes that statistical parameters of both series are equal. P value was used to check each hypothesis. Its threshold value was 0.05. If p value is greater than the threshold, the null hypothesis is then fulfilled. To check the differences between the data series, different tests were performed and p value was calculated for each case. The so called t-test was used to compare the means of both series. It was also assumed that the variance of both samples could be considered equal. The variance was analyzed using the F-test. Here, a normal distribution of samples was assumed. The results showed that the p values for heat model in all 2 statistical factors (Comparison of means, and variance) is lower than regression model and so the heat model did not have a good efficient to predict Tri and Ta. RMSE, MAPE, EF and W factor was calculated for to models. Results showed that heat model cannot predict the inside air and roof temperature compare to regression model. Conclusion This article focused on the application of heat and regression models to predict inside air (Ta) and roof (Tri) temperature of a semi-solar greenhouse in Iran. To show the applicability and superiority of the proposed approach, the measured data of inside air and roof temperature were used. To improve the output, the data was first preprocessed. Results showed that RMSE for heat model to predict Ta and Tri is about 1.58 and 6.56 times higher than this factor for regression model. Also EF and W factor for heat model to predict above factors is about 0.003 and 0.041, 0.013 and 0.220 lower than regression model respectively. We propose to use Artificial Neural Network (ANN) and Genetic Algorithm (GA) to predict inside variables in greenhouses and compare the results with heat and regression models.
A. Kosari Moghaddam; H. Sadrnia; H. Aghel; M. Bannayan Aval
Abstract
Introduction
The working day is an important component in selection and analysis of farm machinery systems. The number of working days is affected by various factors such as climate, soil characteristics and type of operation. Daily soil moisture models based on weather long-term data and soil characteristics ...
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Introduction
The working day is an important component in selection and analysis of farm machinery systems. The number of working days is affected by various factors such as climate, soil characteristics and type of operation. Daily soil moisture models based on weather long-term data and soil characteristics were almost used for calculating probability of working days. The goal of this study was to develop a simulation model to predict the number of working days for secondary tillage and planting operation in fall at 50, 80 and 90% probability levels.
Materials and Methods
A Simulation model was developed using 21 years weather data and soil characteristics for calculate daily soil moisture content in Research Station of Ferdowsi University of Mashhad. So soil moisture was calculated using daily soil water equation for top 25 centimeter of soil depth. Moisture equal or lower than 85% of soil field capacity and precipitation lower than 4 millimeter (local data) were considered as soil workability criteria. Then the working days were determined for secondary tillage and planting operation at 50, 80 and 90% probability levels in falls. The number of days at 50% probability was the mean over 21 years and the number of days at 80% and 90% were determined for each two weeks period as the average number of working days minus the product of t value and standard deviation of those numbers.
Model Evaluation
Evaluation of model included a comparison of predicted and the observed the number of working days in Research Station of Ferdowsi University of Mashhad during 2002-2010 and sensitivity analysis was implemented to test the effect of changes in soil workability criterion (80, 90, 95 and 100% of soil field capacity), drainage coefficient (25 % decrease and increase) and soil field capacity (40% increase) on simulation results.
Results and Discussion
Comparison of predicted and observed days showed that correlation coefficient was 0.998 and the difference between the simulated data and observed data was not significant at the 5% level.
Results from sensitivity analysis in Table 3 showed that when soil workability, drainage coefficient and field capacity increased, the number of working days increased, but model sensitivity was very low to drainage coefficient and soil field capacity. In general, the most important factor is precipitation in this weather conditions.
The number of working days for secondary tillage and planting operation for each period in fall are shown in Table 4.
Conclusions
A simulation model was developed for predicting the number of working days for secondary tillage and planting operation in fall. This model was based on weather long-term data and soil characteristics for the Research Station of Ferdowsi University of Mashhad. The most important factor was precipitation and the model had low sensitivity to drainage coefficient and soil field capacity. The number of working days in 50%, 80% and 90% probability levels for period of ten days was on average 9.94, 9.21, 8.57 days for 23th September to 22th October and 9.77, 8.02, 6.41 days for 23th October to 21th November and 9.68, 7.48 and 5.24 for 22th November to 21th December, respectively.
S. M. Mir-ahmadi; S. A. Mireei; M. Sadeghi; A. Hemmat
Abstract
Introduction: Iran is one of the main producers of kiwifruit in the world. Unfortunately, the sorting and grading of the kiwifruits are manual, which is a time consuming and labor intensive task. Due to the lack of appropriate devices for sorting and grading of kiwifruit based on the quality parameters, ...
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Introduction: Iran is one of the main producers of kiwifruit in the world. Unfortunately, the sorting and grading of the kiwifruits are manual, which is a time consuming and labor intensive task. Due to the lack of appropriate devices for sorting and grading of kiwifruit based on the quality parameters, only 10% of total production is exported (Mohammadian & Esehaghi Teymouri, 1999).
One of the main quality attribute for evaluating the kiwifruits is weight. Based on the standards, the minimum weight for an excellent kiwifruit is 90 g, while these values for the first and second classes should be 70 and 65 g, respectively (Abedini, 2003). Therefore, developing a device for fast weighing of fruits in the sorting lines can be useful in packaging, storage, exporting and distributing kiwifruit to the consumer markets.
In the past, the mechanical-based systems were commonly used for online weighing of the agricultural materials, but they did not lead to the promising accuracy and speed in sorting lines. Today, electrical instruments equipped with the precise load cells are substituted for fast weighing in the sorting lines. The dropping impact method, in which a free falling fruit drops on a load cell, is one of the suitable techniques for this purpose.
Different studies have addressed the application of dropping impact for fast weighing of agricultural materials (Rohrbach et al., 1982; Calpe et al., 2002; Gilman & Bailey, 2005; Stropek & Gołacki, 2007; Elbeltagi, 2011). The aim of this study reported here was to develop an on-line system for fast weighing of kiwifruit and compare the accuracy of different methods for extracting the weight predictive models.
Materials and Methods:
Sample selection: A total of 232 samples with the weight range of 40 to 120 g were selected. Before conducting the main experiments, the weight and dimensions of the sample were measured using a digital balance and caliper, with the precisions of 0.001 g and 0.01 mm, respectively.
Impact measuring system: The impact signals of kiwifruits in an online situation were acquired using a system, including conveying and ejecting unit, a load cell and data acquisition unit (Fig.1). The load cell was a single point load cell with 5 kg capacity. The load cell was connected to the data acquisition unit (Fig.2) in order to record the impact signal of the device in time domain of 0-5 s.
Before performing the main experiments, the load cell was calibrated using 100, 200, 500 and 1000 g standard masses. All the tests were carried out on three different forward speeds of conveyor, including 1, 1.5 and 2 m s-1 in order to obtain the optimum forward speed.
Data Analysis: In this study, two different methods were applied to build the weight predictive models. In the first method, the main components of the impact signal, including the force value at the first peak Fp, time required to peak force Dp, and the impulse or area under the first peak Ip were calculated and used as independent variables to develop the weight predictive models. In the second method, the impact components were calculated for the 40 successive peaks. Multiple linear regression (MLR) analyses were used to correlate the independent (impact components) and dependent (weight) variables.
Results and Discussion: The weight statistical characteristics of the samples, including the maximum, minimum, average, standard deviation and coefficient of variability in total data, calibration and test sets are shown in Table 1. As depicted, almost the same range and variability were observed for calibration and test data sets, indicating the proper distribution of the samples.
Table 2 summarizes the results of simple and multiple linear regressions for predicting the weight from the signal components (Fp, Dp, Ip) of the first peak at different speeds of 1, 1.5 and 2 m s-1. As shown, at the forward speeds of 1 and 2 m s-1, the multiple regression models based on all three signal components, and at forward speed of 1.5 m s-1, the model based on the combination of Fp and Ip, resulted to the best prediction powers. Among different forward speeds, the forward speed of 1 m s-1 gave the best model with SDR value of 2.180. Fig.4 depicted the predicted versus true values of weight obtained from the best linear regression models using components of Fp, Dp, Ip, Fp-Ip, and multiple of the first peak of impact signal.
The results of simple and multiple linear regression for predicting the weight from the signal components (Fp, Dp, Ip) of the first forty peaks at different speeds of 1, 1.5 and 2 m s-1 are summarized in Table 3. The best models were obtained by multiple combination of all three impact signals at the forward speed of 1 and 2 m s-1, and combination of Fpi-Ipi (i=1,...,40) at 1.5 m s-1 speed. Compared with the first peak results, the accuracy of prediction reached to 84%, 60% and 52% at forward speeds of 1, 1.5 and 2 m s-1, respectively. The best results were obtained at a forward speed of 2 m s-1, in which the SDR reached to a satisfactory value of 2.857 by applying the Ipi (i=1,...,40) values. The predicted versus true values of weight obtained from the best linear regression models using components of Fp, Dp, Ip, Fp-Ip, and multiple of the first forty peaks of impact signal are illustrated in Fig.5.
Conclusions: The results of this study revealed that among different impact component, Ip was the best predictor of the kiwifruits weight. Moreover, the developed models based on impact components of the first forty successive peaks gave the best accuracy with respect to the first peak components.
I. Ahmadi
Abstract
Cumulative effect of transmitted vibrations to the tractor driver not only leads to driver health problems, but also reduces the driver working efficiency. Tractor suspension system is one of the methods which is employed to lower the level of transmitted vibrations to the driver. In this study the design ...
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Cumulative effect of transmitted vibrations to the tractor driver not only leads to driver health problems, but also reduces the driver working efficiency. Tractor suspension system is one of the methods which is employed to lower the level of transmitted vibrations to the driver. In this study the design and performance assessment of a semi-active suspension model of tractor cabin was considered. Tractor full vibration model was developed first, and subsequently a semi-active ON-OFF damper model was designed. The examination of the model indicated that doubling the piston area and the volume of hydraulic accumulator air chamber, led to 39% increase and 31% reduction of the resonance frequency of transmitted vibrations to the driver, respectively. On the other hand doubling the piston area and the primary air pressure of the accumulator, affected the RMS of transmitted vibration to the driver by 77 cm s-2 reduction and 66 cm s-2 increase, respectively. Moreover, the numerical comparison of the model outputs with and without activation of semi-active cabin suspension, while the model was stimulated with the same input function, led to 43% improvement in RMS acceleration of the transmitted vibrations to the tractor seat. Therefore, the designed semi-active suspension model of cabin was able to attenuate the level of transmitted vibrations to the tractor driver.
S. Abbasi; S. Minaei; M. H. Khoshtaghaza
Abstract
In this study thin layer drying of corn in a convective dryer was investigated at air temperatures of 50, 60 and 70ºC and air flow rates of 1, 1.4 and 1.8 kg min-1. Experiments were performed in Completely Randomized Design (CRD). The effect of air temperature and flow rate on drying time, drying ...
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In this study thin layer drying of corn in a convective dryer was investigated at air temperatures of 50, 60 and 70ºC and air flow rates of 1, 1.4 and 1.8 kg min-1. Experiments were performed in Completely Randomized Design (CRD). The effect of air temperature and flow rate on drying time, drying rate, effective diffusivity coefficient and activation energy were studied. Results showed that the effects of temperature and flow rate on drying process were significant. Increasing the air temperature from 50 to 70 ˚C, caused 31.7 percent decrease in drying time and change of air flow rate from 1 to 1.8 kg min-1 reduced drying time 27 percent in average. The effective diffusivity coefficient and activation energy varied from 3.47258 ×10-11 to 7.34352×10-11 m2 s-1. and 13.761 to 16.193 kJ mol-1, respectively depending on the drying treatments. The Logarithmic model was found to be in a better agreement with experimental data compared with other models. The minimum value of specific energy requirement (3.61 kWh kg-1) was obtained at a drying air temperature of 50 °C and air flow rate of 1 kg min-1, whereas the corresponding parameters for the maximum value (5.34 kWh kg-1) were determined as 70 °C and air flow rate 1.8 kg min-1.
