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.
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.
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.
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.
