B. Rahmati nejad; M. Abbasgholipour; B. Mohammadi Alasti
Abstract
IntroductionMore than 30% of the heat energy generated by the engine is transferred by the cooling system. If this heat transfer is not accomplished properly, then the engine heat will increase and it will wear the parts by removing oil film between the pieces. A cooling system is used to remove this ...
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IntroductionMore than 30% of the heat energy generated by the engine is transferred by the cooling system. If this heat transfer is not accomplished properly, then the engine heat will increase and it will wear the parts by removing oil film between the pieces. A cooling system is used to remove this heat. The radiator is an important component of this system. Increasing heat transfer in the car engine by the cooling system is possible by using two methods of changing the radiator geometry and optimizing it and using fluids with high thermal properties. In this research, we investigated the improvement of radiator thermal performance using nanofluids using a laboratory model. The effect of nanoparticle volume fraction and cooling flow rate on heat transfer rate, and heat transfer coefficient was investigated.Materials and MethodsIn this research, a laboratory model was designed and manufactured to evaluate the thermal performance of the MF 285 tractor radiator using nanofluid. In this laboratory model, water was combined and used as a base fluid with nanoparticles AL2O3. 20 nm nanoparticles with volume percentages of 1 to 4% were used. An electric stirrer and magnetic stirrer were used to prepare the nanofluid. For the produced fluid to be usable, add SDBS surfactant to it. The temperature of the inlet fluid to the radiator was 85 °C and the cooling fluid flow rate was 3.18 to 15.08 (lit. min-1 )) and the airflow rate was 3.2 to 6.4 (m s-1). Two T-type thermocouples are installed to measure the inlet and outlet temperature of the radiator and two other front and rear fans to measure the inlet and outlet air temperature and four more are installed on the radiator to measure the radiator body temperature.Results and DiscussionThe results show that in nanofluid with a 4% volume fraction compared to a 1% volume fraction, it can be seen an increase of 8.7% in density, 7.7% in viscosity, and 9.1% in thermal conductivity, and also a decrease of 8.8% in specific heat. The maximum temperature difference between the inlet and outlet sensors of the radiator when the thermostat is open and the cooling fluid flows through the radiator is 12 to 15 °C. By increasing the speed of the electromotor from 40 Hz to 50 Hz, the temperature of the water cooling fluid at the outlet part becomes 4.7 °C cooler and the air temperature at the outlet part becomes 7.3 °C warmer. As the speed of the electromotor increases, the rate of heat transfer increases. At the maximum value of airflow and cooling fluid, by adding 4% by volume of nanoparticles to the base fluid, the rate of heat transfer can be increased about 37% compared to the base fluid. Compared to water, nanofluid containing 4% by volume of AL2O3 at maximum speed has a 28% increase in heat transfer coefficient. Also, by increasing the electric motor speed from 20 Hz to 40 Hz, the heat transfer coefficient of pure water shows about 26% increase and the nanofluid shows an average of 29% increase.ConclusionIncreasing the volume fraction of nanoparticles suspended AL2O3 in the base fluid increases the density, viscosity, and thermal conductivity, which increases the heat transfer rate and reduces the outlet temperature of the radiator. The presence of nanofluid in the engine cooling system increases the heat transfer from the radiator, and despite this feature, the size and weight of the radiator can be reduced without affecting its heat transfer performance. It can also improve heat transfer performance by increasing the cooling flow rate and the airflow rate.
M. Abbasgholipour
Abstract
Introduction Corn harvest losses are imposed by several factors, the most important of which is harvesting-time. Since the harvesting-time is coincident with the rainy season, it is necessary to appropriately estimate the corn harvest time to avoid harvesting losses and losing the next cultivation. Accordingly, ...
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Introduction Corn harvest losses are imposed by several factors, the most important of which is harvesting-time. Since the harvesting-time is coincident with the rainy season, it is necessary to appropriately estimate the corn harvest time to avoid harvesting losses and losing the next cultivation. Accordingly, in the current research, the effect of harvesting-time on corn losses during the month and the day has been into consideration. An expert fuzzy system was designed to predict the best harvest time as it operates based on the losses amounts which are measured in processing and collection units into the combine, and losses due to the humidity percentage. Materials and Methods In this paper, corn harvest losses in a John Deere Combine, Model 1165, was studied in a different climatic circumstance in Moghan region. Moreover, a split plot experiment in a completely randomized block design was conducted with three replications. The losses data were collected from the processing and collection units of the combine harvester on the November 5th, 8th and 11th, 2017, in three different daily times of 8-10, 11-13 and 14-16 with three replications. The Mamdani fuzzy inference system with singleton fuzzifire and center average defuzzifire was used to develop a fuzzy expert system. In the designed expert system, the losses percentage in the processing and collection units and the humidity percentage were considered as system inputs and optimal harvesting time was used as the system output. "Low, Very low, high and very high" and "Best, Suitable, Unfit, and Worst" were four groups of linguistic variables for input and output parameters, respectively. These variables follow the triangular and trapezoidal membership functions. The number of 64 fuzzy rules were considered and introduced into the fuzzy system by experts, experienced farmers, and combiners. Furthermore, the same field data (measured data) were applied to evaluate the designed system, so that the predicted value was accounted as the system output. Results and Discussion Analysis of variance showed that there was a significant difference between the harvesting dates at the 0.05 probability level and significant difference between the harvesting times of a day at the 0.01 probability level. It can be concluded that the harvest dates and harvest times of a day were very effective in the number of corn losses, but the interaction effects were not significant. The results appeared that the lowest losses were 10.05% on November 8th, 2017, at 14-16 p.m., and the highest losses were 12.88% on November 11th, 2017, at 8-10 a.m. The amount of losses was increased due to the higher air humidity and lower temperature. In the fuzzy simulation model, the suitable harvesting-time can be predicted based on the losses quantities in the processing and collection units and the humidity percentage. The results showed that the predicted values for harvesting-times, by a designed fuzzy system, were completely matched with measured values in this study. The coefficient of determination (R2) was 0.980 between measured and predicted harvesting times. This coefficient demonstrated that the developed fuzzy logic system was suitable for prediction of harvesting time in the studied area. Conclusion The experimental observations in the field and data analysis showed that in the corn harvesting in the Moghan region, the humidity level, date, and harvesting-time were the most effective factors in the harvesting losses. In this paper, based on measured data from a small farm and implementation of the expert fuzzy system, the most suitable harvest date was set on November 8th at 14-16 p.m, at 21-24°C and relative humidity of 44%-53% to have 10.5% losses which has been confirmed by the lowest losses observed in the corn plan (10%). Moreover, the high value of the determination coefficient demonstrates a high correlation between measured and predicted data.
Design and Construction
J. Ghezavati; D. Mohammad Zamani; M. Abbasgholipour; B. Mohammadi Alasti; A. Ranji
Abstract
Introduction: From an economic viewpoint, tomato is considered as the second most valuable crop after potato. It is also preceded by the potato in terms of per capita consumption in the world. In 2008, the cultivation area used for the tomato as equal to 163,539 hectares in Iran and the production of ...
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Introduction: From an economic viewpoint, tomato is considered as the second most valuable crop after potato. It is also preceded by the potato in terms of per capita consumption in the world. In 2008, the cultivation area used for the tomato as equal to 163,539 hectares in Iran and the production of it was equal to 5,887,715 tons with an average production of 117,887 tons in 4352 hectares in the provinces, respectively. Having high production volume and quality, costly hybrid seeds are currently used for the major planting areas of vegetable in Iran. Most of the used transplanted seedlings are 83%. Since the seeds are expensive, the percentage of seedlings and healthy and disease-free seeds should be used for maximized germination and be transferred to the fields of open space. Preparing seedlings in transplanting trays is a technology to respond to this need. Trays are covered with a layer of Peat and Miculite fertilizers. Then, one seed is manually placed in each cell after gauging and preparing a suitable field. However, manually placing seeds is time-consuming and requires hard labor. Sixteen working labors per hour are required for 15 × 7 cell in order to have 10200 seedlings grown in 100 trays. Due to lack of adequate labor, production capacity of greenhouses is reduced, especially in the farming season when finding labor for planting vegetable sprouts is laborious. Therefore, mechanizing tray seeding operations is essential to increase the capacity of the growing industry of greenhouses in Iran.Materials and Methods: Initially, the tomato seeds were examined in the laboratory. The most important parameters of the study included size, shape, weight, the speed of getting out of the tank and the minimum carrying speed. Then, a vacuum-based single seed picking unit was prepared to investigate the factors influencing the design, so that a single tomato seed can be harvested from the masses. The most important factors considered in the design and construction included: cost, ease of performance, portability, use of local equipment, the planter’s capacity as well as the style of picking single seeds (In Fig.1, the original scheme of the device is presented). The planter consists of several parts operating harmoniously to yield the desired results. These parts include a chassis and conveyor belt mechanism, primary and secondary fertilizer tanks, squashing unit, seed metering device and vibrating reservoir of the seed (The main text of modeling the device with SolidWorks software is shown in Fig.2). This device is designed in such a way that the position of the nozzle, the suction pressure, the height of removing seeds and the vibration frequency of the seeding tray are adjustable. Evaluation of the device was carried out by single seeding of tomato seeds in trays with 105 cells (7 × 15). Suction pressure and nozzle size were calculated for tomato seeds. Scaling distances were considered equal, based on the 30.5-mm intervals of the cells. Single seed picking efficiency of seeds was calculated by the system, as the single percentage of seeding and the total percentage of seeded cells. Seed consumption efficiency is the ratio of the total seeded cells to the total number of existing seeds in the cells. Seeding efficiency also refers to single, dual, and multiple harvested seeds. Furthermore, the device capacity is defined as the number of seeded tray cells per hour. In order to design and build a precise robot planter, an experiment including the designed planter and planting speed of workers in 10 repetitions was designed and implemented to estimate the seeding time and compare with automated and manual planting methods. Seedling trays with four replications were cultivated by the designed robot and the number of cultivated seeds per tray at each stage were correctly counted. After that, the spent planting time by a worker was determined with four replications.Results and Discussion: The planting rate of tomato seeds is different when comparing mechanized and manual methods. As it is known, the time required for cultivation in the mechanized method is at least one-tenth of the time required for cultivation in the conventional and manual method, which causes the planting rate to increase, and this robot is one of the components of cultivation in the mechanized method in cultivation and production of tomatoes. By assessing planting time using the mechanized method it was revealed that an average of 26.3 seconds is needed to fill a 7 × 15 centimeter tray of tomato seeds with 105 cells. The same planting procedure in the manual method takes an average of 357 seconds which is indicative of the high rate of the designed device. The planter capacity experimented using a seedling tray with the size of 15×7 cells, was calculated to be 17750 cells per hour showing that the suction pressure increases by a reduction in seed size. Thus, while working with small-sized seeds, fluctuations of the suction pressure must be carefully considered to be minimized and the seed being dual was only affected by the opening diameter. Therefore, the opening diameter should become the same in size in order to minimize the dual seed instances. In case of the tomato, the opening diameter had a great influence on the seeds being bulky.Conclusions: Manual planting takes a considerable time in comparison with the mechanized planting. Furthermore, using the designed device in addition to speeding up the planting process, caused regular and accurate cultivation of tomato seeds in order to produce seedlings. The results indicate that utilizing the device over time is highly economical for the major producers of tomatoes, and it is recommended to be used in agro-industry companies, and in the mechanized method of planting in large scales.
Design and Construction
S. Khalili; B. Mohammadi Alasti; M. Abbasgholipour
Abstract
Introduction: Grading agricultural products always has a particular important position for submission to domestic and overseas markets. The grading causes more profitable product ranges and customer satisfaction. Grading treatment is carried out based on various parameters such as color, ripeness level, ...
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Introduction: Grading agricultural products always has a particular important position for submission to domestic and overseas markets. The grading causes more profitable product ranges and customer satisfaction. Grading treatment is carried out based on various parameters such as color, ripeness level, dimensions and weight. Product weight is one of the most effective parameters in grading operation. Egg weight is directly related to the smallness and coarseness of eggs. In egg grading, the largeness value is very important in marketing. This research aimed to design, fabricate and evaluate the egg weighing system based on its dielectric properties.Materials and Methods: To perform this research, the stages of work are divided into several sections including, design and construction of the hardware section, writing code for the software section to collect data, conducting nondestructive tests and data collection, analysis of obtained data using artificial intelligence, and giving the results of analysis for device calibration of the system as the software code. The large eggs as dielectric substances cause more increase in the capacity of the capacitive sensor. Furthermore, by derivation of a relation between capacity of capacitive sensor and egg weight, one can predict the weight of the sample. A prototype unit of weighing system was designed and fabricated. The designed unit was composed of a chassis, a voltage source, a sinusoidal signal generator, a voltage measurement unit, an AVR micro controller, a COM port, a capacitive sensor, and an LCD and a keyboard. Neural network technique was used for egg weight prediction. The designed net receives 16 voltage values at different frequencies as inputs and its output is the egg weight. In order to calibrate and evaluate the weighing unit, 150 fresh egg samples were provided on egg laying day from a local poultry farm. Experiments were divided into three groups. The experiments were carried out on egg-laying day, and the second and fourth day after laying.Results and Discussion: In this study, two networks were built and evaluated. In the first series, two-layer networks and in the second series, three-layer networks were developed. In the two-layer neural networks, the number of neurons in the hidden layer was changed from 2 to 10.According to the given results for two-layer networks, two layer networks with 10 neurons offer the best results (the highest R-value and minimum RMSE) and it can be chosen as the most effective two-layer network. Three-layer neural networks have been composed of two hidden layers. The number of neurons in the first hidden layer was 10 and in the second layer it was changed from 1 to 20. Between three-layer networks, the network with 7 neurons with the highest R-value and the lowest error is the most appropriate network. It is even more efficient than the two-layer network with 10 neurons. So, the most appropriate structure is 1-7-10-16 and it has been selected for calibration of the weighing device. To evaluate and assess the accuracy of the weighing machine, weights of 24 samples of fresh eggs were predicted and compared with the actual values obtained using a digital scale with the accuracy of 0.01 gr. The paired t-test has been used to compare the measured and predicted values and the Bland-Altman method has been used for charting the accordance between the measured and predicted values. Based on the findings, the difference between the measured and predicted values was observed up to 5.4 gr that is related to a very large sample. The mean absolute error is equal to 2.21 gr and the mean absolute percentage error is equal to 3.75 %. According to the findings, 95% of the actual and approximate matching range to compare the two weighing methods is between -5.3 gr and 3.36 gr. Thus, the dielectric technique may underestimate the egg weight up to 5.3 gr or it may overestimate it up to 3.36 gr more than the actual prediction.Conclusions: The best results were obtained with a 3 layers net having 10 and 7 neurons, respectively in the first and the second hidden layers with the highest R-value, 0.983 and the lowest error, 0.502. Therefore, this net was applied for egg weight prediction. To evaluate the device, the weights of 24 fresh eggs were estimated using the device and were compared with actual values and the maximum error was observed to be equal to 5.4 gr.
Kh. PashaiHulasu; B. Mohammadi Alasti; M. A. Haddad Derafshi; M. Abbasgholipour
Abstract
Introduction: Tractors are considered as the main power generators in mechanized agriculture. Hence, the experts and engineers in tractor manufacturing of the country, are required to focus on developing and designing new features in tractor manufacturing. This must be, of course, paralleled with the ...
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Introduction: Tractors are considered as the main power generators in mechanized agriculture. Hence, the experts and engineers in tractor manufacturing of the country, are required to focus on developing and designing new features in tractor manufacturing. This must be, of course, paralleled with the economic aspects. Achieving this goal, Iran Tractor Manufactories Co., (ITMCO) has designed and developed tractors equipped with turbochargers. This has been performed on ITM800 & ITM485 models, according to world standards. The turbocharger system, with harnessing of lost energy in engine output fumes, compresses the air entering the engine and more air enters the cylinder. This will cause the engine to burn fuel more efficiently and thus produce more power.
Materials and Methods: This study has been carried out on ITM485 & ITM800 tractors (with turbocharger system) and ITM285 & ITM475 tractors (without turbocharger system) to assure the improvement of engine performance and compare them employing OECD world standards. Experiments were performed in the concrete runway of Tabriz Tractor Manufacturing Company. For experiments, a dynamometer was used to measure the traction force between two tractors, a measuring unit for fuel, a thermometer unit and a timer to measure the quantities of fuel consumption, drawbar force and power. For drawbar traction test, each of the tested tractors pulled the rear tractor in different gears and the dynamometer between these 2 tractors recorded the tractors traction force by data loggers. To measure tractors fuel consumption, a measuring unit of fuel (VDO - EDM 1404) was used that calculated the flow rate in the path of fuel from the fuel tank to the engine and the return path from the engine to the fuel tank and showed the quantity of fuel consumption in liters per hour digitally.
Results and Discussion: In comparison of traction power and force of tractors with turbochargers and without turbochargers in different gears, the results of variance analysis showed that the effect of tractor was significant. Traction power and force at tractors with turbochargers ITM485 and ITM800 and without turbocharger ITM475 have a significant difference in the level of one percent. Tukey post hoc test results also indicate that traction power and force in tractors with turbochargers ITM485 and ITM800 are significantly more than the tractor without turbocharger ITM475. The gear effect is also significant. Traction power and force in different gears have significant difference at the probability of one percent. Tukey post hoc test results indicate that power quantity is highest in the gears: (1+H, 2*H, 1*H, 3+L) and minimum in the gears: (1*L, 1+L, 2*L), (* Turtle and + Rabbit). But Tukey post hoc test results indicate that traction force quantity is highest in the gears: (1*L, 2*L, 1+L) and minimum in the gears: (2*H, 1+H). In the comparison of specific fuel consumption of tractors with turbochargers and without turbochargers in different gears, the results of variance analysis showed that the effect of tractor was significant. The amount of specific fuel consumption at tractors with turbochargers ITM485 and ITM800 and without turbocharger ITM475 has a significant difference in the level of one percent. Tukey post hoc test results also indicate that specific fuel consumption quantity in tractors with turbochargers ITM485 and ITM800 in the level of one percent is significantly less than the tractor without turbocharger ITM475. The gear effect is also significant. The specific fuel consumption quantity in different gears has significant difference at the probability of one percent. Tukey post hoc test results indicate that specific fuel consumption quantity is highest in the gears: (1*L, 1+L, 2*L) and minimum in the gears: (1+H, 2*H, 1*H).
Conclusions: The tests were performed on tractor drawbar traction. Results of variance analysis in this experiment on a concrete surface, indicated that the calculated traction power and force of ITM485 and ITM800 tractors (with turbocharger system) were higher than the ITM475 & ITM285 tractors (without turbocharger) and this difference was significant at the one percent level of probability. Meanwhile specific fuel consumption in the ITM485 and ITM800 tractors (with turbocharger system) was lower than that of the ITM475 & ITM285 tractors (without turbocharger) and this difference was significant at the one percent level of probability. This will lead to significant savings in fuel consumption.
