Post-harvest technologies
J. Abdi; A. Golmohammadi; Gh. Shahgholi; A. Rezvanivand fanaei
Abstract
IntroductionPeanut (Arachis hypogaea L.) is an annual plant of the legume genus that is cultivated in 109 countries due to its high-quality oil and seed protein. In Iran, this crop is cultivated on an area of 3000 hectares, with an average yield of 4 tons per hectare. Threshing performance significantly ...
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IntroductionPeanut (Arachis hypogaea L.) is an annual plant of the legume genus that is cultivated in 109 countries due to its high-quality oil and seed protein. In Iran, this crop is cultivated on an area of 3000 hectares, with an average yield of 4 tons per hectare. Threshing performance significantly affects seed loss and physical damage, including cracking and crushing of seeds during harvest. Therefore, over the last century, extensive research has been conducted on different types of threshing methods, as well as the design and development of various threshing machines.Research on seed crops such as cereals and seeds suggest that factors such as the rotational speed of the thresher, threshing-concave distance, feeding rate, and shape of threshing teeth play a crucial role in determining the threshing efficiency and quality of the threshed seeds. Although limited research has been conducted on peanut threshing, there are currently no combine-machines available for this crop on global markets. Therefore, this study aims to investigate several working parameters of an experimental peanut thresher, including the effect of sieve angle, sieve range of movement, and suction speed on the separation unit.Materials and MethodsThe relevant experiments were conducted in the Parsabad Moghan region of Ardabil province (latitude 39.65 North, longitude 47.91 East). To conduct the experiments and separate the seeds from the pods, we used a peanut threshing machine cultivar Nc2, which is commonly cultivated under agricultural conditions in Ardabil and Gilan Agricultural Research Centers.To achieve the aims of this research, we investigated several effective parameters in the performance of the machine, including sieve angle, sieve movement range, and fan suction speed, to obtain the best settings for maximum threshing performance and separation efficiency. It is worth noting that the average seed weight per kilogram of peanut plant was between 300-400 grams, and the moisture content of the seeds in the tested cultivar was 45%. Before using the machine, workers must first dig up the plants and place them on the ground in a coupe, after which another worker must feed the plants into the machine through the feeder.Results and DiscussionThe study found that changes in sieve angle, sieve movement range, and suction speed significantly affect the separation efficiency and peanut loss rate at a 1% significance level. Increasing the sieving angle leads to a higher speed of material movement on the sieve, which results in insufficient time for separating straw from the seed. Similarly, increasing the sieve movement range causes a rapid decrease in cleaning efficiency. To achieve better straw-seed separation, it is necessary to apply impact shocks to the products located on the sieve within a short period. However, as the range of movement increases, the time interval between impact shocks also increases, which disrupts the straw's separation from the seed.The study found that increasing the sieve range and suction speed leads to a higher rate of peanut loss. This is due to the fact that when the suction speed and sieve movement range are increased, the product spends less time on the sieve, which results in insufficient time for proper separation. Additionally, high speed may exceed the limit of peanut seed and cause it to move out of the machine with the straw. Increasing the sieve movement range leads to a more uniform movement of straw and seed on the sieve; however, achieving better separation of straw from the sieve requires dynamic shocks and sudden acceleration, which decreases as the sieve movement range increases. The optimal farm capacity and material capacity were achieved with a 5-degree slope at 0.55 hectares per hour and 509 kilograms per hectare, respectively, using a sieve range of 3.5 centimeters and a fan suction speed of 8 meters per second.ConclusionThe study concluded that the sieve movement range has the most significant impact on cleaning efficiency, while the sieve angle has the least effect. Similarly, the sieve movement range has the most significant influence on the rate of peanut loss, while the sieve angle has the least effect.
S. Naimei Dizajyekan; Gh. Shahgholi; A. Rezvanivand fanaei; V. Rostampour
Abstract
IntroductionCyclones are widely used to separate solid particles from the fluid phase. Due to the ease of construction, low running costs, and hard-working conditions at high temperatures, people's interest in using cyclones is increasing day by day. Engineers are generally interested in two parameters ...
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IntroductionCyclones are widely used to separate solid particles from the fluid phase. Due to the ease of construction, low running costs, and hard-working conditions at high temperatures, people's interest in using cyclones is increasing day by day. Engineers are generally interested in two parameters to perform a complete evaluation of the design and operation of a cyclone. These parameters are the particle collecting efficiency and the pressure drop inside the cyclone. The precise prediction of the pressure drop in cyclone is very important which it is directly related to operating costs.Computational Fluid Dynamics (CFD) is a diversified tool for predicting flow behavior in a wide range of design and operational conditions. Numerical solution of Navier-Stokes equations is the basis of all CFD techniques, which is the result of fast computer upgrades and a better understanding of the numerical resolution of turbulence.Materials and MethodsRegarding preliminary experimental tests and understanding the fluid flow, the flow rate of 0.08 kg s-1 was selected as the flow rate. Six levels of inlet velocities 10, 12, 14, 16, 18, and 20 m s-1 were selected for understanding the effect of inlet velocity on the cyclone performance. The measurements were carried out using a hot-air anemometer (TSI-8484model with a resolution of 0.07 m s-1 and an operating range of 0.125 to 50 m s-1), and a pressure differential meter instrument (CPE310s-KIMO model) with an accuracy of 0.1 Pa.The region is discretized as a finite volume in a set, called the region grid or mesh after discretization. For incompressible fluids, pressure-based and density-based solvers are used, respectively. Regarding the velocity of the material entering the cyclone and low Mach number, a pressure-based solver could be used in this study.The shear stress transport model (SST) is a modified version of the k-ω 2-equation model. This model combines the two turbulence k-ω and k-ε models. The Lagrangian discrete phase model in Ansys Fluent follows to the Euler-Lagrangian model.Defining the best type of boundary condition is important for solving the problem and extracting solving fields. The boundary conditions used in this study include the inlet velocity in the entrance of cyclone and output pressure in both the upper and lower output sections.Results and DiscussionIn the results section, the results are initially validated by experimental results. Then, the parameters relating to separation efficiency and pressure drop are discussed. Finally, the tangential and axial velocities are considered as important parameters in the cyclone performance.One of the important issues in the cyclones is the static pressure because it completely affects the phenomenon of separation in the cyclone. The velocities of 16 m s-1 and 18 m s-1 have a good potential for use as the base velocity of the inlet fluid to the cyclone. The velocity of 20 m s-1 is not suitable for separation due to high-pressure drop related to high static pressure.The separation efficiency in the cyclone was 92 to 99% at all levels, the highest separation efficiency of 99% occurred at the velocity of 16 m s-1 and the lowest separation efficiency of 9% happened at the velocity of 20 m s-1.An increasing trend in axial and radial velocities occurred and the highest tangential velocity occurring in the input section. Considering the working conditions, the inlet velocities of 10 m s-1 to 16 m s-1 are appropriate for the turbulence intensity viewpoint.Conclusion(1): The speeds 16 m s-1 and 18 m s-1 showed a good potential for use as a base velocity of the fluid to the cyclone.(2): The highest separation efficiency for the velocity of 16 m s-1 (99%) and lower isolation efficiency was obtained at velocity of 20 m s-1 (92%).(3): The velocities of 10 m s-1 to 16 m s-1 are suitable input rates from the point of view of turbulence intensity.(4): It is concluded that from the point of view of wear to the velocity of 10 to 16 m s-1, practical use is possible, and the velocity of 18 m s-1 and 20 m s-1 require the reinforcement of the relevant sections.
T. Mesri Gundoshmian; F. Keyhani Nasab; Gh. Shahgholi; E. Abdollahi
Abstract
Introduction Today, most of the agricultural machines for doing agricultural operations and covering the entire farm, must move in the farm, and travel a certain distance without doing anything useful. Common agricultural machines are controlled by human beings using habits, machinery models, and personal ...
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Introduction Today, most of the agricultural machines for doing agricultural operations and covering the entire farm, must move in the farm, and travel a certain distance without doing anything useful. Common agricultural machines are controlled by human beings using habits, machinery models, and personal experiences without using computer-based tools. This trend leads to repetitive patterns and affect farm efficincy. Therefore, applying optimization techniques in determining the optimum pattern and track for on-farm machinery would be very effective. One of the main problems of conventional movement patterns on farms is the time wasted on moving towards the end of the field, which will have a big impact on field efficiency. The purpose of this study is to reduce the useless distance traveled by agricultural machines using genetic algorithm while moving on the farm and going from one track to the next, and, consequently, increase farm efficiency. Materials and Methods In this study, the rectangle farm that was 80 meters wide and had an arbitrary length was selected for simulation, and different types of turning methods were tested. The calculations and simulation were based on genetic algorithm using the MATLAB 2013 software. In this case, the minimum traveled distance was set as solution evaluation criterion. The solutions were applied and simulated according to these assumptions: Each gene was considered a track number, and the algorithm’s chromosomes were produced by connecting all the tracks to each other,. The width of each track was considered equal to the width of the machine, and based on reproduction parameters such as population size and the number of repetitions, a percentage of the children were produced through point intersection and another percentage were produced through mutation. In determining the distance between the tracks, Ω or T or U were used for two adjacent tracks, U was used for two tracks that had a track between them, and a longer U was used for tracks that had more than one track between them. Based on the number of the initial population, the chromosomes that were supposed to be parents at the beginning were selected. The children produced new population was created and the above steps were repeated. During the last repetition, the best child chromosome was introduced as the answer. In order to calculate the effects of different methods of turning on the non-working distance covered during agricultural operations, the non-working distance traveled during 5 orders of movement, including 4 traditional orders (continuous, spiral, all-around, and blocked) and 1 smart order were compared to each other. In the continuous pattern, because movement continues in the next track at the end of each track, all the turnings are inevitably done in the Ω way, and thus a greater distance is travelled compared to the U way. In the spiral pattern, the distance travelled in turnings between different tracks on the farm is equal. In the all-around pattern, movements are done from the sides and the operation is concluded at the center of the farm; therefore, the long U method of movement is used at the end of all the tracks, and Ω turning is used for the last track at the center of the farm. In the blocked pattern, the farm is devided into two or more blocks, and the all-around movement pattern is used in each block as an independent farm. In the smart movement pattern, the beginning and ending of the agricultural operations are considered in the vicinity of the hypothetical road which, in addition to facilitating access to the road, had a significant impact on reducing the useless distance traveled on the farm. Results and Discussion The final optimum pattern was compared to traditional patterns in the form of charts. The optimum pattern of movement which uses smart genetic algorithm and avoids long turning methods (such as, Ω and T) leads to reduced wasted time and distance traveled by agricultural machines and increased field efficiency and also, decreasing the non-working traveled distance and waste time approximately, 45 % and 47 % respectively. This is due to avoiding turning methods of Ω and T (compared to the U method). Also, the fatigue resulting from these approaches and their wasted time is greater than U method used in the genetic algorithm pattern. Conclusion The optimum pattern of movement which uses smart genetic algorithm was compared to conventional patterns that showed significant saving in non- working distance and waste time in farm. This optimum pattern can be implemented in automatic navigation but there is the possibility of its implementation by operators in common navigation.
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.
