Modeling
Z. Zibahoosh; J. Khodaei; S. Zareei
Abstract
IntroductionThe most costly part of poultry breeding is feeding. Due to the noticeable developments in animal husbandry and agricultural sectors, it is necessary to use the mechanized methods to reduce the casualties, increase the productivity as well as reduce the time and cost in each of these sectors. ...
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IntroductionThe most costly part of poultry breeding is feeding. Due to the noticeable developments in animal husbandry and agricultural sectors, it is necessary to use the mechanized methods to reduce the casualties, increase the productivity as well as reduce the time and cost in each of these sectors. Reducing the particle size is one of the ways to process cereals which improves the mixing and also the nutritional value of the feed and the quality of the pellet feed. Optimizing the performance of hammer mill with the aim of reducing the size of different materials for poultry feed, would be very beneficial for obtaining the minimum cost of food, maximum quality and capacity. The main objective of this research was to optimize the operational variables, including sieve size, grain moisture content, feed rate and the number of hammers, each of them at three levels, on a hammer mill during the process of poultry food production from wheat, corn, barley and soybean grains. Materials and MethodsThe seeds used in experiments were wheat (Azar2 variety), corn (Brazilian variety), soybean (Danpars variety) and barley (Aras variety). A laboratory hammer mill was used to perform experiments. The treatments including sieve diameter (2, 2.3and 4.4 mm), grain moisture content (10, 14 and 18%), seed input rate to milling compartments (one-third, two-thirds and fully openness of tank gate) and the number of hammer (12, 18 and 24) were investigated. In order to measure the working capacity of the hammer mill, the required time for milling was recorded. The amount of final milled crop in each experiment was weighed and divided into the needed time for milling. Sieve analysis was used to determine the distribution and dispersion of the milled material which works according to the standard of ASTM E-11-70 Part 41 (Anonymous, 2004). In this study, the effects of input variables were investigated using the response surface method focusing on the central composite design approach to optimize the fineness degree and working capacity of the mill. The Design Expert 8.0.6 software was applied for statistical analysis, modeling and optimization. Results and DiscussionThe results indicated that sieve size and the number of hammers have been affected by the fineness degree of wheat grains, significantly. In addition, all four factors and interaction effects between sieve size and moisture content and also moisture content and number of hammers influential working capacity at the significant level of 1%. In the case of corn, the influence of moisture content and its interaction with sieve size on grain fineness, and the effect of sieve size, moisture content, feed rate and interactions between sieve size and moisture content and moisture content and feed rate of working capacity were significant at the level of 1%. For barley, moisture content at the level of 1% and interaction between sieve size and moisture content at the probability level of 5% were effective on barley fineness degree. Meanwhile, the moisture content at the level of 1% and sieve size and its interaction with moisture content at the level of 5% influenced working capacity, significantly. Soybeans were not able to respond the required moisture level for the experiments due to their soft and brittle texture, whereas unreliable results were obtained by changing its moisture levels. The best size of sieve holes, grain moisture content, feed rate and the number of hammers were determined to minimize the fineness degree and maximize the working capacity of the hammer mill. ConclusionIn this research, the response surface method considering a central composite design was used to optimize the operational variables of a hammer mill, including sieve hole size, grain moisture, feed rate and the number of hammer to produce poultry feed with the aim of achieving a minimum fineness degree (more grain crushing) and maximum milling capacity. The results of variance analysis were presented for wheat, corn, barley and soybean. Regression models could represent the relationship between the independent variables and the outputs with high confidence coefficient, and the best values of input variables were determined to optimize grinding operation.
Modeling
M. Mehrijani; J. Khodaei; S. Zareei
Abstract
Introduction Tillage as a preliminary step for agricultural production consumes large amounts of energy. Regarding the energy crisis and the greenhouse gas emissions caused by the indiscriminate use of fossil fuels, many efforts have been done to reduce energy consumption as much as possible. About half ...
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Introduction Tillage as a preliminary step for agricultural production consumes large amounts of energy. Regarding the energy crisis and the greenhouse gas emissions caused by the indiscriminate use of fossil fuels, many efforts have been done to reduce energy consumption as much as possible. About half of the energy used in the crop production has been dedicated to tillage operations; hence the optimization of tillage tools performance can lead to decrease the energy loss. Tillage operation in most regions of Iran is carried out by moldboard plow. The ability of this plow in turning the soil has made it impressively different from the other plows. The energy used in tillage operations depends on various factors such as soil type and its conditions (soil moisture and texture), plow depth and forward speed. The aim of this study is to investigate the effect of forward speed, plow depth and soil moisture on fuel consumption and required tensile force during tillage operation with a moldboard plow which uses three plows in clay soil. Materials and Methods The current study was carried out to optimize the tillage operation with a moldboard plow in the clay soil. Tillage experiments were performed to evaluate the effect of forward speed, plow depth and soil moisture content on the required tensile force and tractor fuel consumption. A moldboard plow with three single-sided plows was used to conduct experiments. Two tractors (MF285 and U650) and a dynamometer were used to measure the required tensile force. To measure the fuel consumption of the tractor during operation, the fuel level was measured in a separate tank system installed on the tractor's fuel system. Experiments were carried out using response surface method and central composite design (CCD) by taking three levels of forward speed (4, 5 and 6 kmh-1), three plow depth (20, 25 and 30 cm) and three levels of soil moisture content (12, 16 and 20%). Design Expert 8.0.6 software was used to analyze the experimental data. Results and Discussion The result of the analysis of variance showed that the effects of plow depth, forward speed and soil moisture, as well as the interaction between forward speed and moisture content on the fuel consumption during tillage operations with moldboard plow are significant. The results also indicated that the increase in forward speed decreased the fuel consumption. Also, fuel consumption decreased with increasing in moisture content at first, but then increased. The reason for this was probably because of the increased strength of soil particles due to the reduced moisture content (the stronger coherence force between the particles), which required more energy to shear the soil. According to the results of analysis of variance, it can be concluded that all three factors of forward speed, plow depth and soil moisture had a significant effect on the required tensile force of moldboard plow at %1 probability level. With increasing the plow depth and forward speed, required tensile force increased significantly. The dependent variables were modeled as second order regression equations and optimal values of independent variables were determined. Optimum performance with maximum desirability was determined at forward speed of 5.08 kmh-1, plow depth of 20 cm and soil moisture content of 16.41%. Conclusion With increasing plow depth, tensile force and fuel consumption increased. Also, tensile force increased with increasing forward speed, but this increase was not severely affected by the plow depth and reduced the fuel consumption. The quadratic regression models can well predict the required tensile force and fuel consumption. Using response surface method, optimum performance was determined at forward speed of 5.08 kmh-1, plow depth of 20 cm and soil moisture content of 16.41%.
S. Zareei; Sh. Abdollahpour
Abstract
Introduction
The noticeable proportion of producing wheat losses occur during production and consumption steps and the loss due to harvesting with combine harvester is regarded as one of the main factors. A grain combines harvester consists of different sets of equipment and one of the most important ...
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Introduction
The noticeable proportion of producing wheat losses occur during production and consumption steps and the loss due to harvesting with combine harvester is regarded as one of the main factors. A grain combines harvester consists of different sets of equipment and one of the most important parts is the header which comprises more than 50% of the entire harvesting losses.
Some researchers have presented regression equation to estimate grain loss of combine harvester. The results of their study indicated that grain moisture content, reel index, cutter bar speed, service life of cutter bar, tine spacing, tine clearance over cutter bar, stem length were the major parameters affecting the losses.
On the other hand, there are several researchswhich have used the variety of artificial intelligence methods in the different aspects of combine harvester.
In neuro-fuzzy control systems, membership functions and if-then rules were defined through neural networks. Sugeno- type fuzzy inference model was applied to generate fuzzy rules from a given input-output data set due to its less time-consuming and mathematically tractable defuzzification operation for sample data-based fuzzy modeling. In this study, neuro-fuzzy model was applied to develop forecasting models which can predict the combine header loss for each set of the header parameter adjustments related to site-specific information and therefore can minimize the header loss.
Materials and Methods
The field experiment was conducted during the harvesting season of 2011 at the research station of the Faulty of Agriculture, Shiraz University, Shiraz, Iran. The wheat field (CV. Shiraz) was harvested with a Claas Lexion-510 combine harvester. The factors which were selected as main factors influenced the header performance were three levels of reel index (RI) (forward speed of combine harvester divided by peripheral speed of reel) (1, 1.2, 1.5), three levels of cutting height (CH)(25, 30, 35 cm), three levels of the horizontal distance of reel tine bar from cutter bar (Hd)( 0, 5, 10 cm) and three levels of vertical distance of reel tine bar from cutter bar (Vd)( 5, 10, 15 cm) which are taken as the input variables for neuro-fuzzy model and only combine header loss is output of the model.
Some frames with the dimensions of 50 × 50 cm2 were randomly used to determine the amount of header loss. In order to determine the header loss, the frame was placed on the ground in the vacant place behind the cutter bar, where output material from the back of the combine was not allowed to pour on the ground. Grains and ears found inside the frame were gathered, weighed and then the amount of pre-harvest loss was subtracted from it. A fractional factorial design based on a completely randomized design was used to determine the header loss. Each test was repeated three times and for each repetition.
The structure of neuro- fuzzy model for this study has four inputs and each input variable as mentioned and three Gaussian membership functions (mf), result in 81 rules.
Results and Discussion
A neuro- fuzzy model was developed for predicting the combine header loss based on reel index, cutting height, the horizontal distance of reel tine bar from the cutter bar and vertical distance of reel tine bar from cutter bar as input variables. The Model has three membership functions for each input. Gaussian membership functions and rules were defined for knowledge representation of header loss.
Predicting header loss is an important issue for minimizing the amount of harvest grain losses. Neuro-fuzzy model presented a satisfactory application to describe header loss of a combine harvester. It showed R² equal to 0.95 which is superior to multiple regression method with 0.71. In fact, the amount of coefficient of determination is a good indicator to check the prediction performance of the model. Based on developed neuro-fuzzy system model, levels of reel index, cutting height, the horizontal distance of reel tine bar from cutter bar and vertical distance of reel tine bar from cutter bar could be recommended according to minimize header loss.
Conclusions
In the final step, the designed controller was simulated in SIMULINK. The Controller can change setting of header components in order to their impaction gathering loss and in each step, compare gathering loss with optimal value and If it was more than optimum then change the settings again. The simulation results were evaluated satisfactory.
