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%.
Agricultural waste management
H. Zaki Dizaji; N. Monjezi
Abstract
Introduction No use of advanced mechanization and weakness in post harvesting technology are the main reasons of agricultural losses. Some of these wastes (agricultural losses) are related to crop growing conditions in field and the remaining to processing of sugar in mill. The most useful priority setting ...
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Introduction No use of advanced mechanization and weakness in post harvesting technology are the main reasons of agricultural losses. Some of these wastes (agricultural losses) are related to crop growing conditions in field and the remaining to processing of sugar in mill. The most useful priority setting methods for agricultural projects are the Analytic Hierarchy Process (AHP). So, this study presents an introduction of application manner of the AHP as a mostly common method of setting agricultural projects priorities. The purpose of this work is studying the sugarcane loss during production process using AHP in Khuzestan province. Materials and Methods The resources of sugarcane waste have been defined based on expert’s opinions. A questionnaire and personal interviews have formed the basis of this research. The study was applied to a panel of qualified informants made up of thirty-two experts. Those interviewed were distributed in Sugarcane Development and By-products Company in 2015-2016. Then, with using the analytical hierarchy process, a questionnaire was designed for defining the weight and importance of parameters effecting on sugarcane waste. For this method of evaluation, three main criteria considered, were yield criteria, cost criteria and income criteria. Criteria and prioritizing of them was done by questionnaire and interview with sophisticated experts. This technique determined and ranked the importance of sugarcane waste resources based on attributing relative weights to factors with respect to comments provided in the questionnaires. Analytical Hierarchy Process was done by using of software (Expert choice) and the inconsistency rate on expert judgments was investigated. Results and Discussion How to use agricultural implements and machinery during planting and harvesting of sugarcane, can increase or decrease the volume of waste. In planting period, the losses mainly consists of loss of setts during cutting them by machine, injury the setts by biological and physical agents, loss of growth in sett field, unsuitable sett covering and replanting the gaps. During cultivation period the losses include late in field harvesting and so late in regrows the cane, unsuitable ratooning and use of cultivator, varying the size of the furrows and ricks in around the field and destroyed the stubbles during rationing. In harvesting the losses easily seen and mainly associated by efficiency of harvester machines. Billets loss of the fleet in the transmission roads toward mill and late in harvest the burnet cane and then transport to mill are main sources of quantities and qualities of losses. The Expert Choice software performed well in conjunction with the panel of experts for choosing the criteria and assigning weights under the AHP methodology. According to the results, effective parameters on sugarcane waste consist of caused by harvesting, transportation, industry, planting, preserve operations, ratooning and land preparation. Weight of effective criteria (yield, cost and income) on losses of sugarcane obtained from paired comparison in the experts’ view which has been calculated with Expert choice software. The result of this survey by AHP techniques showed that yield criteria had the most and income criteria had the least importance for expert in sugarcane production. In this stage of research, alternatives of paired comparison relative to criteria was separately formed and information of questionnaire which relates to paired comparison of criteria was obtained. Between effective parameters on losses of sugarcane, harvesting with 0.243 weighted average was the most effective factor and transportation with 0.187 weighted average, industry with 0.179 weighted average, planting with 0.156 weighted average, preserve operations with 0.109 weighted average, ratooning with 0.071 weighted average, and land preparation with 0.055 weighted average was later, respectively (Inconsistence Rate =0.04). The results are examined by monitoring sensitivity analysis while changing the criteria priorities. Since different judgments are made on comparison of criteria, we use sensitivity analysis in order to provide stability and consistence of analysis. With increasing or decreasing of the criteria, we will conclude that ratio of other indices will not change. Conclusion This paper looks at AHP as a tool used in Sugarcane Agro-Industries to help in decision making. Results show that criteria studied in this research can help prioritizing of loss resources during sugarcane production process. According to the results, effective parameters on sugarcane waste consist of caused by harvesting, transportation, industry, planting, preserve operations, ratooning and land preparation.
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.
