Bioenergy
D. Baveli Bahmaei; Y. Ajabshirchy; Sh. Abdollahpour; S. Abdanan Mehdizadeh
Abstract
This research aims to optimize the mixing process in gas-lift anaerobic digesters of municipal sewage sludge since mixing and maintaining uniform contact between methanogenic bacteria and nutrients is essential. Wastewater municipal sludge sampling was performed at the Ahvaz West treatment plant (Chonibeh, ...
Read More
This research aims to optimize the mixing process in gas-lift anaerobic digesters of municipal sewage sludge since mixing and maintaining uniform contact between methanogenic bacteria and nutrients is essential. Wastewater municipal sludge sampling was performed at the Ahvaz West treatment plant (Chonibeh, Iran) during the summer of 2022. A Computational Fluid Dynamics (CFD) model was implemented to simulate, optimize, and confirm the simulation process using ANSYS Fluent software 19.0. The velocity of the inlet-gas into the digester was determined and a draft tube and a conical hanging baffle were added to the digester design. Different inlet-gas velocities were investigated to optimize the mixing in the digester. Furthermore, turbulence kinetic energy and other evaluation indexes related to the sludge particles such as their velocity, velocity gradient, and eddy viscosity were studied. The optimal inlet-gas velocity was determined to be 0.3 ms-1. The simulation results were validated using the Particle Image Velocimetry (PIV) method and the correlation between CFD and PIV contours was statistically sufficient (98.8% at the bottom corner of the digester’s wall). The results showed that the model used for simulating, optimizing, and verifying the simulation process is valid. It can be recommended for gas-lift anaerobic digesters with the following specifications: cylindrical tank with a height-to-diameter ratio of 1.5, draft tube-to-digester diameter ratio of 0.2, draft tube-to-fluid height ratio of 0.75, the conical hanging baffle distance from the fluid level equal to 0.125 of the fluid height, and its outer diameter-to-digester diameter of 2/3.
Modeling
M. Rahmatian; R. Yeganeh; M. A. Nematollahi
Abstract
IntroductionTillage is a very important operation that influences the growth and productivity of agricultural products. It is necessary to introduce some conditions to improve soil physical properties, aeration, permeability and root development in tillage operations. However, in primary tillage, especially ...
Read More
IntroductionTillage is a very important operation that influences the growth and productivity of agricultural products. It is necessary to introduce some conditions to improve soil physical properties, aeration, permeability and root development in tillage operations. However, in primary tillage, especially when moldboard ploughs are used, this may be time consuming and costly for researchers to use it in their research. Some researchers use physical experiments to perform the work, which the accuracy of the results is dependent on the measuring instruments precision. However, some other researchers use simulation and mathematical modeling to reduce the time and costs and increase the relative accuracy of the research results. Many studies have also shown that modeling the forces involved in tillage is a good way to estimate the performance of different tillage tools and improve their geometry. However, the key to success in numerical simulation of tillage operations is to simulate the exact instrumentation, based on the correct assumptions as well as the proper methods. The prediction of the forces involved in tillage tools has an important role in their design. Collecting data on the forces involved in tillage tool under different farm conditions is a time consuming and costly task. Therefore, the prediction of a tillage tool forces is very important for the designer and the user in order to achieve better performance of the tool. Materials and MethodsIn this study, a cylindrical moldboard made by Alpler Company in Turkey was used to simulate the moldboard. A measuring device was designed and constructed to measure the various points of the desired moldboard. Then, the spatial points obtained by the measuring device were presented to the SolidWorks 2016 software and the desired moldboard was modeled. The finite element method by Abacus 2016 was then used to simulate the interaction between soil and moldboard. Treatments used in simulated tillage operations included tillage depths (5, 10, 15, 20 and 25 cm) and forward speed (1, 1.5, 2, 2.5 and 3 millimeters per second). The independent variables were considered as tensile, vertical and lateral forces (Kilo newton). After simulating the tillage operations, tensile, vertical and lateral forces were obtained. These forces were modeled using response surface and artificial neural networks techniques. Then, the obtained models were compared using R2, RMSE and MRDM statistical indices and the best model was selected. Results and DiscussionWhen using the response surface method, the quadratic model was selected by using the maximum value of the statistical indices R2, R2a and R2p, among the linear, two-factor and quadratic models. Then, the significance of model variables was evaluated by using variance analysis. The forces were also modeled by using the neural network method. According to the fitting curves and statistical indices of R2, RMSE and MRDM for the tensile, vertical and lateral forces, it is revealed that both methods could well predict the forces but artificial neural network was more suitable than the response surface method. Moreover, by investigating the interactions of tillage treatments and forward speed on the forces in this research, it was observed that by increasing the depth of tillage and velocity, tensile, vertical and lateral forces were increased nonlinearly by 66.55%, 68.47%, and 64.76%, respectively. ConclusionRegarding all the results obtained from this study, it can be concluded that the developed models using the artificial neural network in this research was a good and powerful tool for predicting the forces involved in moldboard ploughs both in the field operations and in related studies. It is also recommended that the developed models in this study can be used to manage the tillage operations, such as selecting the proper tractor. However, it is also suggested that other affecting factors, such as moldboard angles, should be included in future models to increase the ability of the model to predict the forces involved in moldboard plows.
S. Mollazehi; H. Sadrnia; M. R. Bayati
Abstract
Introduction In recent decays, the microwave heating treatment is one of the best ways for the pest control. It is difficult to determine temperature in different parts of materials by Thermometer, but we can solve this problem by Comsol Multiphysics Software. In a research, results of a farm test were ...
Read More
Introduction In recent decays, the microwave heating treatment is one of the best ways for the pest control. It is difficult to determine temperature in different parts of materials by Thermometer, but we can solve this problem by Comsol Multiphysics Software. In a research, results of a farm test were consistent with laboratory data and high temperature area was belonged to the outer part of wooden piece (Massa et al., 2015). The numerical simulation of Microwave heating was successfully done for fruits and compared with experimental measurement in two cylindrically and spherically states by Zhao et al. (2011). The results indicated that, the temperature prediction in a wooden piece under heating of a Microwave system was in conformity with experimental infra-red rays data (Rattanadecho, 2006). The outer part of the piece was impressed by inspired heating and the inner part by transmission of heating (Massa et al., 2011). A high frequency structure simulator software, a radiant trumpet shaped antenna with 2.45GHz frequencies, 100 watt electric power were the tools that were used to predict the temperature at a Date Palm Wooden piece at 10, 12, 14 and 16 centimeters (Al Shwear and Remili, 2016). Microwave pretreatment was studied with two factors of Microwave radiation (170, 450, and 850 W) and Microwave duration (2, 6, and 10 min). It can be concluded that the Ozonolysis is the most effective pretreatment regarding to saccharification percentage of sugarcane bagasse (Eqra et al., 2015). This study has been done with the aim of fighting with Rhynchophorus ferrugineus blight by microwave and removing toxins in crops. Materials and Methods Samples features such as physical, mechanical and magnetic once were established in both Tehrans Material and Energy lab and Polymer and Petrochemical Research Center, Then it was simulated by Time_ Temperature profile software. For simulating research by Comsol Multiphysics software, at first sample and chamber sizes were determined and the type of material, meshing, 2.45GHz frequencies and the time duration of heating were measured, respectively. Finally the research was analyzed and Time_Temperature profile which was one of the outcomes of Multiphysics software was determined. A cubic piece of wood (103×86×78 mm) (Fig. 1), a Digital Thermometer and a Microwave are the tools which the researcher used in this sample. The temperature was measured at three different parts of cub diagonal by Thermometer. At first, the wooden sample was divided in two equal parts and a sensor was placed in the middle of it and then it was placed in the Microwave. The primary temperature of sample and Microwaves was 27°C. We turn the Microwave on for a period of 10 minutes, after that we check the wooden piece temperature by Thermometer at 20 seconds intervals. Results and Discussion T-test was used to compare statistical results achieved by simulated and experimental temperature of cubic diagonal. According to T mark at 5 percent level, we can say that there is a significant difference between simulated and experimental temperature at point1, however, there is no such a significant difference at 2 and 3 points. In the following phase, the temperature was compared at two simulated and experimental states by variance analysis test. There was significant difference at 1, 2 and 3 points according to data are shown at figure 4. Moreover, Duncan Post hoc test is shown at figures 5 and 7 that experimental temperature shows no difference at 1 and 3 points but it makes difference at 1, 2 and 2, 3 points. Conclusion Results show that the simulation model can predict the temperature in different parts of a wooden sample. The temperature will be higher as much as the points will be closer to the wave producer resource. In order to control pests in the trunk of a tree, we should use several wave generator systems, instead of ones. It is recommended that cylindrical microwave should be simulated and designed instead cubic ones, because it is better adjusted with tree stock and the wave generator system is placed on this surface so that the temperature will be distributed symmetrically along the diagonal.
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 ...
Read More
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.
H. Roshan; S. J. Razavi; M. Gheysari
Abstract
Water scarcity is today’s world biggest challenge which requires different countries to manage their water resources in the most efficient way. Sprinkler irrigation increases water consumption efficiency due to more uniform distribution of water across the field. Precision farming is based on the ...
Read More
Water scarcity is today’s world biggest challenge which requires different countries to manage their water resources in the most efficient way. Sprinkler irrigation increases water consumption efficiency due to more uniform distribution of water across the field. Precision farming is based on the site-specific use of inputs according to soil characteristics and plant needs. One of the main inputs for agricultural production is water. Thus, efficient use of water resources based on variable rate irrigation is considered to be a basic approach of precision irrigation. The main purpose of this study was to simulate and fabricate a variable flow sprinkler, applicable in solid set sprinkler irrigation system. The preliminary drawing of the proposed sprinkler, which equipped with a flow and pressure control plunger, was simulated using Fluent software. The actual sprinkler was then fabricated and evaluated in a field. The performance of the sprinkler was evaluated at three pressure levels, three plunger positions (at the points of the least and biggest sprinkler’s cross section for water passage) and three diameters of outlet nozzle opening. Results showed that the plunger had the capability of varying outlet flow and pressure in the sprinkler and trends in flow and pressure variation as affected by the plunger position was very complicated. The Fluent model for conditions with fully open of the plunger and half opened was effectively efficient. However, as the plunger closed the water passage more than the half of cross section, the model did not show an acceptable efficiency.
