Modeling
M. Dana; P. Ahmadi Moghaddam
Abstract
IntroductionToday, the development of the livestock industry and feed supply is a vital issue due to the growing world population, the importance of animal protein supply, and the growing requirement for livestock products.A porous medium refers to a solid-void (pore) space that is occupied by a fluid ...
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IntroductionToday, the development of the livestock industry and feed supply is a vital issue due to the growing world population, the importance of animal protein supply, and the growing requirement for livestock products.A porous medium refers to a solid-void (pore) space that is occupied by a fluid (gas or liquid). Generally, many of these pores are interconnected which makes the transportation of mass and heat possible through the pores and this contributes to a faster transportation process through the solid matrix. Porosity is the fraction of void space to total volume.While the pores are large enough, water vapor and air in the porous media can be transported by molecular diffusion. Molecular diffusion of a gas species (e.g., vapor) in a gas mixture (e.g., vapor and air) is described by Fick’s law.Materials and MethodsIn this study, the samples were classified into four categories, including control, 3-impacts (low conditioning), 8-impacts (average conditioning), and 13-impacts (high conditioning). Each category included six samples (50-grams) that were used to measure different characteristics at different stages. All samples were weighed every two hours using a digital scale (0.001 gr precision). The leaf-stem separation force then was extracted using a texture analyzer. All experiments were repeated three times, and finally, the mean of these three repetitions was reported as the final value for the intended parameter.The geometry of the alfalfa stem was drawn in Gambit software and after meshing and applying boundary conditions; it was transferred to ANSYS Fluent software. Then, while the solver was selected, adjusted under relaxation factors were applied. In the following, mesh independency was checked and the results were reported.Results and DiscussionTo ensure numerical accuracy, the experimental data should be validated with the simulation results. For this purpose, experimental moisture losses were compared to the software results and showed a good agreement. Then, the moisture ratio curves (kinetics of drying) and force-time chart were presented.The impact of the moisture content of the tissue was evaluated on the value of force per time. Therefore, three samples of alfalfa with different relative humidity in terms of leaf-stem separation force were reported.The results of the numerical simulations were presented as two main contours: the velocity magnitude and moisture (water vapor) mass fraction. The simulation results were provided for all different modes and compared to the experimental data. Finally, errors between both results were presented in a table.ConclusionRegarding the quality and losses of the final product and comparisons between four different modes (control, 3 impacts, 8 impacts, and 13 impacts), the mode with 8 impacts was selected as the best mode.The Force-time chart illustrated two peaks due to the special multi-layer texture of the alfalfa. Regarding reducing the moisture ratio of the alfalfa as compared to the optimal, the force required to separate the leaves from the alfalfa stem was significantly decreased. Also, a significant increase in the losses was observed for impacts modes higher than 8.
O. Ghaderpour; Sh. Rafiee; M. Sharifi
Abstract
Introduction Agricultural productions has been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO2 emissions coming from agriculture. Identification and assessment of environmental impact in the production system will be leading to ...
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Introduction Agricultural productions has been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO2 emissions coming from agriculture. Identification and assessment of environmental impact in the production system will be leading to achieve the goals of sustainable development, which would be achieved by life cycle assessment. To find the relationship between inputs and outputs of a production process, artificial intelligence (AI) has drawn more attention rather than mathematical models to find the relationships between input and output variables by training, and produce results without any prior assumptions. The aims of this study were to life cycle assessment (LCA) of Alfalfa production flow and prediction of GWP (global warming potential) per ha produced alfalfa (kg CO2 eq.(ha alfalfa)-1) with respect to inputs using ANFIS. Materials and Methods The sample size was calculated by using the Cochran method, to be equals 75, then the data were collected from 75 alfalfa farms in Bukan Township in Western Azerbaijan province using face to face questionnaire method. Functional unit and system boundary were determined one hectare of alfalfa and the farm gate, respectively. Inventory data in this study was three parts, included: consumed inputs in the alfalfa production, farm direct emissions from crop production and indirect emissions related to inputs processing stage. Direct Emissions from alfalfa cultivation include emissions to air, water and soil from the field. Data for the production of used inputs and calculation of direct emission were taken from the EcoInvent®3.0 database available in simapro8.2.3.0 software and World Food LCA Database (WFLD). Primary data along with calculated direct emissions were imported into and analyzed with the SimaPro8.2.3.0 software. The impact-evaluation method used was the CML-IA baseline V3.02 / World 2000. Damage assessment is a relatively new step in impact assessment. The purpose of damage assessment is to combine a number of impact category indicators into a damage category (also called area of protection). To assess the damage in this study, IMPACT 2002+ V2.12 / IMPACT 2002+ method was used. ANFIS is a multilayer feed-forward network which is applying to map an input space to an output space using a combination of neural network learning algorithms and fuzzy reasoning. In order to enable a system to deal with cognitive uncertainties in a manner more like humans, neural networks have been engaged with fuzzy logic, creating a new terminology called ‘‘neuro-fuzzy method. An ANFIS is used to map input characteristics to input membership functions (MFs), input MF to a set of if-then rules, rules to a set of output characteristics, output characteristics to output MFs, and the output MFs to a single valued output or a decision associated with the output. The main restriction of the ANFIS model is related to the number of input variables. If ANFIS inputs exceed five, the computational time and rule numbers will increase, so ANFIS will not be able to model output with respect to inputs. In this study, the number of inputs were ten, including machinery, diesel fuel, nitrogen, phosphate, electricity, water for irrigation, labor, pesticides, Manure and seed and GWP was as the model output signal. To solve this problem and employ all input variables, we proposed clustering input parameters to four groups. Correspondingly, the proposed model was developed using seven ANFIS sub-networks. To obtain the best results several modifications were made in the structure of ANFIS networks, and some parameters were calculated to compare the results of different models. Making a comparison between different topologies the employment of some indicators was a pivotal to get a good vision of various the structures, such as the correlation coefficient (R), Mean Square Error (MSE) and Root Mean Square Error (RMSE). In addition, for checking comparison between experimental and modeled data, the t-test was performed. The null hypothesis was equality of data average. To develop ANFIS models, MATLAB software (R2015a) was used. Results and Discussion Impact categories including Global warming potential (GWP), eutrophication potential (EP), human toxicity potential (HTP), terrestrial ecotoxicity potential (TEP), oxidant formation potential (OFP), acidification potential (AP), Abiotic depletion (AD) and Abiotic depletion (fossil fuels) were calculated as 13373 kg CO2 eq, 19.78 kg PO4-2 eq, 2054 kg 1,4-DCB eq, 38.7 kg 1,4-DCB eq, 3.84 kg Ethylene eq, 90.64 kg SO2 eq, 0.015 kg Sb eq and 205169 MJ, respectively. The results of damage assessment of alfalfa production revealed that electricity in three categories, human health damage, climate change and ecosystem quality had maximum role, but in the resources damage category was the largest share of damage related direct emissions. The value of the climate change was calculated as 13373 kg CO2 eq. The best structure was including five ANFIS network in the first layer, two network in the second layer and a network in output layer. Values of R, MSE and RMSE for the final ANFIS in k-fold model were 0.983, 0.107 and 0.327 and in C-means model were 0.999, 0.007 and 0.082, respectively. The p-value in t-test was 0.9987 that indicates non-significant difference between the mean of modeling and experimental data. Coefficient of determination (R2) between actual and predicted GWP based on the best k-fold and C-means models were 0.994 and 0.99, respectively. The coefficient of determination for these index demonstrated the suitability of the developed network for prediction of GWP of alfalfa production in the studied area. Conclusion Based on the results of this study, to reduce the emissions, electricity consumption should be reduced. Adapting of electro pumps power with the well depth and the amount of required water taken for field will be a possible solution to reduce the use of electricity in order to trigger of electro pumps and thus reducing of emissions related to it. In some situations, the type of mineral fertilizer is the main determinant of emissions at the whole farm level and changing the type of fertilizer could significantly reduce the environmental impact. Comparison of GWP modeling results using two methods of k-fold and C-means revealed that C-means method has higher accuracy in prediction of GWP. Also the high quantities for the determination coefficient related to both modeling methods demonstrates high correlation between actual and predicted data.
H. Hooshmand; M. Loghavi
Abstract
The most advanced part of precision agriculture technology is yield monitoring of grain and non-grain crops. In this study, the horizontal pressing force of baling plunger and the angular position of the plunger connecting rod were simultaneously measured by installing a load cell and a shaft encoder ...
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The most advanced part of precision agriculture technology is yield monitoring of grain and non-grain crops. In this study, the horizontal pressing force of baling plunger and the angular position of the plunger connecting rod were simultaneously measured by installing a load cell and a shaft encoder on the connecting rod and plunger flywheel of a small rectangular baler, respectively. The signals of these sensors were processed in an electronic board and the output data were recorded on a portable computer for monitoring and further analysis. Before baling the harvested alfalfa from the test field, random samples were collected and weighted to obtain a referenced measure of the yield variation along the entire field. Comparing the yield data with the pressing energy and angular position data indicated a good correlation between the throughput rate of the baler and the horizontal force imparted on the baler plunger. The estimated crop yield variations were geo-referenced by using a GPS receiver. By combining the output data of the installed sensors and the positioning data, the yield map of the test field was prepared.
M. Maharlooei; M. Loghavi; S. M. Nassiri
Abstract
Precision Agriculture is continuously trying to address the sources and factors affecting the in-field variability and taking appropriate managerial decisions. One of the popular research focuses in the recent three decades has been on the development of new approaches to making yield variability maps. ...
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Precision Agriculture is continuously trying to address the sources and factors affecting the in-field variability and taking appropriate managerial decisions. One of the popular research focuses in the recent three decades has been on the development of new approaches to making yield variability maps. Advancement in development of sensors and the importance of quality factor in high value crops has motivated scientists to investigate real-time and nondestructive testing methods. This study tried to introduce and evaluate a new approach to concurrent yield mapping and to estimate some nutritional qualitative factors of alfalfa production. In this study, yield quantity was determined by measurement of added hay slice in every hay compression cycle by employing a new star wheel and integrating its output with positioning data from Global Positioning System. To predict some nutritional quality properties, measurement of specific shear energy applied on the cutting blade and compressive energy on plunger head of a hay baler in field conditions were also evaluated. The results of statistical analysis of yield quantity measurement data showed a very good correlation between the suggested approach and yield mass (r=0.96 and R2=0.92). The results of using specific shear energy for estimation of crude fiber and cumulative index RFV with regard to field conditions were rated as acceptable. Using specific compression energy was suitable only for estimating the dry matter. None of the suggested methods was able to estimate the hay crude protein. Further investigations at more extensive variations of quality indices and alfalfa varieties are suggested.
