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.
Image Processing
M. Nadafzadeh; A. Banakar; S. Abdanan Mehdizadeh; M. R. Zare-Bavani; S. Minaei
Abstract
IntroductionNowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized ...
Read More
IntroductionNowadays, machine vision systems are extensively used in agriculture. The application of this technology in the field can help preserve agricultural resources while reducing manual labor and production costs. In the field of agricultural automation, accurately detecting crop rows is recognized as a crucial and challenging issue for weed identification and the automatic guidance of machines. Therefore, it is necessary to explore practical solutions to optimize this process. Hence, the purpose of this study is the precise identification of basil cultivation rows to enable the automatic navigation of robots in the cultivation field.Materials and MethodsIn the first stage of this research, six images from each growth period of basil plants (third, fourth, and fifth week) were taken and weeds were removed from the area between the crop rows using three different methods of area opening, dimensional removal, and masking. In the next stage, six images of crop rows without weeds were examined by performing image processing operations and implementing several routing algorithms, namely, Hough transform, wavelet transform, Gabor filter, linear regression, and an additional algorithm proposed in this study. The output of each of these algorithms was compared with the ideal path identified by the user. For this purpose, after capturing an image, green areas were extracted from it by performing the segmentation process. By applying each of the routing algorithms to the image, plant cultivation lines were identified and their equations were determined. Finally, the performance of the designed robot was evaluated using the most appropriate routing algorithm.Results and DiscussionExamining the performance of three different methods of weed removal in three periods of plant growth (third, fourth, and fifth week) showed that during this interval, the masking method had the lowest error rate compared to the ideal path and the shortest average operation time of 1.64 seconds, followed by the dimensional removal and the area opening methods. Comparing the routes detected by different routing algorithms with the ideal routes and according to the results of the t-test at 5% probability level, the order of the studied routing methods from the most superior is as follows: the proposed algorithm, Gabor filter, linear regression, Hough transform and wavelet transform algorithm. Overall, the proposed algorithm had the highest rate of adaptation to the ideal path (with an average error of 3.65 pixels) and the shortest operation time (4.79 seconds) and was selected as the most appropriate routing algorithm and the performance of the designed robot was evaluated using it.ConclusionA reliable crop row detection algorithm can reduce production costs and preserve the environment. In this study, the masking method was used for removing weeds from the images. The new proposed routing algorithm has superior performance when compared with common routing algorithms such as the Gabor filter, linear regression, Hough transform, and wavelet transform. Additionally, it was shown that the designed robot using the proposed algorithm (with an average error of 3.65 pixels) has the desired performance.AcknowledgmentThe authors express appreciation for the financial support provided by Tarbiat Modares University.
Image Processing
S. Abdanan Mehdizadeh
Abstract
IntroductionAdopting new technologies for crop growth has the characteristics of improving disaster resistance and stress tolerance, ensuring stable yields, and improving product quality. Currently, the cultivation of seed trays relies on huge labor power, and further mechanization is needed to increase ...
Read More
IntroductionAdopting new technologies for crop growth has the characteristics of improving disaster resistance and stress tolerance, ensuring stable yields, and improving product quality. Currently, the cultivation of seed trays relies on huge labor power, and further mechanization is needed to increase production. However, there are some problems in this operation, such as the difficulty of improving the speed of a single machine, seedling deficiency detection, automatic planting, and controlling the quality, which need to be solved urgently. To solve these problems, there are already some meaningful attempts. Si et al. (2012) applied a photoelectric sensor to a vegetable transplanter, which can measure the distance between seedlings and the movement speed of seedlings in a seedling guide tube, to prevent omission transplantation. Yang et al. (2018) designed a seedling separation device with reciprocating movement of the seedling cup for rice transplanting. Tests show that the structure of the mechanical parts of the seedling separation device meets the requirements of seed movement. The optimization of the control system can improve the positioning accuracy according to requirements and achieve the purpose of automatic seedling division. Chen et al. (2020) designed and tested of soft-pot-tray automatic embedding system for a light-economical pot seedling nursery machine. The experimental results showed that the embedded-hard-tray automatic lowering mechanism was reliable and stable as the tray placement success rate was greater than 99%. The successful tray embedding rate was 100% and the seed exposure rate was less than 1% with a linear velocity of the conveyor belt of 0.92 m s-1. The experiment findings agreed well with the analytical results.Despite the sharp decline in Iran's water resources and growing population, the need to produce food and agricultural products is greater than ever. In the past, most seeds were planted directly into the soil, and many water resources, especially groundwater, were used for direct seed sowing and plant germination. One way to reduce the consumption of water, fertilizers, and pesticides is to plant seedlings instead of direct seed sowing. Therefore, the purpose of this study was dynamic modeling and fabrication of seed planting systems in seedling trays.Material and MethodsIn this experiment, Flores sugar beet seeds (Maribo company, Denmark) were used. The seedling trays had dimensions of 29.5*60 cm with openings and holes of 5.5 and 4 cm, respectively. To plant seeds in seedling trays, first, a planter arm was modeled and its position was obtained at any time. Then, based on dynamic modeling, the arm was constructed and a capacitive proximity sensor (CR30-15AC, China) and IR infrared proximity sensor (E18-D80NK, China) were used to find the location of seedling trays on the input conveyor and position of discharging arm, respectively. To achieve a stable and effective control system, a micro-controller-based circuit was developed to signal the planting system. The seed planting operation was performed in the seedling tray according to the coordinates which were provided through the image processing method. The planting system was evaluated at two levels of forward speed (5 and 10 cm s-1). Moreover, a smartphone program was implemented to monitor the operation of the planting system.Results and DiscussionThe planting system was assessed for sugar beet seeds using two levels of forward speed (5 and 10 cm s-1). The nominal capacity of this planter ranged from 3579 to 4613 cells per hour, with a miss and multiple implantation indices of 0.03% and 8.17%, respectively, in 3000 cells. Due to its planting accuracy, speed, and low energy consumption (25.56 watt-hours), this system has the potential to replace manual seeding in seedling trays.ConclusionIn the present study, a seed-sowing system for planting seedling trays was designed, constructed, and evaluated based on dynamic modeling. In the developed system, unlike previous research, planting location detection was conducted through image processing. Additionally, a smartphone program was established to monitor the operation of the planting system without interfering with its performance. This study demonstrates that image processing can successfully detect planting locations and can effectively improve efficiency over time for major producers.
M. Malek mohammadi; M. Rahnama; S. Abdanan Mehdizadeh; N. Kazemi
Abstract
Introduction Due to the rapid growth in the urban population, the numbers of cars also have increased which resulted in an increase of pollution level in the urban areas of the developing countries. The pollutants emerging from combustion engines may include: carbon monoxide (CO), unburned hydrocarbons ...
Read More
Introduction Due to the rapid growth in the urban population, the numbers of cars also have increased which resulted in an increase of pollution level in the urban areas of the developing countries. The pollutants emerging from combustion engines may include: carbon monoxide (CO), unburned hydrocarbons (UBHC), oxide of nitrogen (NOx), oxides of sulfur (SOx), particulate matter (PM), soot, hydrogen, oxygen, traces of aldehydes, alcohols, ketons, phenols, acid, lead aerosol, etc., along with normal combustion products i.e. carbon dioxide (CO2) and water vapors. In order to overcome the problems associated with the bio-fuel, the chemical substances like fuel additives derived from organic, inorganic metals were used. Fuel additives generally improve the combustion efficiency and reduce the pollution. Metallic based compounds, such as manganese, iron, copper, barium, calcium and platinum, etc., which have been used as a combustion catalyst for hydrocarbon fuels. Recent advances in nanoscience and nanotechnology enables production, control and characterization of nanoscale energetic materials. Nano materials are more effective than bulk materials because of its higher surface area. Another important advantage of nanoparticle is its size, because there is no chance for fuel injector and filter clogging as in the case of micron sized particles. Gan and Qiao, (2011) investigated the burning characteristics of fuel droplets containing nano and micron sized aluminum (Al) particles by varying its size, surfactant concentration and type of base fluid. Tyagi et al. (2008) conducted a study to improve the ignition properties of diesel fuel and investigated the influence of size and quantity of Al and Al2O3 nanoparticles in a diesel fuel. It was inferred that it shortens the ignition delay and increased the ignition probability of fuel. Finally, it was concluded that, the increase in heat and mass transfer properties of the fuel has the potential of reducing the evaporation time of droplets. In the present investigation, the effect of mixture of ethanol with gasoline and carbon nanotubes on emission characteristics was evaluated using Jatropha biodiesel in a compression in a spark ignition engine.Materials and MethodsIn this study, a mixture of ethanol with gasoline (at five levels, 0, 10, 20, 30 and 40%) as a renewable fuel and carbon nanoparticles (at three levels of 0, 20 and 80 ppm) as catalyst were used in spark ignition engine (in 1000, 2000 and 3000 rpm). Engine pollutants such as sound, carbon monoxide, unburnt hydrocarbons, carbon dioxide and oxygen output were measured. Furthermore, a device was designed and manufactured to measure and display the amount of carbon monoxide in the exhaust outlet; moreover, if the amount of carbon increased air compressor was activated to reduce carbon monoxide in the exhaust outlet.Results and Discussion The results showed that with increasing ethanol consumption, the amount of carbon monoxide and unburned hydrocarbons were reduced. Furthermore, the amount of produced oxygen and carbon dioxide increased. Also adding carbon nanoparticles to fuel caused the engine sound level decreased. According to the observation, carbon monoxide decreased while using an electronic device compare to the engine without a carbon monoxide controlling system. This depicts that implementation of carbon monoxide can be control and reduce which is very useful while engine is working under the close environments.ConclusionThe use of alternative fuel, gasoline as well as the reduction of exhaust emissions in the spark ignition engine is of great importance. Therefore, in the present study five levels of ethanol (0, 10, 20, 30 and 40%) and three levels of carbon nanoparticles (0, 20 and 80 ppm) were mixed with gasoline and used in spark ignition engine at three rotation speed (in 1000, 2000 and 3000 rpm). According to the results, there is a reduction in carbon monoxide and unburned hydrocarbons and increasing carbon dioxide emission by using ethanol, because of its fuel bound O2. Furthermore, 3.8% dB 54% reduction in sound and CO, respectively at 3000 rpm with E10 were observed.
H. Biabi; S. Abdanan Mehdizadeh; M. Salehi Salmi
Abstract
IntroductionThe automatic detection of plant diseases in early stages in large farms, in addition to increasing the quality of the final product, could prevent the occurrence of irreparable damage. To this end, accurate and timely diagnosis of farm conditions is of great importance. In order to facilitate ...
Read More
IntroductionThe automatic detection of plant diseases in early stages in large farms, in addition to increasing the quality of the final product, could prevent the occurrence of irreparable damage. To this end, accurate and timely diagnosis of farm conditions is of great importance. In order to facilitate production potential and prevent a significant decline in yield, disease diagnosis is necessary periodically throughout the whole life of the. On the other hand, early detection of the disease in its early stages of growth can also prevent the spread of diseases. One of the most common methods for diagnosing plant diseases is the use of visual methods, but this method is difficult to evaluate the performance of a number of parameters such as the effects of the environment, nutrients, and organisms and so on. Furthermore, the accuracy of repetitions is very much related to individual fatigue of inspector. Research on activities that have the ability to identify diseases at an early stage and prevent the spread of contagious diseases are of great importance. Therefore, the use of new applications and new detection technologies to protect can significantly reduce the risk of product loss. Therefore, the purpose of this research is to design and construct an intelligent control system that automatically detects the health of the lilium plant and to improve the plant's condition.Materials and MethodsSample collectionIn this study, 80 pots of four kilograms (including healthy and disease plants) were considered for plant growth in vegetative stage. The spring onions were grown in pots with 20 cm diameter and 30 cm height. Experiments were carried out in a greenhouse with a temperature of 27.15°C day/night and a relative humidity of 70-75%.Image processing In this research, the camera was placed at a constant distance of 50 cm from the flower to evaluate the stem and the leaves attached to it. The images were captured under the constant light conditions in the greenhouse during a specific hour of the day (10 to 12) every other day. The image was taken in RGB color space with a resolution of 1024 × 840 pixels, and after image transfer to the computer, image processing was performed using Matlab 2016a. After examining the plant image, 9 color channels (R, G, B, L, a, b, H, S, and V) were examined from three color spaces (RGB, Lab and HSV) and stem length to diagnosis of Botrytis elliptica disease.Feature selection and classification In this research, after improving the image and extracting the feature, the linguistic hedges method was used to select the features and the K-means clustering was applied in the N-division of the k-clustering specified by the user. In this method, each attribute was assigned to a cluster closer to the mean vector. This method continues until there was no significant change in the mean vectors between successive repetitions of the algorithm.Results and DiscussionAccording to the results of feature selection L leaf, L stem, a leaf, b leaf, H leaf, b stem, H stem, V leaf and stem length, were the best features. Moreover, the accuracy of diagnosis for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent. Therefore, in general, it can be said that the proposed image processing method is desirable and acceptable in order to diagnose the disease. According to this, zhuang et al. (2017) used sparse representation (SR) classification and K-means clustering to identify leaf-based cucumber disease. In the proposed method, it has been shown that system could detect cucumber diseases with accuracy rate of 85.7%. Therefore, the proposed image processing technique seems to be able to diagnose the disease quickly and easily.ConclusionToday, in the modern agricultural systems, numerous computational methods have been designed to help farmers to control the proper growth of their products. However, there are still major problems with the rapid, accurate and classification of diseases in the early days of the disease. Therefore, the purpose of this study was to design, construct and evaluate a smart system based on image processing in order to identify and classify the leaf disease of the leaves of the lilium plant and remove it by spraying the contaminated parts. For this purpose, the linguistic hedges method was used to select the characteristics and k-means method to classify the infected plant from healthy. The results of the classification for the diseased and healthy plants were 96.42 and 100 percent, respectively, and the overall classification accuracy was 97.63 percent, which indicates the acceptable accuracy of the machine vision system in detecting the disease.
Design and Construction
H. Biabi; S. Abdanan Mehdizadeh; M. Nadafzadeh; M. Salehi Salmi
Abstract
Introduction Leaf color is usually used as a guide for assessments of nutrient status and plant health. Most of the existing methods that examined relationships between chlorophyll status and carotenoid of leaf color were developed for particular species. Different methods have been developed to measure ...
Read More
Introduction Leaf color is usually used as a guide for assessments of nutrient status and plant health. Most of the existing methods that examined relationships between chlorophyll status and carotenoid of leaf color were developed for particular species. Different methods have been developed to measure chlorophyll status and carotenoid. However, the high cost and difficulty to use have restricted their application, whereas the handheld chlorophyll meters such as the SPAD has become popular in the last decade for non-destructive measurement of chlorophyll content. SPAD meter readings have found to be related to the plant’s nutrition status, seed protein content, types of nodulation, and photosynthetic rates of leaves. Digital color (RGB) image analysis, another nondestructive technique is becoming increasingly popular with its potential in phenotyping various parameters of plant health status. The development of low-cost digital cameras that use charged-couple device (CCD) arrays to capture images offers an advantage of low-cost real-time monitoring process over optical sensor based SPAD meter. Gupta et al. (2012) estimated chlorophyll content, using simple leaf digital analysis procedure in parallel to a SPAD chlorophyll content meter. The chlorophyll content as determined by the SPAD meter was significantly correlated to the RGB values of leaf image analysis (RMSE = 3.97). The aim of this research is developing a new inexpensive, hand-held and easy-to-use technique for detection of chlorophyll and carotenoid content in plants based on leaf color. This method provides rapid analysis and data storage at minimal cost and does not require any technical or laboratory skills. Materials and Methods Sample collection In this research, 15 leaves were randomly selected from six types of plants (Shoeblackplant, Vitex, Spiderwort, Sacred fig, Vine and Lotus). Afterwards, the chlorophyll content of the leaf was measured in 3 different ways: 1) using a SPAD instrument; 2) using machine vision system (non-destructive method), and 3) laboratory test using a spectrophotometer. Chlorophyll and carotenoid content The chlorophyll content of the leaf was measured and recorded using SPAD chlorophyll meter (Hansatech, model CL-01, Japan) and spectrometer as explained by Dey et al. (2016). Furthermore, to measure the carotenoid content method described by Gitelson et al. (2006) was utilized. Image processing For estimation of chlorophyll using the image processing algorithm, sample images were taken using CCD (CASIO, model Exilim EX-ZR700, Japan) and transferred to the computer. The camera was mounted perpendicular to the horizontal plane at a fixed distance of 25 cm from the samples. In a consequence histogram of leaf, images were equalized and the average of each color channels from RGB, Lab, HSV, and I1I2I3 were extracted using Matlab 2016. Decision tree regression (DTR) algorithm To develop a regression model to predict chlorophyll and carotenoid content, two decision tree were constructed. The average of each color channels from RGB, Lab, HSV, and I1I2I3 become the predictor variables or feature vector and the real known chlorophyll and carotenoid content become the target variable or the target vector of each regression tree. To develop the regression models, dataset (90 observations) was split into training (60 observations) and test (30 observations) data. Results and Discussion According to the obtained results, a high correlation of 0.92 for chlorophyll and 0.85 for carotenoid was achieved, respectively, between the image processing method and the values measured by the spectrometer. Therefore, it can be said that the proposed image processing method has a desirable and acceptable performance for prediction of both chlorophyll content and carotenoid. The review points out a need for fast and precise leaf chlorophyll measurement technique. With this in mind, Dey et al. (2016) used image processing techniques to measure chlorophyll content. For the purpose of analysis of the proposed model, the model outcome was compared with the LEAF+ chlorophyll meter reading. Regression analysis proofed that there was a strong correlation between the proposed image processing technique and chlorophyll meter reading. Thus, it appears that the proposed image processing technique of leaf chlorophyll measurement will be a good alternative for measuring leaf chlorophyll rapidly and with ease. Conclusion In this research, collections of images from six divers plants (Shoeblackplant, Vitex, Spiderwort, Sacred fig, Vine and Lotus) were analyzed to predict chlorophyll and carotenoid content at different color spaces (RGB, Lab, HSV, and I1I2I3). Based on the results, it was shown that there were high correlations of 0.92 for chlorophyll content as well as 0.85 for carotenoid between the image processing method and the values measured by the spectrometer. Therefore, in general, it can be concluded that the proposed image processing method has a desirable and acceptable performance for prediction of chlorophyll content as well carotenoid.
Image Processing
F. Behzadi Pour; M. Ghasemi-Nejad Raeini; M. A. Asoodar; A. Marzban; S. Abdanan Mehdizadeh
Abstract
Introduction Today, attention to safety and environmental issues in all sectors in agriculture, industry and services is very important. Chemical poisons play an important role in rapid progress of agricultural products. Every year about 25 to 35 percent of the world's crops are affected by insects, ...
Read More
Introduction Today, attention to safety and environmental issues in all sectors in agriculture, industry and services is very important. Chemical poisons play an important role in rapid progress of agricultural products. Every year about 25 to 35 percent of the world's crops are affected by insects, weeds and plant pathogens disappear and this figure would be raised to 80% if no control was applied. Drift problem and its devastating effects are the most important issue which related to users and sprayers manufacturers. Spray drift reduction and improvements in the efficiency of pesticide application processes are global goals. Where ever spraying is applied, drift will be produced and it must be controlled by controlled of the droplet size. The application of these sprayers is the high in the farms (the number of 2303 in Iran). So, this research was carried out to improve the quality of work in these sprayers by studying the droplets diameter and the spray quality index. Materials and Methods The research was conducted at the University of Khouzestan Ramin Agriculture and Natural Resources. Tests were done with 20 m of water sensitive papers at a distance of 2 meters from each other. To evaluate the technical items affecting on drift, an experiment was conducted using a turbo liner sprayer (TURBINA S.A. 800) and the John Deer (JD) 3140 tractor. A completely randomized factorial design was applied. By using 3 replications and the factors were spraying pressure applying three levels (10, 25 and 35 bar), the fan speed with two levels (1998 and 2430 rpm) and forward speed with two levels (9 and 13.5 km hr-1). The sprayer started the application, spraying a solution of water and tracer (yellow Tartrazine E 102), 15m before the water sensitive papers and then moved over the water sensitive papers. The spraying was continued 15 m after the end of the sampling area. After spraying, sensitive papers were photographed and then volume diameter of 50% (DV50) and median numerical diameter (NMD) and spraying quality indicator were calculated. A Spectrophotometry device at the wavelength of 427 nm, Image J and sas 9.2 software were used for measurement. This research was carried out in accordance with the calendar crop canola spraying in field conditions and the weather was calm that the wind speed was 0- 2.5 km hr-1, relative humidity was 29.7% - 32.5% and air temperature was 18.8˚C – 20.7˚C. Results and Discussion According to the results sprayer pressure, fan speed and forward speed were shown significantly different (P≤0.01) on the volume diameter of 50% (DV50) and median numerical diameter (NMD). The effect of spraying pressure on distributing quality indicator was shown significant (P ≤ 0.01), but the fan and forward speed did not shown any significant effect. Mean comparison of the interaction of pressure and forward speed on the spray quality index and the number median diameter were shown significant (P ≤ 0.01), but they did not shown any significant effect on the volume diameter of 50% (DV50). With increasing spraying pressure and fan speed, the droplet size, volume diameter of 50% (DV50) at 72% and numerical median diameter (NMD) at 69% and distributing quality indicator at 46% were decreased that were corresponded with the result of Czaczyk et al. (2012), Peyman et al. (2011), Nuyttens et al. (2009) and Landers and Farooq (2004). With increasing spraying pressure and forward speed, the droplet size, numerical median diameter (NMD) at 63% and distributing quality indicator at 35% were decreased that these resulted were corresponded with the results of Naseri et al. (2007) and Dorr et al. (2013). Conclusion With increasing spraying pressure, fan and forward speed, the droplet size, volume diameter of 50% (DV50) and numerical median diameter (NMD) were decreased. Therefore, spraying quality indicator was decreased. The maximum pressure (35 bars), maximum fan speed (2430 rpm) and maximum forward speed (13.5 km hr-1) were able to produce the minimum spraying quality indicator (10.3). At the minimum pressure (10 bars), maximum fan speed (2430 rpm) and minimum forward speed (9 km hr-1), the maximum spraying quality indicator (2.91) was resulted.
F. Nadi; S. Abdanan Mehdizadeh; O. Nourani Zonouz
Abstract
Introduction The significant of solar energy as a renewable energy source, clean and without damage to the environment, for the production of electricity and heat is of great importance. Furthermore, due to the oil crisis as well as reducing the cost of home heating by 70%, solar energy in the past two ...
Read More
Introduction The significant of solar energy as a renewable energy source, clean and without damage to the environment, for the production of electricity and heat is of great importance. Furthermore, due to the oil crisis as well as reducing the cost of home heating by 70%, solar energy in the past two decades has been a favorite of many researchers. Solar collectors are devices for collecting solar radiant energy through which this energy is converted into heat and then heat is transferred to a fluid (usually air or water). Therefore, a key component in performance improvement of solar heating system is a solar collector optimization under different testing conditions. However, estimation of output parameters under different testing conditions is costly, time consuming and mostly impossible. As a result, smart use of neural networks as well as CFD (computational fluid dynamics) to predict the properties with which desired output would have been acquired is valuable. To the best of our knowledge, there are no any studies that compare experimental results with CFD and ANN. Materials and Methods A corrugated galvanized iron sheet of 2 m length, 1 m wide and 0.5 mm in thickness was used as an absorber plate for absorbing the incident solar radiation (Fig. 1 and 2). Corrugations in absorber were caused turbulent air and improved heat transfer coefficient. Computational fluid dynamics K-ε turbulence model was used for simulation. The following assumptions are made in the analysis. (1) Air is a continuous medium and incompressible. (2) The flow is steady and possesses have turbulent flow characteristics, due to the high velocity of flow. (3) The thermal-physical properties of the absorber sheet and the absorber tube are constant with respect to the operating temperature. (4) The bottom side of the absorber tube and the absorber plate are assumed to be adiabatic. Artificial neural network In this research a one-hidden-layer feed-forward network based on the back propagation learning rule was used to simulate the output temperature of a solar collector. The number of neurons within the hidden layer varied from 1 to 20. The hyperbolic tan- sigmoid and pure-line were used as the transfer function in the hidden layer and output layer, respectively. Minimization of error was achieved using the Levenberg-Marquardt algorithm. To carry out the aforementioned steps, the dataset (105 observations) was split into training (70 observations), and test (35 observations) data. Training sets used to develop models included air velocity, solar radiation, time of the day, ambient moisture and temperature values as inputs with an associated temperature of the collector as outputs. The aim of every training algorithm is to reduce this global error by adjusting the weights and biases. Results and Discussion Compare experimental results with ANN The performance of the three-layer ANN for the prediction of output temperature of flat-plate solar collector by the Levenberg–Marquardt training algorithm was illustrated in Fig. 4. ANN predicted output temperatures with R2 and RMSE of 0.92 and 1.23, respectively. Furthermore, the maximum error in prediction of output temperature of solar collector was 3.3 K. These results are in agreement with Tripathy and Kumar, (2009) those who have predicted the output temperatures of food product in the solar drier using ANN with and RMSE of 0.95 and 0.77, respectively. Compare experimental results with CFD simulation Fig. 6 shows that over the starting length of the absorber plate, there is a variation of the velocity profile which is caused by sharp geometry and it leads to some recirculation of the air in this part of absorber plate. After this part of boundary layers, flow is fully developed and velocity profile becomes smoother and constant. Fig. 8 shows that the predicted temperature was within the experimentally measured temperature. The highest differences between simulated and experimental temperatures were around -2.4K to 4.6K for different time periods. The temperature differences of 4K were reported by Selmi et al. (2008). This disagreement is due possibly to the fact that there are unknown experimental inputs such as turbulence intensity, radiative heat loss from the absorber sheet to the surroundings, Leakage, and measurement tool errors which were not accounted in the model simulations. These losses by radiation are significant at high irradiation levels. This result agrees with studies done in Badache et al. (2012). Thickness of absorber plate and radiation loss, in CFD model, does not take into consideration. For this reason maximum output temperature is seen in maximum radiation which is 12 p.m. While in real condition, it takes some time for absorber plate to get to its maximum temperature. Moreover, the numerical temperature is smaller than the real temperature after 12 p.m. This may occur because of the thickness of metal which keeping the absorbed heat and losing it after awhile. Generally there is a time step hysteresis for the numerical temperature. Conclusion According to this study it can be concluded that the ANN operates better than CFD to predict the output temperature operation. However, ANN method does not give any information about the prediction of temperature distribution and velocity profiles in the solar collector. Although prediction accuracy of the CFD method is less than ANN method, but the provided information on the velocity and temperature profile of the solar collector is still valuable.
Z. Abdolahzare; M. A. Asoodar; N. Kazemi; M. Rahnama; S. Abdanan Mehdizadeh
Abstract
Introduction: Since the application of pneumatic planters for seeds with different physical properties is growing, it is essential to evaluation the performance of these machines to improve the operating parameters under different pressures and forward speeds. To evaluate the performance of precision ...
Read More
Introduction: Since the application of pneumatic planters for seeds with different physical properties is growing, it is essential to evaluation the performance of these machines to improve the operating parameters under different pressures and forward speeds. To evaluate the performance of precision vacuum seeders numerous procedures of laboratory and field have been developed and their feed mechanism evaluation is of great importance. The use of instrumentation is essential in laboratory procedures. Many systems have been designed, using instrumentation, to be able to monitor seed falling trajectory and as a result, in those systems the precise place of falling seed in the seed bed could be determined. In this study, the uniformity of seed spacing of a seed drill was determined using of high speed camera with a frame rate of 480 frames s-1. So that, the uniformity of planting was statistically significant under the influence of the speed of seed metering rollers (Karayel et al., 2006). Singh et al. (2005) studied the effects of disk rotation speed, vacuum pressure and shape of seed entrance hole on planting spacing uniformity using uniformity indices under laboratory and field conditions. They reported miss index values were reduced as the pressure was increased but they were increased with increasing of the speed. The multiple indices on the other hand were low at higher speed but they were increased as the pressure was increased. Ground speed was affected by changes in engine speed and gear selection, both of which effect on amount of fan rotation speed for different pressures. The aim of this study was to identify and determine the effects of forward speed and optimum vacuum pressure amount of the pneumatic seeder.Materials and Methods: The pneumatic planter (Unissem) was mounted on a tractor (MF399) and passed over the soil bin. Thus, the acquired data would be more reliable and practical. To do so, the tractor was equipped with electronic devices for online measurement of various parameters, including: the actual forward speed, wheel sleep percent, drawbar pull, motor RPM, and fuel consumption. Wheel drive of the seed metering mechanism was equipped with Rotary Encoder model S48-8-0360ZT (TK1) to determine the seed disk rotation. For more precise vacuum pressure monitoring, a Vacuum Transmitter model BT 10-210 was used to measure relative pressure from 0 mbar to -1000 mbar. Investigation of seed falling trajectories was conducted using the AVI video acquisition system consisted of CCD (charge-coupled device) camera (Fuji F660EXR) capable of capturing images with a constant speed of 320 frames per second and a spatial resolution of 320×240 pixels. All data were transmitted to a data logger and displayed online on the PC's screen.For optimization of the factors affecting the performance of the pneumatic planter, the experiments were conducted with: two ranges of forward speeds [3 to 4 km h-1, and 6 to 8 km h-1; three levels of vacuum pressure [-2.5kPa, -3.5kPa and -4.5 kPa]; and two types of seed [cucumber and watermelon], keeping a three-factor factorial experimental design. The tests were replicated three times. The uniformity of seed spacing was measured with indicators described by kachman and smith (1995) which are defined as:I_miss=N_1/N×100 (1)I_mul=N_2/N×100 (2)I_qf=100-(I_mul+I_miss) (3)P=s_d/x_ref (4)Which for planting distance of 45 cm, N1 is number of spacing > 1.5Xref; N2 is number of spacing ≤ 0.5Xref and N is total number of measured spacings, Sd is standard deviation of the spacing more than half but not more than 1.5 times, the set spacings Xref, Imiss is the miss index, Imul is the multiple index, quality of feed index Iq is the percentage of spacings that are more than half but not more than 1.5 times, the set planting distance and P is error index.Results and Discussion: According to the studies on both watermelon and cucumber, the ‘quality of feed index’ value in forward speed rang of 6 to 8 km h-1 was less than one in forward speed rang of 3 to 4 km h-1. Also, the ‘error index’ value in forward speed rang 3 to 4 km h-1 was little rather than forward speed rang of 6 to 8 km h-1, but it was desirable.For watermelon and cucumber seeds, the ‘quality of feed index’ were the maximum with mean of 97% and 87% under vacuum pressures of -2.5 kPa and -4.5 km h-1, respectively and forward speed of 3 to 4 km h-1; so that for cucumber seed in the mention treatment, the ‘miss index’ was lowest with mean of zero.The ‘multiple index’ was highest with mean of 6% at 3 to 4 km h-1 forward speed and vacuum pressures of -4.5 for watermelon seed. Values of this index at both forward speed and three levels of vacuum pressures, for cucumber seed was more than watermelon seed.Miss index values were reduced as the pressure was increased but increased with increasing of speed. With lower vacuum pressure and at higher speeds, the metering disc did not get enough time to pick up seeds, resulting the higher miss indices. On the other hand, the multiple indices were low at higher speed but were increased as the pressure was increased (Panning et al. 2000; Zulin et al. 1991).Conclusions: It was observed that seed spacing uniformity was affected by both speed and pressure but not equally. Extracted regression models showed that the best uniformity of spacing for watermelon seed obtained at the rang of speed of 3 to 4 km/h and pressure of -3.5 kPa with a error in spacing of 7% in laboratory condition. Furthermore, in field condition the best uniformity of the seed space occurred at the pressure of -2.5 kPa and rang of speed of 6 to 8 km/h with a 9% error. Similarly, for cucumber seed results showed that the best uniformity obtained at the rang of speed of 3 to 4 km.h-1 and pressure of -4.5 kPa in laboratory condition, and at the low speed and pressure of -2.5 kPa in the field.
