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 ...
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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.
M. Hamdani; M. Taki; M. Rahnama; A. Rohani; M. Rahmati-Joneidabad
Abstract
IntroductionControlling greenhouse microclimate not only influences the growth of plants, but is also critical in the spread of diseases inside the greenhouse. The microclimate parameters are inside air, roof, crop and soil temperature, relative humidity, light intensity, and carbon dioxide concentration. ...
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IntroductionControlling greenhouse microclimate not only influences the growth of plants, but is also critical in the spread of diseases inside the greenhouse. The microclimate parameters are inside air, roof, crop and soil temperature, relative humidity, light intensity, and carbon dioxide concentration. Predicting the microclimate conditions inside a greenhouse and enabling the use of automatic control systems are the two main objectives of greenhouse climate model. The microclimate inside a greenhouse can be predicted by conducting experiments or by using simulation. Static and dynamic models and also artificial neural networks (ANNs) are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Usually thermal simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. So the main objective of this paper is comparison between two types of artificial neural networks (MLP and RBF) for prediction 4 inside variables in an even-span glass greenhouse and help the development of simulation science in estimating the inside variables of intelligent greenhouses.Materials and MethodsIn this research, different sensors were used for collecting the temperature, solar, humidity and wind data. These sensors were used in different positions inside the greenhouse. After collecting the data, two types of ANNs were used with LM and Br training algorithms for prediction the inside variables in an even-span glass greenhouse in Mollasani, Ahvaz. MLP is a feed-forward layered network with one input layer, one output layer, and some hidden layers. Every node computes a weighted sum of its inputs and passes the sum through a soft nonlinearity. The soft nonlinearity or activity function of neurons should be non-decreasing and differentiable. One type of ANN is the radial basis function (RBF) neural network which uses radial basis functions as activation functions. An RBF has a single hidden layer. Each node of the hidden layer has a parameter vector called center. This center is used to compare with the network input vector to produce a radially symmetrical response. Responses of the hidden layer are scaled by the connection weights of the output layer and then combined to produce the network output. There are many types of cross-validation, such as repeated random sub-sampling validation, K-fold cross-validation, K×2 cross-validation, leave-one-out cross-validation and so on. In this study, we pick up K-fold cross- validation for selecting parameters of model. The K-fold cross-validation is a technique of dividing the original sample randomly into K sub-samples. Different performance criteria have been used in literature to assess model’s predictive ability. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2) are selected to evaluate the forecast accuracy of the models in this study.Results and Discussion The results of neural networks optimization models with different networks, dependent on the initial random values of the synaptic weights. So, the results in general will not be the same in two different trials even if the same training data have been used. So in this research K-fold cross validation was used and different data samples were made for train and test of ANN models. The results showed that trainlm for both of MLP and RBF models has the lower error than trainbr. Also MLP and RBF were trained with 40 and 80% of total data and results indicated that RBF has the lowest sensitivity to the size data. Comparison between RBF and MLP model showed that, RBF has the lowest error for prediction all the inside variables in greenhouse (Ta, Tp, Tri, Rha). In this paper, we tried to show the fact that innovative methods are simple and more accurate than physical heat and mass transfer method to predict the environment changes. Furthermore, this method can use to predict other changes in greenhouse such as final yield, evapotranspiration, humidity, cracking on the fruit, CO2 emission and so on. So the future research will focus on the other soft computing models such as ANFIS, GPR, Time Series and … to select the best one for modeling and finally online control of greenhouse in all climate and different environment.ConclusionThis research presents a comparison between two models of Artificial Neural Network (RBF-MLP) to predict 4 inside variables (Ta, Tp, Tri, Rha) in an even-span glass greenhouse. Comparison of the models indicated that RBF has lower error. The range of RMSE and MAPE factors for RBF model to predict all inside variables were between 0.25-0.55 and 0.60-1.10, respectively. Besides the results showed that RBF model can estimate all the inside variables with small size of data for training. Such forecasts can be used by farmers as an appropriate advanced notice for changes in temperatures. Thus, they can apply preventative measures to avoid damage caused by extreme temperatures. More specifically, predicting a greenhouse temperature can not only provide a basis for greenhouse environmental management decisions that can reduce the planting risks, but also could be as a basic research for the feedback-feed-forward type of climate control strategy.
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 ...
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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.