M. Asafi; R. Meamar Dastjerdi; M. Noshad
Abstract
Introduction In recent years, with increasing population growth and improving livelihoods, the consumption of vegetable oils has been increasing and has led to an increase in the level of oilseed cultivation. Sesame (Sesamum indicum L.) is an economically important crop which is widely cultivated all ...
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Introduction In recent years, with increasing population growth and improving livelihoods, the consumption of vegetable oils has been increasing and has led to an increase in the level of oilseed cultivation. Sesame (Sesamum indicum L.) is an economically important crop which is widely cultivated all over the world. Sesame has been considered as an oil plant for cultivation in Iran's climatic conditions recently. Sesame contains about 58-44% oil, 18-25% protein and 13.5% carbohydrate. Sesame is grown mainly in the developing tropical and subtropical areas of Asia, Africa. The three countries of China, India and Myanmar are accounted as the largest producers of this product in the world. Screw pressing is the most reliable method for extracting oil from oilseed grains. This method is simpler than others and is more efficient in terms of cost and food security. The general objective of this research was to investigate the effects of rotational speed, temperature, type of screwing and die diameter on the amount of oil extraction from sesame oil and prediction of oil extraction using artificial neural network and compare to regression models. Materials and Methods In this research, a sesame oil extractor machine was designed and manufactured. Various experiments were carried out to determine the amount of oil extracted based on variable parameters such as the shape of the press screw, the rotational speed, the temperature and the diameter of the die. The experiment was performed at three levels of press screw type (constant pitch, variable pitch and conical), temperature (30, 60, 90), three levels of rotational speed (20, 50, 80 rpm) and three level of die diameter (6, 8, 10mm). The experimental design was factorial based on completely randomized design with three replications. The mathematical software (Matlab, 2012b) was used to determine the optimal neural network. The type of network was Multi-Layer Perceptron (MLP). In order to design this network, there were 3 neurons in the first layer (input), which was equal to the number of studied variable parameters (type of screw, rotational speed and temperature), the second layer was hidden layer, and the last layer (the output) had a neuron for the extracted oil) was equal to the number of outputs examined in this network. The Levenberg-Marquardt algorithm (LM) was used to train it, which is one of the fastest neural network training methods. The Second-order polynomial regressions were performed based on the step-by-step method and non-meaningful sentences were eliminated from the model. The accuracy of the models was determined by calculating the correlation coefficient and root mean square error (RMSE) indices. Results and Discussion The results of the experiments showed that the effect of type of press screw, rotational speed, extraction temperature and die diameter on the amount of oil extraction was significant (p≤0.01). The highest amount of extracted oil was obtained at conical press screw , rotational speed of 50 rpm, temperature of 60 °C and die diamter of 6 mm. An artificial neural network of three-layer perceptron and regression models were used to predict the amount of sesame oil extracted. The results showed that the artificial neural network model (1-8-3) with a correlation coefficient of 97.47% and a RMSE of 0.65 compared to linear regression and quadratic regression models had the higher efficiency in predicting the amount of extracted oil. Conclusion In this study, the effect of temperature, rotational speed, press screw type and die diameter on the amount of extracted oil were investigated. The results of this study showed that the change in the type of screw, rotational speed, diameter of die and temperature on the amount of extracted oil was significant at 1% level. Results also showed that the artificial neural network method was more efficient than linear and second order regression methods.
M. Baghani; M. H. Aghkhani
Abstract
IntroductionIran as one of the largest producers of poultry in Asia and plays major role in feeding the world's population, particularly in the poultry industry. Research about this industry will help to improve the quality and the quantity of products. Increasing of the concentration of toxic gases ...
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IntroductionIran as one of the largest producers of poultry in Asia and plays major role in feeding the world's population, particularly in the poultry industry. Research about this industry will help to improve the quality and the quantity of products. Increasing of the concentration of toxic gases such as NH3 (ammonia), CO2 (carbon dioxide), SH2 and CH4 in poultry houses comes from bird activity inside the barn is one of the basic problems of the farming. Increasing the amount of these gases more than standard level would cause heavy mortality and reductions in the production. Ammonia is one of the most toxic gases in poultry houses, which must be controlled. Different studies have been carried out on measurement of ammonia emissions from poultry houses to reduce energy consumption and reduce emissions of ammonia. But no specific study has been found on ammonia emissions in Iran and there is no reliable documents of ammonia emissions from poultry in this country.Materials and MethodsIn this study a poultry house with 18 thousand chickens was used to measure the emission rate of ammonia, the effect of temperature, moisture and age of chickens on emissions of ammonia in Sabzevar city. The barn was equipped with semi-automatic mechanical ventilation. At the first step of this research all sensors was installed for data collection, i.e., air velocity, temperature, humidity and ammonia concentration. Recorded data information were stored in a central computer. Five digital sensors, model AM2303, have been used to measure the temperature and humidity of the ambient air quality. The concentration of ammonia in the air inputs and outputs of the farm was measured using an ammonia sensor model TGS2444 every 10 seconds throughout the study and recorded in the central system. The average speed of the exhaust air was measured using the hot wire anemometer probe for every fan. The outputs of all sensors was converted to digital data and transferred to the central computer using RS485 cable in each module. Converting of the sensors output to digital data reduces changing the data and probable errors. Ammonia emission rates was found by calculating the concentration of ammonia and measuring the rate of input air and fans exhaust air by ammonia gas equilibrium equation. Relation of the ammonia emission rate was achieved using affective factors such as age of the birds and inside air humidity and temperature by regression method.Results and DiscussionThe average rate of ammonia emission during broiler growing were measured 89 mg per day for each bird. Ammonia emission rates increased until the age of 37 days and then decreased after the age of 37 days. Age of birds has the highest impact coefficient and temperature and relative humidity of the barn have the least impact coefficients on the ammonia emission rate. The ammonia emission rate has also increased by increasing the age of the bird, temperature and relative humidity of the air. Comparing of the ammonia emission rate derived from regression equation with real conditions showed that the regression equation method has a high precision for estimating the ammonia emission rate.ConclusionIt is showed that the results of this research can predict the ammonia emission rate in the poultry houses and predict the required ventilation rates to minimize the amount of ammonia concentration. The results of this study can be used for automatic control system to minimize energy consumption in the poultry houses. According to the results, the reduction of temperature and humidity in poultry house can be used to reduce the ammonia level.
N. Gholamrezaei; K. Qaderi; K. Jafari Naeimi
Abstract
Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent ...
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Introduction Energy consumption management is one of the most important issues in poultry halls management. Considering the situation of poultry as one of the largest and most developed industries, it is needed to control growing condition based on world standards. The neural networks as one of the intelligent methods are applied in a lot of fields such as classification, pattern recognition, prediction and modeling of processes. To detect and classify several agricultural crops, a research was conducted based on texture and color feature. The highest classification accuracy for vegetables, grains and fruits with using artificial neural network were 80%, 86% and 70%. In this research, the ability to Multilayer Perceptron (MLP) Neural Network in predicting energy consumption, temperature and humidity in different coordinate placement of electronic control unit sensors in the poultry house environment was examined. Materials and Methods The experiments were conducted in a poultry unit (3000 pieces) that is located in Fars province, Marvdasht city, Ramjerd town, with dimensions of 32 meters long, 7 meters wide and 2.2 meters height. To determine the appropriate placement of the sensor, 60 different points in terms of length, width and height in poultry were selected. Initially, the data was divided into two datasets. 80 percent of total data as a training set and 20 percent of total data as a test set. From180 observations, 144 data were used to train network and 36 data were used to test the process. There are several criteria for evaluating predictive models that they are mainly based according to the difference between the predicted outputs and actual outputs. To evaluate the performance of the model, two statistical indexes, mean squared error (MSE) and the coefficient of determination (R²) were used. Results and Discussions In this study, to train artificial neural network for predicting the temperature, humidity and energy consumption, the trainlm algorithm (Levenberg-Marquardt) was used. To simulate temperature, humidity and energy consumption, networks were trained with two and three layers, respectively. Network with two layers with10 neurons in the hidden layer and one neuron in the output layer with (R²) equal to 0.96 and (MSE) equal to 0.00912, was given the best result for predicting temperature. For humidity electronic sensors, results showed that network with three layers with the 10 neurons in the first hidden layer, 20 neurons in the second hidden layer and one neuron in the output layer with (R²) equal to 0.8 and (MSE) equal to 0.00783 was the best for predicting humidity. Finally, network with two layers with 10 neurons in the first hidden layer, 10 neurons in the second hidden layer and one neuron in the output layer was selected as the optimal structure for predicting energy consumption. For this topology, (R²) and MSE were determined to 0.98 and 0.00114, respectively. Linear and multivariate regression for the parameters affecting temperature, humidity and energy consumption of electronic sensors was determined by the STATGR software. Correlation coefficients indicated that parameters such as length, height and width of the electronic control sensors placed in the poultry hall justified 82% of the temperature changes, 61% of the humidity changes and 92% of the energy consumption changes. Therefore, comparing with correlation coefficients obtained from the neural network models, the highest correlation coefficient was related to energy parameter and the lowest correlation was linked to humidity parameter. Conclusion The results of the study indicated the high performance for predicting temperature, humidity and energy consumption. The networks hadthree inputs including length, width and height of electronic sensor positions and an output for temperature, humidity and energy consumption. For training networks the multiple layer perceptron (MLP) with error back propagation learning algorithm (BP) was used. Functions activity for all networks in hidden layers were tangentsigmoid and in the output layer, linear (purelin). Comparing the results of artificial neural network and logistic regression model showed that artificial neural network model with correlation coefficients of 0.98 (energy), 0.96 (temperature) and 0.8 (humidity) provided closer data to the actual data compared with regression models with correlation coefficients of 0.92, 0.82 and 0.61 for the energy, temperature and humidity respectively.
E. Ahmadi; H. Barikloo
Abstract
Introduction: Some forces and impacts that occur during transporting and handling can reduce the apricot quality. Bruise damage is a major cause of fruit quality loss. Bruises occur under dynamic and static loading when stress induced in the fruit exceeds the failure stress of the fruit tissue. Needless ...
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Introduction: Some forces and impacts that occur during transporting and handling can reduce the apricot quality. Bruise damage is a major cause of fruit quality loss. Bruises occur under dynamic and static loading when stress induced in the fruit exceeds the failure stress of the fruit tissue. Needless to say that physical and mechanical properties of fruits in the design and optimization of systems related to production, processing and packaging of the products are important. Harvesting, transport, packaging and transportation of fruits and vegetables, result in their bruising which can cause loss of marketability of the fruit by consumers. The term of ‘absorbed energy’ could be used to express the quantity of damage done on the fruit and the high the absorbed energy, the higher the damage on the fruit. The object of this research was due to the importance of apricot fruit and lack of information about the mechanical behavior.
Materials and Methods: In this study, apricot fruit variety “Ziaolmolki” was examined to determine some physical and mechanical properties. In order avoid any damage, the fruits were carefully harvested from trees and gathered in plastic boxes in a row, to prevent damage to the apricots. For determination of mechanical properties and levels of impact energy used test axial machine and pendulum device, respectively. Dependent variables (acoustics stiffness, radius of curvature, color characteristic a* and b*, Brix percentage, penetration force, penetration work and penetration deformation) and independent variables (impact energy in three levels, temperature and color in 2 levels each) were selected and analyzed by block designs with factorial structure. In the experimental design, the fruits were stored in two temperature levels, 3oC and 25oC. Two areas of any fruit (red and yellow areas) were subjected to 3 impact energy levels. For each of the 8 levels, 8 fruit samples were selected. Overall, 96 fruits {8 (number of fruit per level) × 3 (impact energy level) × 2 (both red and yellow) × 2 (at 25oC and 3oC)} was selected. In this study, using a factorial experiment in a completely randomized design, the effect of different factors (impact energy in 3 levels, temperature in 2 levels 3oC and 25°C and color in 2 levels red and yellow) on acoustic stiffness, radius of curvature, color characteristic a* and b*, precent Brix, penetration force, penetration work and penetration deformation in apricot under the quasi-static forces were studied. In order to conduct this experiment, the universal testing machine of biological materials was used. After the determination of mechanical properties of the products, the SAS statistical program (1.9) was applied to analyze and normalize the resulted data.
Factorial test also was used to determine the effects of independent variables on the dependent variables. Data analyses were performed using Statistical Package for the Social Sciences (SAS version 19.0).The variance analysis of the data was conducted in the form of multivariate factorial (2×2×3) design. The data were collected by three controlling factors: two temperature levels (3 and 20°C), two types of colour (Yellow and Red fruits) and three levels of impact energy. The Duncan’s multiple range tests was used to compare the means. The values of reducible sugars were measured by the fruit juice standard - test methods No. 2685 (Institute of Standards and Industrial Research of Iran). The apricots TSS (total soluble solids) for each temperature level by Refractomete (Model: 3820 (PAL-2), Resolution: ± 0.1% Brix) were obtained.
Results and Discussion: Respectively, the main and interaction effects of these variables were examined. The results of analysis of variance showed that,, the radius of curvature, color characteristic, acoustics stiffness, elastic modulus, percent Brix, penetration force and penetration deformation on main and interaction effects were significant at 5% and 1% probability level. According to the analysis of variance table between dependent and independent parameters, a significant effect was observed. Increasing impact energy, the penetration force and penetration deformation at 3°C was higher than at 25°C (Fig.3, 4, 7 and 8). Increasing impact energy, the red zone showed more penetration deformation and penetration force than the yellow zone (Fig.5 and 6). In a constant level of energy the higher the temperature of fruit tissue, the more energy is absorbed, due to this fact that lower temperatures can increase stiffness of the fruit, and leads to transport of absorbed energy to inside the tissue and increase the fruit bruising and final results in less needed penetration force for fruit transformation. Apricot acoustic stiffness in the temperature of 3oC was higher than in the temperature 25oC (Table 3). Fruit stiffness and tissue viscosity increases with increasing temperature. With increasing tissue stiffness, the less impact energy is absorbed and less bruising in fruit tissue is created. Because of more tissue stiffness, in order to create penetration in fruit tissue the more transformation is needed.
Conclusions: The red zone showed a higher bruise susceptibility of ripe apricots. According to the analysis of variance table between dependent and independent parameters, a significant effect was observed. Increasing impact energy, the penetration force and penetration deformation at 3°C was higher than at 25°C. Increasing impact energy, the red zone showed more penetration deformation and penetration force than the yellow zone. Apricot acoustic stiffness in the temperature of 3 oC was higher than in the temperature 25oC.
J. Khodaei; H. Samimi
Abstract
Kurdistan Rasa grape is one of the delicious and sweet fruits with black color. It contains vitamins E, C and some protectors such as antioxidants. In order to design equipments and facilities for drying, preservation and processing of Rasa grape, it is necessary and important to know about its specific ...
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Kurdistan Rasa grape is one of the delicious and sweet fruits with black color. It contains vitamins E, C and some protectors such as antioxidants. In order to design equipments and facilities for drying, preservation and processing of Rasa grape, it is necessary and important to know about its specific heat and thermal conductivity. In this paper the specific heat and thermal conductivity of Rasa grape were studied. The method of mixtures and hot wire as a heating source was used for measuring the specific heat and thermal conductivity, respectively. The experimental variables were temperature at four levels of 40, 50, 60 and 70 °C and moisture content at four levels of 22.36, 37.56, 52.13 and 71.53%. The results showed that the specific heat and thermal conductivity of Rasa grape increased linearly from 1.6523 kJ kg-1°C-1 to 3.3253 kJ kg-1°C-1 and 0.1252 W m-1°C-1 to 0.4202 W m-1°C-1 respectively, with increasing moisture content and temperature. The results also showed that the effect of moisture content on increasing the specific heat and thermal conductivity was more significant than that from temperature rise.
