ahmad banakar

In this study, the economic analysis of the architectural part of the building, the use of both the solar thermal and solar electric systems in a poultry farm were discussed. In this economic analysis, the implementation of architectural solutions as well as the utilization of the solar thermal system considering the global gas price and the current gas rates in the country and finally the use of solar electric system with different subsidies were analyzed. Economic analysis was conducted using RETSCREEN software. The results showed that the internal rate of return (IRR) for the architectural solutions of using double-glazed windows and wall insulation, considering the global gas price, was estimated as 53.1% and, the investment return, taking into account the inflation and discount rates, was equal to 2.6 years, which is the break-even point of investing in this solution. Utilizing the solar thermal system and without considering the cost of the underfloor heating system, the IRR value is equal to 34.1%, and considering the cost of the underfloor heating system, the IRR value is equal to 27.9%, which is economic in terms of these solutions. This option proves to be cost-effective. When employing the solar electric system with an IRR of 4.4%, this solution proves uneconomical under all circumstances due to the IRR falling below the bank rate and yielding a negative Net Present Value (NPV). However, when considering a 50% government subsidy and a 1.5-fold increase in the electricity purchase tariff for the solar electric system, this solution approaches the threshold of economic viability

Olive is a Mediterranean fruit that is cultivated mainly for its oil. Extra virgin olive oil has always been the attention and demand of users. Therefore, fraud in virgin and extra virgin olive oils is observed by adding valuable food oils and lower prices such as canola, sunflower, olive pomace, etc. Fraud detection with conventional methods such as gas chromatography is difficult, time-consuming, and requires sample and operator preparation. For this reason, the use of non-destructive technologies for fraud detection is important. In this research, detection of adulteration of pomace oil (olive pomace oil) was investigated using visible/near-infrared spectroscopy (Vis/NIR) technology. Three types of extra virgin olive oil, virgin and refined olive oil were used for sampling. Moreover, the samples were made in six categories of pure, 11, 20, 33, 50 and 100% fraud. Each treatment was prepared and tested in ten samples. Next, in order to analyze the qualitative features and classify the data extracted from the spectrometer, pattern recognition methods including linear discriminant analysis (LDA) and support vector machine (SVM) were used. The obtained results showed that visible/near infrared (Vis/NIR) spectroscopy is able to distinguish olive oil samples based on different percentages of pomace adulteration. Although the LDA method was able to classify olive oil samples with acceptable accuracy according to the adulteration rate, the SVM method had a better accuracy and fit with a training accuracy of 96.69% and a validation of 94.21%. According to the results, the linear function was suggested as the best function for building the classification models using the SVM method.

Today, concerns regarding global warming resulting from fossil fuel usage and the depletion of these resources have led individuals to consider alternative, clean energy sources. Renewable energies such as solar energy are non-polluting, inexhaustible resources, serving as an excellent alternative to fossil fuels. Solar concentrate sunlight, directing it towards the solar oven. Utilizing renewable energy for cooking not only saves time and money but also ensures safety and security. The aim of this research is to construct and assess a solar oven equipped with a point Fresnel lens. The evaluations were conducted over a three-day period in March 2023. The maximum total thermal efficiency obtained for heating the milk for three days with different radiation intensity, wind speed and ambient temperature tested was 45%, 72.7% and 90%, respectively. The findings indicated that the solar oven could raise the milk temperature to 80℃ within one hour. The maximum attainable performance of this system was 90%.

Nowadays, due to the evaluation and high costs of maintenance and repair of sugarcane harvesting machines, it is necessary to monitor sugarcane harvester hydraulic oil using a faster and non-destructive method to determine contamination and TAN index. In this research, the ability of the visible spectroscopy method to non-destructively measure and predict the water content and TAN index in harvester Austoft 7000 hydraulic oil samples at different operating hours was investigated. For this purpose, spectra were taken from the samples in the spectral region of 400-780 nm. Multivariate Partial Least Squares (PLS) regression models were developed based on reference measurements and pre-processed spectra information by combining different pre-processing (Moving Average, Savitzky-Golay, Standard normal variate and First Derivative) methods to measure and to predict the water content and TAN index of hydraulic oil.  The results showed that the visible spectroscopy method could be used for quick and non-destructive measurement of water content and TAN index at different operating hours of harvester Austoft 7000 hydraulic oil. The best prediction results of water content in hydraulic oil were obtained with PLS model based on moving average (MA) preprocessing method (rcv=0.96, RMSECV=1.86, rp=0.89 and RMSEP=3.18), which had excellent accuracy (SDR=3.12). On the other hand, the PLS model based on the combination of moving average preprocessing and standard normal distribution (MA+SNV) was able to predict the TAN index with excellent accuracy (SDR=3.1) (rcv=0.94, RMSECV=0.007, rp=0.89 and RMSEP= 0.010). Therefore, the application of visible spectroscopy technology in agriculture and industries can be recommended for rapid monitoring of hydraulic oil quality and with the aim of controlling pollution.

Microbial contamination of spices and medicinal plants at various stages of the production leads to low quality and shorter shelf life. As a result, sterilization is required to decrease the microbial load. The purpose of the current study was qualitative comparison of the two methods induction heating technology (115, 135 and 155 °C for 45, 60 and 75 s) with gamma irradiation (5 and 10 kGy) for sterilization of cumin seeds. For this purpose, microbial load (total microbial count, mold and yeast and coliform), total color differences and essential oil content were studied as quality properties of cumin seeds. After examining the influence of various induction heating system treatments on the specified parameters, process optimization was carried out by using the response surface methodology. Optimal decontamination conditions were obtained with a combined treatment of 151 °C -46 s. In these conditions, total microbial count, mold and yeast, coliform, total color differences and the amount of essential oil were 3.06 CFU/g, 3.1 CFU/g, 2.28 CFU/g, 4.15 and 2.57%, respectively. The optimal points of the induction heating method were validated and compared with the gamma irradiation method. The results showed that gamma irradiation caused in the greatest decrease of microbial load (1.52 CFU/g) (10 kGy). The least amount total color differences were found at 5 kGy treatment with a value of 2.76. No significant effect on total color differences was identified between gamma irradiation (10 kGy) and induction heating system. The highest and lowest levels of essential oil were found in the induction heating treatment (151 °C -46 s) and gamma irradiation (5 kGy), with the values of 2.45 and 1.7 %, respectively. According to the results of scanning electron microscope, induction heating led to a change in the surface structure, pores and cracks in the seed surface compared to gamma irradiation.

In the current energy and environmental conditions, it is necessary to implement systems based on renewable energy sources in order to reduce energy consumption worldwide. Solar collectors have been studied for years, and many researchers have focused their attention on increasing their efficiency and cost-effectiveness. Solar collectors are implemented for heating domestic water, heating or industrial processes. A promising system that is not yet widely known is air heating by solar collectors, which can be an efficient way to use solar energy with lower investment costs, a system that can preheat the fresh air needed for heating, drying. In this research, the air was established between the absorbent space and its glass cover at speeds of 2, 4, and 6 m/s using a fan, and the effect of incoming air speed and weather conditions such as solar radiation intensity, wind speed, and ambient temperature on the outlet air temperature from the collector and the absorber rod temperature were checked. Based on the obtained results, the temperature of the air coming out of the collector and the temperature of the absorber decreases with the increase in the speed of the incoming air. The greatest impact of weather conditions on the temperature of the absorber rod is related to the intensity of the sun's radiation. The numerical analysis results were obtained with an error of 5% compared to the experimental results.

Determining the status of egg fertilization plays a major role in determining the quality of eggs and their products. In this regard, in order to achieve greater productivity and production, egg evaluation is considered necessary and important in terms of spermatogenesis. In this regard, spectroscopy was performed in the range of 0.01900 nm from 130 local egg samples in the direction of the main diameter for 3 days during the storage period. Spectrum data from spectrometers, in addition to sample information, include unwanted information and noise.  For this reason, in order to achieve accurate classification models, it is necessary to process spectral data before developing the appropriate model. In this regard, intelligent neural network classification was developed based on reference measurements and information of pre-processed spectra by combining different methods of smoothing, normalizing and increasing spectral separation power to determine the presence of sperm in the egg.  Classification results on day zero, first, second, warehousing with 72.3% accuracy, 73.1%, 75.5%, and detection, 86.31, 87.1%, 76% and sensitivity, respectively: 83 61%, 79.63% and 73.3% were obtained.

The cultivation of greenhouse crops is an advance in agricultural production technology that is increasing from temperate to warmer regions. However, greenhouse protects crops in a bad weather condition, but high temperature and humidity in the tropics have a converse effect on a production of crops. Therefore, in these regions, it is necessary to reduce the temperature inside the greenhouse. Since the design of the cooling system is highly dependent on environmental conditions, it is difficult to develop an appropriate system that provides local climate conditions for product growth. Therefore, in this research, the performance of fan-pad cooling system was studied in different climatic conditions. For this purpose, the greenhouse environment was first modeled using TRNSYS software. After verifying the model with experimental results, the system's ability to cool the greenhouse and system performance was evaluated relative to the parameters of air velocity, temperature and humidity ratio. The results showed that in ambient conditions with dry bubble temperature and humidity ratio less than 40˚C and 0.015 kg/kg, direct evaporative cooling system (fan-pad) at an air velocity of 1.25 m/s was a suitable method for cooling the greenhouse and provides suitable conditions for product growth. The cultivation of greenhouse crops is an advance in agricultural production technology that is increasing from temperate to warmer regions. However, greenhouse protects crops in a bad weather condition, but high temperature and humidity in the tropics have a converse effect on a production of crops. Therefore, in these regions, it is necessary to reduce the temperature inside the greenhouse. Since the design of the cooling system is highly dependent on environmental conditions, it is difficult to develop an appropriate system that provides local climate conditions for product growth. Therefore, in this research, the performance of fan-pad cooling system was studied in different climatic conditions. For this purpose, the greenhouse environment was first modeled using TRNSYS software. After verifying the model with experimental results, the system's ability to cool the greenhouse and system performance was evaluated relative to the parameters of air velocity, temperature and humidity ratio. The results showed that in ambient conditions with dry bubble temperature and humidity ratio less than 40˚C and 0.015 kg/kg, direct evaporative cooling system (fan-pad) at an air velocity of 1.25 m/s was a suitable method for cooling the greenhouse and provides suitable conditions for product growth. Also, the results showed that in the case of moisture content greater than 0.015 kg/kg, the evaporative cooling system alone could not cool the greenhouse.

Ohmic heating is defined as a process which alternating electric current is passed through food with the primary purpose of heating it due to the electrical resistance and can be specially applied as an alternative heating method. In this research, Aloe vera gel concentrates having 0.5-2% soluble solids were ohmically heated up to 60°C by using four different voltage gradients (30–60 V/cm). The dependence of electrical conductivity on temperature, voltage gradient, and concentration were obtained. Results indicated that there was a linear relationship between temperature and electrical conductivity. The range of the electrical conductivity was 0.45 to1.20 S/m, which was dependent on the concentration and voltage gradient, although the effect of concentration was very higher than voltage gradient. The ohmic heating System Performance Coefficients (SPCs) were calculated by using the energies given to the system and taken by the Aloe vera gel samples and were in the range of 0.67- 0.89 and the highest SPC (0.89) was observed at 0.5 % and 30 V/cm.

Climate change and essentiality of the food security have motived scientists to try innovative approaches, among which, crop growth models can help to predict crop yield. In order to simulate tomato (Solanum lycopersicum) growth, phenological characteristics of a short-life variety of tomato were assessed. Phenologic characteristics included leaf area index (LAI), specific leaf area (SLA), crop height (H), leaf fresh and dry weight (LFW and LDW), and stem fresh and dry weight (SFW and SDW). These parameters were measured at four different times (i.e. 33, 45, 55, and 87 days after planting) during tomato growth and development. Fruit fresh and dry weight (FFW and FDW), harvest index (HI), and water efficiency () were measured at the end of the crop season. This study was done in a randomized complete block design with three levels of irrigation (i.e. at 48h (i1), 72h (i2), and 96h (i3)) in three replications. Irrigation treatment had significant effects on LAI1, LAI2, H2, FLW1, FLW2, DLW1, DLW2, DL2, FSW1, DSW1, DSW2, and DS2 at the 0.01 level, while its effect on SLA1, SLA2, H1, and FSW2 was significant at the level 0.05. Two-tailed correlations among characteristics were investigated and regression models developed for DFW. Dry fruit weight was simulated using both AquaCrop and regression models, separately. It was found that regression model could predict DFW of tomatoes under different treatment better than AquaCrop. It was also concluded that the phenologic characteristics measured at 55 DAP provide good criteria for predicting tomato fruit production.

This paper presents a new method for the design and operation of photovoltaic power plants connected to the network anywhere in the country. Using an appropriate module and inverter can power while increasing efficiency and ideal use of solar energy potential, prevent power dissipation and system cost in different parts of the Draft .also prevent the loss of power in the shadow of modules on each other or the other computing takes place. The main characteristics of the panel and inverter design that is fit and homogeneity. HelioScope Software due to weather conditions and the geometry of the selection panel, the best way to offer the appropriate inverter type of wiring. SketchUp Software suitable guideline for evaluating the shadows during the year. Acoustic station Mehrabad NASA weather data and virtual stations can be used with high accuracy. Plant Mehrabad weather station data can yield% annual 7/78 and 3/735 MW due to the virtual station NASA 8/79% yield equivalent to 7/724 MW power generation have. The highest temperature and the lowest power dissipation in the wiring and inverter are mismatched.

Separation of fertile and infertile eggs is a major concern of the poultry industry. If this separation is done at the early days of the incubation period, less space will be occupied in the incubator resulting in improved capacity and efficiency. Dielectric constant and loss factor were used in this study to detect infertile Hy-line eggs. In this study, 126 eggs were selected and their dielectric properties were measured on the third day of incubation. On the fifth day, it was detected that there were 112 fertile and 14 infertile eggs. Data were then analyzed by Weka software using artificial neural network (ANN) and support vector machine (SVM) classifiers. Results showed that, on the third day of incubation, SVM and ANN successfully detected infertile and fertile eggs with 100% accuracy. Detecting the fertile egg by using electrical conductivity is a non-destructive method and is also more accurate than image processing, signal processing and PCR methods.

In this research with utilization various neural networks models¡ the relationship between the amount of water production and the temperature of the vapor with different weather conditions¡ time of day and several water debit in desalination system equipped whit linear solar parabolic concentrator was investigated. The results showed that static and dynamic networks can be modeled the process of production fresh water with high accuracy. Static neural network can do the modelling process with higher speed than dynamic neural network. However it seems that the amount of error with using dynamic networks was reduced in process modeling. Coefficient of determination (R2) for training¡ validation and testing in static networks were 0.9898¡ 0.9899 and 0.9889¡ respectively. While coefficient of determination (R2) for training¡ validation and testing in dynamic networks were 0.9922¡ 0.9894 and 0.9901¡ respectively. Also the amount of mean square error (MSE) in static network for training¡ validation and testing was 0.0011¡ 0.0027 and 0.0024¡ respectively and for dynamic networks was 0.0018¡ 0.0007 and 0.0004¡ respectively. Comparison between dynamic and static networks show that the dynamic networks can be predicted the production of fresh water and vapor temperature according to changes in atmospheric parameters accurately than the static networks.

In this paper, a smart method is designed in order to classify healthy and illness ducks using their emission voice. For this purpose, firstly, the birds based on their healthy condition are divided into the different categories and then their voices are saved using a microphone and data acquisition card. Gained signals were transformed from time-domain signal to frequency domain using Fast Fourier Transform (FFT). Then, 5 statistical features are extracted from both time and frequency signals namely, mean, standard division, root mean square, variance and kurtosis. Two classifiers which are artificial neuralnetworks (ANN) and support vector machine (SVM) are used, in order to acquire the bird classification in healthy and sick accuracy. The accuracy of ANN classifier in detection of healthy birds within sick and weak birds was determined 75% and 82.1 % based on the time and frequency domain of the sound signals, respectively. The accuracy of SVM classifier in detection of healthy birds within sick and weak birds was determined 85.7 % and 92.8 % based on the time and frequency domain of the sound signals, respectively.

In this research, an intelligent method is introduced for remaining useful life prediction of an internal combustion engine timing belt based on its vibrational signals. For this goal, an accelerated durability test for timing belt was designed and performed based on high temperature and high pre tension. Then, the durability test was began and vibration signals of timing belt were captures using a vibrational displacement meter laser device. Three feature functions, namely, Energy, Standard deviation and kurtosis were extracted from vibration signals of timing belt in healthy and faulty conditions and timing belt failure threshold was determined. The Artificial Neural Network (ANN) was used for prediction and monitoring vibrational behavior of timing belt. Finally, the ANN method based on Energy, Standard deviation and kurtosis features of vibration signals was predicted timing belt remaining useful life with accuracy of 98%, 98% and 97%, respectively. The correlation factor (R2) of vibration time series prediction by ANN and based on Energy, Standard deviation and kurtosis features of vibration signals were determined as 0.87, 0.91 and 87, respectively. Also, Root Mean Square Error (RMSE) of ANN based on Energy, Standard deviation and kurtosis features of vibration signals were calculated as 3.6%, 5.4% and 5.6%, respectively.

One of the best applications of automated navigation systems is the use of automatic steering systems in agricultural vehicles. In this research، a machine vision algorithm was designed، based on image acquisition from a camera mounted on front of acombine harvester cab fordetection of its forward path. After filtration and optimization of the image، Hough transform was utilized for extracting the path as a line. Then lateral and angular deviations from the path obtained as main inputs for the automatic guidance system of combine harvester. The algorithm was tested in 7 lateral and 7 angular deviation treatments with three replications. The tests were carried out under different conditions of day light. Comparison of the values extraction by the image processing algorithm with angles and deviations measured manually showed that the algorithm is able to detect the designed line with an accuracy of 98%.