Modeling
A. Shahraki; M. Khojastehpour; M. R. Golzarian; E. Azarpazhooh
Abstract
IntroductionDrying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the ...
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IntroductionDrying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the drying process. However, this approach consumes time and cost, with a significant amount of energy resources. By harnessing available software and leveraging technological advancement to develop a general model for drying food under varying initial conditions, the drying process can be significantly optimized.Materials and MethodsThis study was conducted with the aim of simulating heat and mass transfer during Refractance window drying for aloe vera gel. Comsol Multiphysics version 5.6 is a three-dimensional model used to solve heat and mass transfer equations. For this purpose, the differential equations of heat and mass transfer were solved simultaneously and interdependently. The above model considered various initial conditions: water temperature of 60, 70, 80, and 90℃, and aloe vera gel thickness of 5 and 10 mm. The initial humidity and temperature of the aloe vera is uniform. The initial temperature is 4℃ and the initial humidity of the fresh aloe vera sample is 110 gwater/gdry matter. Heat is supplied only by hot water from the bottom surface of the product.Results and DiscussionThe drying time was needed to reduce the moisture content of aloe vera gel from 110 to 0.1 gwater/gdry matter during Refractance window drying. Aloe vera gel with a thickness of 5 mm dried in 120, 100, 70, and 50 minutes at water temperatures of 60, 70, 80, and 90℃, respectively. For a 10 mm thick layer of aloe vera gel, the drying time was 240, 190, 150, and 120 minutes, for water temperatures of 60 to 90℃, respectively. These results demonstrate the importance of both the water temperature and thickness on the drying time. Furthermore, the drying rate of aloe vera gel increased as the water temperature increased from 60 to 90℃, the drying rates were 0.915, 1.099, 1.57, and 2.198 gwater/min for 5 mm thickness and 0.457, 0.578, 0.732, and 0.915 gwater/min for 10 mm thick layer of aloe vera gel, respectively.ConclusionBased on the simulation results, the optimal model is with a water temperature of 90℃ and an aloe vera gel thickness of 5 mm. Overall, the modeling results are consistent with the results of experimental data.
Modeling
F. Motazedian; M. Taki; R. Farhadi; M. Rahmati-Joneidabad
Abstract
IntroductionGreenhouse cultivation is the popular intensive kind of crop production with a yield per cultivated unit area more than 10 times higher compared to field crops. Greenhouse production requires the use of large amounts of energy, water, and pesticides and it usually generates huge quantities ...
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IntroductionGreenhouse cultivation is the popular intensive kind of crop production with a yield per cultivated unit area more than 10 times higher compared to field crops. Greenhouse production requires the use of large amounts of energy, water, and pesticides and it usually generates huge quantities of wastes to be disposed of it. Investment, labor, and energy costs per unit area are much higher in the greenhouse industry than in any other agricultural sectors. Sustainable greenhouse systems, socially supportive, commercially competitive, and environmentally sound, depend on cultivation techniques, equipment management, and constructive materials that aim to reduce agrochemicals, energy and water consumption as well as waste generation. The management of the greenhouse environment is depending on temperature manipulation. Temperature manipulation is critical to influencing plant growth, quality, and morphology and so is a major strategy in the environmental modification of crops. Heterogeneous indoor microclimate of a greenhouse has long become a matter of concern in many studies. It is believed to be unfavorable for crop growth, which damages crop activity, particularly transpiration and photosynthesis, one of the major causes of non-uniform production and quality. Since early and conventional methods are not sufficient to evaluate microclimate variables inside a greenhouse, Computational Fluid Dynamics (CFD) approach was applied for better and more accurate results. CFD is an effective numerical analysis technique to predict the distribution of the climatic variables inside cultivation facilities. Numerous studies have focused on the internal temperature, humidity, solar radiation, and airflow inside multiple cultivation facilities. For example, the CFD method was used to simulate natural ventilation for agricultural buildings and improve crop production systems. The CFD simulation and evaluation models could be applied for evaluation of the inside situation and temperature in greenhouses. Thermal and water vapor transfer is influenced by the openings of greenhouses in the CFD simulation. The CFD model was developed to predict the distribution of temperature, water vapor, and CO2 occurring in a Venlo-type semi-closed glass greenhouse equipped with air conditioners. Based on the above literature, this research aims to evaluate the energy flow and modeling of an un-even semi-buried greenhouse using external and internal variables and numerical solutions by the CFD method.Materials and MethodsIn this study, Computational Fluid Dynamic (CFD) solution was applied to evaluate the inside environment of a semi-double glass greenhouse with an east-west location. This greenhouse has a special structure that is used in very hot or very cold areas due to its depth of more than one meter below the ground. The greenhouse has an area of 38m2 and an air volume of 78.8m3. The temperature and humidity data were collected from inside and outside the greenhouse by temperature sensors (SHT 11 model made by CMOS USA). Irradiation data were collected inside the greenhouse, on level ground, by the TES132 radiometer.Results and DiscussionIn this study, the CFD method was used for a model solution with ANSYS Fluent version 2020R2 software. To evaluate the predictive capability of the model and its optimization, the comparison between actual (ya) and predicted values (yp) was used. Three criteria of RMSE, MAPE, and R2 were also used to evaluate the accuracy of the final model. The results showed that the dynamic model can accurately estimate the temperature of the air inside the greenhouse at a height of 1 m (R2 = 0.987, MAPE = 2.17%) and 2 m (R2 = 0.987, MAPE = 2.28%) from the floor. The results of energy flow showed that this greenhouse transfers 6779.4.4 kJ of accumulated thermal energy to the ground during the experiment.ConclusionIn the present study, the computational fluid dynamics method was used to simulate the internal conditions of an un-even semi-buried greenhouse with external and internal variables including temperature and solar radiation. The results showed that this greenhouse structure is able to transfer part of the increase in temperature caused by sunlight to the soil depth (104.214 kJm-2 heat through the floor, 178.443 kJm-2 through the north wall and 113.757 kJm-2 through the south wall). By increasing the thermal conductivity of the inner surface of the greenhouse, the heat flux to the depth of the soil can be increased.
A. Ziaaddini; H. Mortezapour; M. Shamsi; A. Sarafi
Abstract
Introduction Greenhouse cultivation has been increased in response to population growth, reduction in available supplies and arable lands and raising the standards of living. The quality and quantity of the products are profoundly affected by the greenhouse temperature. Therefore, providing an appropriate ...
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Introduction Greenhouse cultivation has been increased in response to population growth, reduction in available supplies and arable lands and raising the standards of living. The quality and quantity of the products are profoundly affected by the greenhouse temperature. Therefore, providing an appropriate heating system is an elementary requirement for greenhouse cultivation. A number of factors such as glazing material, greenhouse configuration, product type, and climate conditions should be considered to design a greenhouse heating system. Due to the environmental concerns associated with the fossil fuels, renewable energy-powered heating systems such as geothermal, solar and biomass- are increasingly considered as the alternative or supplementary to the traditional fossil fuel heating equipment in greenhouses. In this way, a number of researchers have developed different greenhouse heating systems to reduce fossil fuel consumption. In Iran, because of appropriate available solar irradiance, the solar heating systems can be efficiently employed for greenhouse cultivation. A compound solar greenhouse heating system was experimentally and analytically investigated in the present study. To verify the obtained heat transfer equations, a set of experiments were carried out at Biosystems Engineering Campus of the Shahid Bahonar University of Kerman. Materials and Methods The designed system was comprised of a Parabolic Trough solar Collector (PTC), a dual-purpose modified Flat Plate solar Collector (FPC) and a heat storage tank. The modified FPC was located inside the greenhouse to act as a heat exchanger to transfer the stored heat to the greenhouse atmosphere during the night. The FPC also collects the solar radiations during the sunshine hours to enhance the thermal energy generation. Heat transfer equations of the PTC and the FPC were written and the useful energy gain of the heating system was determined at the quasi-static condition during the day. Experimental verification of the analytical models was conducted using regression coefficient (r) and root mean square percent deviation (e) criteria as follows: where Xi and Yi are respectively the ith analytical and experimental data and n shows the number of observations. Exergy analysis of the PTC and the FPC were carried out and the effect of the different fluid flow rates through the PTC on the exergy efficiency of the different components was investigated using the experimental data. Results and Discussion Increasing the fluid flow rate increased outlet temperature of the PTC due to the increase in heat removal factor and inlet temperature; whereas, caused a reduction in outlet temperature of the FPC. Since the thermal efficiency of the PTC improved with the fluid flow rate, the PTC fraction enhanced when the flow rate increased from 0.5 to 1.5 kg min-1. However, the PTC fraction values were less than 50% and sometimes have dropped below zero. The exergy efficiency of the PTC improved with increasing the flow rate. The reason was that the difference between the inlet and outlet temperatures of the PTC increased with the flow rate at the similar conditions of solar irradiance and ambient temperature. The highest exergy efficiency of the FPC was observed at the flow rate of 0.5 kg min-1. Conclusion The results of the study revealed that: There was a suitable agreement between the obtained analytical expressions and the experimental data based on root mean square percent deviation and regression coefficient criteria. The highest stored energy in the tank was around 40.02 MJ at the flow rate of 0.5 kg min-1. Increasing the flow rate improved the PTC exergy efficiency.
M. Taki; Y. Ajabshirchi; S. F. Ranjbar; A. Rohani; M. Matloobi
Abstract
Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. Predicting ...
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Introduction Controlling greenhouse microclimate not only influences the growth of plants, but also is critical in the spread of diseases inside the greenhouse. The microclimate parameters were inside air, greenhouse roof and soil temperature, relative humidity and solar radiation intensity. 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 are used for this purpose as a function of the metrological conditions and the parameters of the greenhouse components. Some works were done in past to 2015 year to simulation and predict the inside variables in different greenhouse structures. Usually simulation has a lot of problems to predict the inside climate of greenhouse and the error of simulation is higher in literature. The main objective of this paper is comparison between heat transfer and regression models to evaluate them to predict inside air and roof temperature in a semi-solar greenhouse in Tabriz University. Materials and Methods In this study, a semi-solar greenhouse was designed and constructed at the North-West of Iran in Azerbaijan Province (geographical location of 38°10′ N and 46°18′ E with elevation of 1364 m above the sea level). In this research, shape and orientation of the greenhouse, selected between some greenhouses common shapes and according to receive maximum solar radiation whole the year. Also internal thermal screen and cement north wall was used to store and prevent of heat lost during the cold period of year. So we called this structure, ‘semi-solar’ greenhouse. It was covered with glass (4 mm thickness). It occupies a surface of approximately 15.36 m2 and 26.4 m3. The orientation of this greenhouse was East–West and perpendicular to the direction of the wind prevailing. To measure the temperature and the relative humidity of the air, soil and roof inside and outside the greenhouse, the SHT 11 sensors were used. The accuracy of the measurement of temperature was ±0.4% at 20 °C and the precision measurement of the moisture was ±3% for a clear sky. We used these sensors in soil, on the roof (inside greenhouse) and in the air of greenhouse and outside to measure the temperature and relative humidity. At a 1 m height above the ground outside the greenhouse, we used a pyranometre type TES 1333. Its sensitivity was proportional to the cosine of the incidence angle of the radiation. It is a measure of global radiation of the spectral band solar in the 400–1110 nm. Its measurement accuracy was approximately ±5%. Some heat transfer models used to predict the inside and roof temperature are according to equation (1) and (5): Results and Discussion Results showed that solar radiation on the roof of semi-solar greenhouse was higher after noon so this shape can receive high amounts of solar energy during a day. From statistical point of view, both desired and predicted test data have been analyzed to determine whether there are statistically significant differences between them. The null hypothesis assumes that statistical parameters of both series are equal. P value was used to check each hypothesis. Its threshold value was 0.05. If p value is greater than the threshold, the null hypothesis is then fulfilled. To check the differences between the data series, different tests were performed and p value was calculated for each case. The so called t-test was used to compare the means of both series. It was also assumed that the variance of both samples could be considered equal. The variance was analyzed using the F-test. Here, a normal distribution of samples was assumed. The results showed that the p values for heat model in all 2 statistical factors (Comparison of means, and variance) is lower than regression model and so the heat model did not have a good efficient to predict Tri and Ta. RMSE, MAPE, EF and W factor was calculated for to models. Results showed that heat model cannot predict the inside air and roof temperature compare to regression model. Conclusion This article focused on the application of heat and regression models to predict inside air (Ta) and roof (Tri) temperature of a semi-solar greenhouse in Iran. To show the applicability and superiority of the proposed approach, the measured data of inside air and roof temperature were used. To improve the output, the data was first preprocessed. Results showed that RMSE for heat model to predict Ta and Tri is about 1.58 and 6.56 times higher than this factor for regression model. Also EF and W factor for heat model to predict above factors is about 0.003 and 0.041, 0.013 and 0.220 lower than regression model respectively. We propose to use Artificial Neural Network (ANN) and Genetic Algorithm (GA) to predict inside variables in greenhouses and compare the results with heat and regression models.