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.
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.
