The Study Of Ventilation Rate And Temperature Prediction In Open-roof Greenhouse In Summer

Greenhouse, as a Production facility which can improve the condition of crop growth and optimize the growth environment in agricultural production. As the special structure of the new type greenhouse, the top window of the Open-Roof greenhouse can be fully opened, it can maximize the use of natural ventilation in summer for cooling which has great significance. Ventilation rate and temperature are important factors to affect the environment in the greenhouse, and the research can provide theoretical basis for the regulation of greenhouse.This paper took the Open-Roof greenhouse as the investigated subject. The model of ventilation rate and the dynamic model include indoor temperature and covering layer temperature were established which based on unsteady energy & mass balance equation, the field experiment and simulation method were combined, the multiple liner regressive model, BP neural network model and dynamic model were contrasted to predict the temperature. The ventilation rate was calculated based on experimental data, the related influencing factors were analyzed, part of greenhouse control system were established to provide theoretical basis for further regulation of greenhouses. The main content and results of this test was concluded as followed:(1) The theoretical analysis of ventilation rate in Open-Roof greenhouse which based on simple unsteady energy & mass balance mode were established, the net solar radiation, convection exchange between the outer surface of the cover and the outside ambient air the thermal radiation exchange between the outer surface of cover and sky, and the conducted heat into the floor were theoretical analyzed, the model of natural ventilation rate under low wind speed in Open-Roof greenhouse in summer was deduced, the temperature prediction model of greenhouse with energy exchange inside and outside the greenhouse was established. The MCGS configuration software of the Open-Roof greenhouse monitoring system was designed, and the dual alarm was set up for the lower limit of ventilation and the upper limit of the temperature.(2) The greenhouse in Zhonghua village, Shanghai Jinshan was taken as investigated subject, the temperature sensors, were used to record air temperature and relative humidity inside the greenhouse, the temperature of covering layer and the temperature of floor; the environmental data recorder was used to record the air temperature and humidity, wind speed and radiation outside the greenhouse; During the experiment of ventilation rate, the shading was closed, the maximum temperature of air was 61.1℃ and the minimum temperature of relative humidity was 21% in the greenhouse. The temperature of floor was lower than the temperature of covering layer and inside air temperature, the temperature of covering layer was higher than air temperature outside the greenhouse but lower than air temperature inside the greenhouse; During the experiment that predicted the temperature in the greenhouse, the shading was opened, the maximum value of radiation was 940 W/m2 at 12:00-14:00. There was no obvious law of wind speed change, the maximum value of the wind speed was 2m/s, the average wind speed was 1.1m/s.(3) On the basis of experimental data, the measured values of environmental factors as input, the dynamic model of temperature was used to predict the air temperatures and temperature of covering layer in the greenhouse; The multiple linear regression equation was established to predict the air temperatures and temperature of covering layer in the greenhouse; The BP neural network was constructed based on the measured environmental factor as input, after training, validation and testing, the neural network model was determined to predict the air temperatures and temperature of covering layer in the greenhouse. The results showed that the three kinds of prediction models had some predictive effect of air temperature and temperature of covering layer in the greenhouse, the dynamic model was better than the multiple linear regression for predicting the temperature, the dynamic model was more precise than BP neural network for predicting the temperature.(4) In accordance with the ventilation rate model and the experimental data, the greenhouse ventilation rate was calculated which was 0.0920 kg·s-1·m-2,the average value was 0.0752 kg·s-1·m-2. Compared with other models, the model was more suitable for the practical use of Open-Roof greenhouse in summer under low wind speed conditions. The relationship between ventilation rate and temperature difference inside and outside the greenhouse was analyzed, and the curve fitting was carried out.