J.A. Sánchez-Molina

A hybrid-controlled approach for maintaining nocturnal greenhouse temperature

Display Omitted A greenhouse temperature hybrid model is developed based on linear reduced models.An HMPC is designed to control greenhouse temperature using two heating systems.Simulations results show that the HMPC framework is suitable to solve the greenhouse heating problem. Forced-air heaters and aerial pipe systems are the most common heating equipment used in greenhouses to control the nocturnal temperature in Mediterranean areas. These heating systems are often used separately and they are seldom combined in the same greenhouse for temperature control purposes. The main reasons are that the advantage of combining both heating systems has not been thoroughly analysed in literature, and that, the complexity of the problem increases from a control point of view due to the mixing of different dynamics. The combination of these two heating systems can be useful in some situations, obtaining a reasonable trade-off between thermal gain and running costs. Thus, this paper proposes to analyse the combination of these two heating systems and provide a solution to the problem of switching two different heating systems to control the nocturnal temperature in a greenhouse by using a hybrid controller. The proposed controller counteracts the switching disadvantages presented by commercial systems based on heuristic rules. To achieve this solution, the system dynamics are represented through a hybrid model, where weather variables act as logical conditions to switch between the different process dynamics. This approach allows considering the greenhouse dynamics as a hybrid system with continuous and discrete components. A Model Predictive Hybrid Controller is used to regulate the inner temperature during the night and calculates optimal control signals based on power consumption and commutation minimisation. The performance of this controller is studied, comparing its reference deviation, number of commutations, and running costs against commercial controllers. The final results show that the adequate combination of these heating systems can contribute to a much better control performance with a minor cost increment.

A risk management system for meteorological disasters of solar greenhouse vegetables

Solar greenhouses are well-established and very popular in the north of China as a way of meeting the demand for fresh local winter vegetables. Nonetheless, they are more susceptible to meteorological disasters, such as fog, haze and cold temperatures. A meteorological risk management system that includes disaster forecasting and control is a useful tool to efficiently capture long-term and up-to-the-minute environmental fluctuations inside greenhouses. Based on the concept of the meteorological disaster warning model, this study has developed a meteorological risk management system built upon a browser/server framework and mobile internet to provide precision agriculture (PA) services with large-scale, long-term, scalable and real-time data collection capabilities for solar greenhouse vegetables. Early warning indicators were established for the main meteorological hazards to winter-spring vegetables in solar greenhouses, including low temperature and sparse sunlight, downy mildew, grey mildew and powdery mildew induced by unfavorable meteorological conditions. The system could provide a valuable framework for farmers and agrometeorological officials in analyzing the relationships between vegetable damage dynamics and meteorological events. Having been applied in Beijing and Tianjin, the system has correctly forecast meteorological disaster and diseases caused by long-term fog and haze from November 2015. Based on the analysis carried out, improved meteorological risk management and a more accurate decision-making strategy can be developed to assist PA in combating meteorological disaster.