AN ANALYTICAL AND DATA DRIVEN ENERGY-WATER- FOOD (EWF) NEXUS APPROACH FOR GREENHOUSE SYSTEMS

Abstract

The global population is increasing exponentially and is expected to reach 9.7 billion in 2050. The increase in population, coupled with resource scarcity and climate change, adversely affects the demand and provision for energy, water, and food resources. Energy, water, and food are critical components that are interconnected, and the security of one can significantly affect the security of the others. The interlinkages among these factors are crucial to address global food security challenges. For arid countries such as Qatar, with high temperatures and scarcity of fresh water, open-field agriculture is an unviable option as the ambient environmental conditions are unfavorable. This has resulted in Qatar relying heavily on imports to meet its food needs, leaving it vulnerable to global food price fluctuations and supply chain disruptions. Therefore, to meet the increasing demand for food, it is necessary to intensify localized food production by developing sustainable, environment-friendly solutions that minimize water and energy consumption and maximize yield. Considering the drive to develop innovative and sustainable food production systems, the first part of this research adopts an analytical modelling approach and analyses renewable energy-powered decentralised greenhouse systems. The systems are designed based on the principles of decentralization within food production systems and sustainability to improve the region's food security. The first integrated system had an overall energy and exergy efficiency of 41.0% and 28.3% respectively while providing the requirements for food production in a sustainable manner. The second system produced a space cooling of 695 kW, 17.5-27.3 m3/day water and 1.03 MW of electricity while providing optimum temperature and humidity to the plants. The third system produced 4.03-5.20 m3/day and 230.95 kW space cooling. The integrated subsystems provided the requirements such as electricity, heating, space cooling, and freshwater, while the greenhouse unit provides optimum growing conditions to the plants throughout the year, thus making sustainable agriculture possible in arid climates. The proposed systems are adaptable and can be scaled up or down based on the crop requirement. The second part of the research focuses on improving the performance of the existing food production systems. Within food systems, agricultural greenhouses provide a closed and controlled environment with optimized growing conditions; however, greenhouses consume more resources than other commercial buildings due to their inefficient design and operation. Therefore, a systematic model-based approach is required to control and optimize greenhouse operations. In terms of system modelling, data-driven models can offer better performance, scalability, flexibility, and accuracy as compared to conventional approaches. Therefore, a data-driven modelling approach is adopted as different models are evaluated for the prediction performance, and the most accurate one is used as a system model. Furthermore, using the data-driven model, advanced control methods such as model predictive control and robust model predictive control are developed and applied to the greenhouse to evaluate energy consumption and temperature control. The performance of the proposed control strategies is compared to the greenhouse's existing climate management control system. The model predictive control strategy led to an energy reduction of 7.70% and 16.57% for a two-day simulation period in winter and summer, respectively as compared to the existing greenhouse system. Similarly in the presence of system uncertainties, the performance of the greenhouse system and model predictive control strategy dropped while the robust model predictive control had a superior temperature control and reduced the energy utilisation by of 9.67% and 23.61% in winter and summer, respectively. In addition, the proposed control frameworks are flexible and can be applied to greenhouses in other geographical locations by tuning the model on the new data set. The proposed research is expected to contribute to the United Nation’s sustainable development goals and Qatar's national vision of 2030 by providing sustainable and local food production solutions.

Date of Award	2023
Original language	American English
Awarding Institution	HBKU College of Science and Engineering