A Compatible Control Algorithm for Greenhouse Environment Control Based on MOCC Strategy

Conventional methods used for solving greenhouse environment multi-objective conflict control problems lay excessive emphasis on control performance and have inadequate consideration for both energy consumption and special requirements for plant growth. The resulting solution will cause higher energy cost. However, during the long period of work and practice, we find that it may be more reasonable to adopt interval or region control objectives instead of point control objectives. In this paper, we propose a modified compatible control algorithm, and employ Multi-Objective Compatible Control (MOCC) strategy and an extant greenhouse model to achieve greenhouse climate control based on feedback control architecture. A series of simulation experiments through various comparative studies are presented to validate the feasibility of the proposed algorithm. The results are encouraging and suggest the energy-saving application to real-world engineering problems in greenhouse production. It may be valuable and helpful to formulate environmental control strategies, and to achieve high control precision and low energy cost for real-world engineering application in greenhouse production. Moreover, the proposed approach has also potential to be useful for other practical control optimization problems with the features like the greenhouse environment control system

Optimal greenhouse cultivation control: survey and perspectives

Abstract: A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well

Courting Catastrophe? Humanitarian Policy and Practice in a Changing Climate

Humanitarian crises appear dramatic, overwhelming and sudden, with aid required immediately to save lives. Whereas climate change is about changing hazard patterns and crises are in reality rarely unexpected, with academic researchers and humanitarian and development organisations warning about possible risks for months before they take place. While humanitarian organisations deal directly with vulnerable populations, interventions are part of global politics and development pathways that are simultaneously generating climate change, inequities and vulnerability. So what is the level of convergence between humanitarian interventions and efforts to support adaptation to climate change, and what lessons can be drawn from current experience on the prospects for reducing the risk of climate change causing increased burdens on humanitarian interventions in the future? This IDS Bulletin is a call for increasing engagement between humanitarian aid and adaptation interventions to support deliberate transformation of development pathways. Based on studies from the ‘Courting Catastrophe’ project, contributors argue that humanitarian interventions offer opportunities for a common agenda to drive transformational adaptation. Changes in political and financial frameworks are needed to facilitate longer-term actions where demands move from delivering expert advice and solutions to vulnerable populations to taking up multiple vulnerability knowledges and making space for contestation of current development thinking. Yet while the humanitarian system could drive transformative adaptation, it should not bear responsibility alone. In this issue, alternative pathways and practical ways to support local alternatives and critical debates around these are illustrated, to demonstrate where humanitarian actions can most usefully contribute to transformation

Hierarchical model predictive control of a venlo-type greenhouse

Greenhouse cultivation can increase crop yield and alleviate the food shortage caused by population growth and reduction of arable land. However, the greenhouse production process consumes lots of energy and water. The energy consumed mainly comes from the combustion of fossil fuels, which will produce lots of greenhouse gases. In addition, the operating efficiency of some greenhouses is low, resulting in energy and water waste and increasing production costs. Therefore, the greenhouse system needs to be optimized to improve the operating efficiency. In this thesis, different methods of greenhouse operation efficiency optimization to improve energy efficiency and water efficiency are studied. In Chapter 3, three strategies for greenhouse operation optimization are studied. Strategy 1 focuses on the optimization of the greenhouse heating system to save energy. The optimization of the heating system can effectively reduce energy consumption. However, people often pay more attention to reducing energy costs than reducing energy consumption in the production process to obtain more profits. Strategy 2 is to reduce the energy cost. It should be noted that Strategy 2 only considers the cost of heating and cooling, while the cost of ventilation and carbon dioxide (CO2) is not considered. Strategy 3 reduces the cost of greenhouse heating, cooling, ventilation and CO2 consumption. In addition, greenhouse environmental factors must be kept within the required ranges. In Chapter 3, a dynamic greenhouse climate model is proposed. In the modeling process, the influence of crop growth and the interaction between different variables are considered to improve model accuracy. The proposed optimization problems are solved by ‘fmincon’ function with sequential quadratic programming (SQP) algorithm in MATLAB. Compared with Strategy 1, Strategy 2 has higher energy consumption but lower energy cost. Because Strategy 2 can shift some loads from high electricity price period to low electricity price period. Moreover, among the three strategies proposed, Strategy 3 has the lowest cost. It should be pointed out that the strategies studied in Chapter 3 only consider the impact of the greenhouse climate, but ignore the irrigation, which is also important for greenhouse production. In Chapter 4, four optimization methods are proposed. These optimization methods consider climate control and irrigation control. Therefore, strategies proposed in this chapter can not only improve energy efficiency, but also increase water efficiency. Method 1 reduces the energy consumption. Method 2 reduces the water consumption. Method 3 reduces the CO2 consumption. Method 4 reduces the total cost of greenhouse heating, cooling, ventilation, irrigation and CO2 supply. In addition, greenhouse environmental factors and crop water demand need to be met. The dynamic model of greenhouse environmental factors presented in Chapter 3 is used for greenhouse climate control. A modified crop evapotranspiration model is proposed to predict crop water demand. Moreover, a sensitivity analysis method is introduced. The influence of prices and system constraints on optimization results is studied. The cost of Method 4 can be reduced compared with other methods. In addition, changes of prices and system constraints have a great impact on optimization results. In Chapters 3 and 4, open loop optimization strategies for a greenhouse system operation are studied. However, these strategies have low control accuracy under system disturbances. Therefore, it is necessary to adopt some control methods to improve the control accuracy. In Chapter 5, a hierarchical model predictive control method is presented. The upper layer generates the optimal reference trajectories by solving greenhouse operation optimization problems. The lower layer designs controllers to follow obtained reference trajectories. Two model predictive controllers (MPC) are designed. Two performance indicators, namely relative average deviation (RAD) and maximum relative deviation (MRD), are used to compare designed controllers. The simulation results show that the proposed MPC can deal with greenhouse system disturbances and the problem of model plant mismatch better than the open loop control method. In Chapter 6, the findings of this thesis are summarized. Moreover, some topics for future research are proposed.Thesis (PhD (Electrical Engineering))--University of Pretoria, 2021.Electrical, Electronic and Computer EngineeringPhD (Electrical Engineering)Unrestricte

Sustainable development, poverty eradication and reducing inequalities

This chapter takes sustainable development as the starting point and focus for analysis. It considers the broad and multifaceted bi-directional interplay between sustainable development, including its focus on eradicating poverty and reducing inequality in their multidimensional aspects, and climate actions in a 1.5°C warmer world. These fundamental connections are embedded in the Sustainable Development Goals (SDGs). The chapter also examines synergies and trade-offs of adaptation and mitigation options with sustainable development and the SDGs and offers insights into possible pathways, especially climate-resilient development pathways towards a 1.5°C warmer world