PhD Position Learning and Control for Complex Large-Scale Systems with Applications in Greenhouses

PhD Position Learning and Control for Complex Large-Scale Systems with Applications in Greenhouses

Recent trends in Controlled Environment Agriculture (CEA) development, design, and operations related to energy saving and greenhouse gas emission reduction, are mainly focused on the ventilation process, which typically considers the use of window and mechanical air treatment based on average climate measurements. Airflow affects crop transpiration, growth, development, yield and quality, but despite its importance, related control strategies in practice are often very crude and rule-based without incorporating any complex plant/microclimate interactions or economic considerations.

The state-of-the-art approaches in optimal climate control of greenhouses are based on implementing economic objective functions exploiting a time scale decomposition between short-term climate control/energy use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use of average climate measurements, which are then used to control the overall climate setpoints.

Awareness of micro-climate insight and fine-grained, model-based control of locally applied ventilation is lacking in all these approaches.

This PhD position aims at developing methods for learning and control in complex large-scale systems. This will be carried out as part of the Green Control project, whose primary objective is to address the above mentioned shortcomings in autonomous greenhouse control. The project team includes PhD students and researchers at TU Delft, Wageningen University, University of Twente, and TU Eindhoven, as well as industrial partners that specialize in greenhouse design and installation, plant breeding, climate control, sensing and monitoring with microdevices, software developers, and technology providers for high-tech greenhouses.

The goal of the Green Control project is to collaborate with this team to support the transition to climate neutrality in CEA by using a completely new operating philosophy that puts plants at the center of the control strategy to achieve 25% energy savings and 35% reduction in energy costs. We will accomplish this by moving from average climate control to direct crop-centric control.

This paradigm shift relies on breakthroughs in microclimate sensing, interpreting crop performance by integrating sensor data at different temporal and spatial scales into a crop modelling framework and predicting daily targets for photosynthesis and transpiration rates (all developed by other researchers in the project consortium). Based on these targets and fluctuating electricity prices, the main objective will be to develop a control-oriented model and algorithm to alter the lighting, CO2 dosing, and air circulation that satisfy the crops' needs, while minimizing the resources used and costs.

The developed control scenarios will investigate targets with increasing complexity, i.e., daily respiration target, daily photosynthesis target, cost and energy use optimization, and will be improved iteratively culminating in validation trials and experiments.

In the Green Control project, the primary aim is to demonstrate the added value of being able to capture such microclimate effects, via new sensing and modelling approaches developed by our partners, and adjust the control strategies to improve photosynthesis efficiency while reducing overall energy use. Research by our project partners will show how crops respond to microclimate setpoints based on detailed sensing data, and microclimate and crop models, obtained both through mechanistic and CFD-based approaches.

These provide the basis for learning reduced complexity control-oriented models that will be exploited by two control strategies:

• Airflow control by forced convection to improve photosynthesis efficiency by CO2 delivery to the leaf surface and to couple the crop and climate regulation more tightly;
• Energy‑saving strategies where the application of lighting, active ventilation and heating are optimized based on plant…