Ongeveer 22 uur geleden - Penta Nova - Tilburg
We are seeking two or three part-time Postdoctoral Research Assistants in Public Administration to join the GovernanceLab of TIAS. The posts are funded by the …
The Dutch horticultural sector aims at becoming climate neutral before 2050. To accomplish this, a substantial energy saving is needed. For a successful …
The Dutch horticultural sector aims at becoming climate neutral before 2050. To accomplish this, a substantial energy saving is needed. For a successful transition to clean energy this should be accompanied with reducing the peaks in energy demand (especially during cold days). This project aims to develop automated decision support for climate management in greenhouses, by employing available streams of momentary and historical data (on crop, weather, energy prices, inside climate, energy buffering), combined with predictive models and decision algorithms.
In particular, this project aims to reduce a substantial amount of energy by employing the robustness of crops towards temporary indoor climate fluctuations. Currently, decision support is given on climate and energy management separately. Since climate and energy are interconnected, energy and climate advice should be integrated. Moreover, prognoses on weather and energy availability are uncertain. Managing under uncertainty requires a different approach than what is currently used.
Your challenge is to design a control algorithm that forms the core of a decision support system. The resulting decision support integrates three aspects: crop control, climate control, and energy provision and purchase. One part of your research is to develop a control algorithm that performs robustly under uncertainty, and one part of your research will be to further develop existing models for crop development, greenhouse climate dynamics, and energy provision, and integrate them.
This project hosts two PhD candidates. You will collaborate with a candidate who works on a crop physiological model that describes growth dynamics of a tomato crop, and its robustness towards climate fluctuations.
The project is funded by NWO-STW and conducted in cooperation with the funding agency Commit2Data and the consortium companies and institutes LTO Glaskracht, AgroEnergy, Delphy, Letsgrow.com, B-Mex, and Wageningen Plant Research. You will collaborate with the consortium partners for mutual advice, provision of data, and for future application of your algorithms within horticultural practice. Intensive coaching and supervision will be provided by experts in mathematics, control theory, crop physiology, and greenhouse technology. As a starting point of your research, basic models on crop development, climate dynamics, and energy flow, as well as control algorithms, are readily available.