Abstract
dc:description.abstractAs cities adapt and evolve to future climatic, demographic and economic conditions, urban integrated agriculture (UIA) is a promising solution to increase urban resource use efficiency, improve local food supply and population well-being. In particular, Controlled Environment Agriculture (CEA) offers the potential to compete with traditional agriculture through its large annual yields on small footprints. Whilst smart control systems are well developed to optimize CEA, there is no sufficient body of evidence to encourage widespread adaptation of CEA in urban environments. Furthermore, there are no methods to evaluate and optimise the integration of CEA with the urban built environment synergistically. This thesis investigates methods and models that can facilitate new and bespoke modes of urban and building integrated agriculture in cities. To this end, the thesis presents digital twins as a suitable framework that encompasses real time monitoring, data curation, and bespoke modelling, in a virtual representation of the actual system. Digital twins can meet the bespoke monitoring and modelling requirements of urban integrated agriculture and be utilised to optimise the farm environment whilst minimising their energy burden. The thesis develops this hypothesis through an `in-vitro' exercise of developing a digital twin of an existing and fully operational underground farm in London (UK) called Growing Underground. This, in the first phase, entailed designing and installing a real-time monitoring network 33~m underground, as well as developing a suitable framework to ensure the translation of monitored data into information that is tractable and usable. In total, 89 different variables are monitored and tracked in the farm, including both features that are observable through the sensing network and those that have to be recorded manually. As demonstrated in this thesis, the development of a usable digital twin necessitates the synthesis of data generation and data analysis. The data tracked through the monitoring network is analysed in order to identify the unique relationships between the operational controls, environmental conditions and crop growth. Results show the importance of controlling air temperatures within an optimal range, and that ventilation and lighting are the key drivers influencing the farm temperatures. Thus, a forecasting model is developed to provide feedback on farm temperatures as a function of changing operational conditions. Data-centric models are thus shown to be effective for managing the farm environment efficiently. However, they are limited to environments that are already operational. The design of new and creative practices in urban-integrated architecture can only be supported through physics-based numerical models. A co-simulation methodology is developed to integrate a validated CEA model into conventional building energy simulation (BES). The model is tested on a hypothetical greenhouse on the top floor of an archetypal school building in London, due to the increasing interest of food growing in schools. This method enables the evaluation of the potential crop growth, heat recovery and reduction in ventilation demand for different levels of greenhouse-building coupling through a sensitivity analysis and parametric study. The increase in humidity due to large planted areas shows the importance of incorporating detailed crop growth and thermodynamics in BES, in order to identify resource trade-offs and ensure optimal designs.
Degree
thesis:*- Name dc:type.qualificationname
- Doctor of Philosophy (PhD)
- Level dc:type.qualificationlevel
- Doctoral
- Grantor dc:publisher.institution
- University of Cambridge
- Year dc:date.issued
- 2020
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Jans-Singh, Melanie
- Advisor dc:contributor.advisor
-
- Choudhary, Ruchi
Subjects
dc:subject × 6Rights
dc:rightsIdentifiers
dc:identifier.*- Author Identifier
- 0000-0002-2345-0747
- OAI identifier oai:identifier
- oai:www.repository.cam.ac.uk:1810/321256