Modelling and predictive control techniques for building heating systems

Model predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.

Prevalence and control of Clostridium difficile in patients with cystic fibrosis

The aim of this thesis was to investigate the high prevalence of Clostridium difficile in patients with cystic fibrosis (CF), and to control its dissemination. To determine the carriage rate of C. difficile in CF patients, 60 patients were tested for C. difficile and its toxin. In total, 50% of patients were found to be asymptomatic carriers of C. difficile despite toxin being detected in 31.66% of patients. Ribotyping of the C. difficile isolates revealed 16 distinct ribotypes, including the hyper virulent RT078. All isolates were sensitive to both Vancomycin and Metronidazole. The effect of CF and its treatment on the gut microbiota of CF patients was assessed by 16s sequencing of the gut microbiota of 68 CF patients. When compared to a healthy control group, CF patient gut microbiota was found to be less diverse and had an increased Firmicutes to Bacteriodetes ratio. Interestingly, CF patients who were carriers of C. difficile had a less diverse gut microbiota than C. difficile negative CF patients. Multilocus sequence typing was found to be comparable to PCR-ribotyping for typing C. difficile isolates from high risk patient groups. The sequence type ST 26 is potentially associated with CF patients as all seven isolates were found in this group and this sequence type has been previously reported in CF patients in a geographically distinct study. The bacteriophage ФCD6356 was assessed as a targeted antimicrobial against C. difficile in an ex-vivo model of the human distal colon. Despite reducing viable C. difficile by 1.75 logs over 24 hours, this bacteriophage was not suitable due to its lysogenic nature. Following treatment, all surviving C. difficile were immune to reinfection due to prophage integration. However, the ФCD6356 encoded endolysin was capable of reducing viable C. difficile by 2.9 over 2 hours in vitro after being cloned and expressed in Escherichia coli.

Modelling and control of a floating oscillating water column

A novel numerical model of a Bent Backwards Duct Buoy (BBDB) Oscillating Water Column (OWC) Wave Energy Converter was created based on existing isolated numerical models of the different energy conversion systems utilised by an OWC. The novel aspect of this numerical model is that it incorporates the interdependencies of the different power conversion systems rather than modelling each system individually. This was achieved by accounting for the dynamic aerodynamic damping caused by the changing turbine rotational velocity by recalculating the turbine damping for each simulation sample and applying it via a feedback loop. The accuracy of the model was validated using experimental data collected during the Components for Ocean Renewable Energy Systems (CORES) EU FP-7 project that was tested in Galway Bay, Ireland. During the verification process, it was discovered that the model could also be applied as a valuable tool when troubleshooting device performance. A new turbine was developed and added to a full scale model after being investigated using Computational Fluid Dynamics. The energy storage capacity of the impulse turbine was investigated by modelling the turbine with both high and low inertia and applying three turbine control theories to the turbine using the full scale model. A single Maximum Power Point Tracking algorithm was applied to the low-inertia turbine, while both a fixed and dynamic control algorithm was applied to the high-inertia turbine. These results suggest that the highinertia turbine could be used as a flywheel energy storage device that could help minimize output power variation despite the low operating speed of the impulse turbine. This research identified the importance of applying dynamic turbine damping to a BBDB OWC numerical model, revealed additional value of the model as a device troubleshooting tool, and found that an impulse turbine could be applied as an energy storage system.