Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

8th Annual Jackson School of Geosciences Student Research Symposium, February 2, 2019

ConocoPhillipsGeological Science

Theoretical Analysis and Experimental Investigation of Simulated Moving Bed Chromatography for the Purification of Protein Mixtures

Stepwise-Elution Simulated Moving Bed Chromatography (SE-SMB) is a promising method for ‘intensification’ of polishing chromatographic processes in downstream bioprocessing. This is because SE-SMB systems are continuous, capable of high-resolution separations, efficient in their utilization of chromatographic resins, well-suited to non-isocratic proteinaceous separation problems operated under high feed-loading conditions, and highly productive. However, there are a number of theoretical and practical problems which have impeded industrial interest in the adoption of SE-SMB separations into downstream processes. Fundamental phenomena, such as the modulator dynamics of SE-SMB systems, have yet to be theoretically analysed. Consequently, important practical questions – such as how productive and high-resolution separations may be best achieved through SE-SMB systems – remain unanswered. Furthermore, the complexity and operational fragility of SE-SMB systems require much improvement in their ‘robustness’ before any consideration of their application to industrial purification of therapeutic proteins may be entertained. This thesis constitutes an initial investigation of the theoretical and practical issues which arise concerning the application of SE-SMB to industrial bioseparations. Regarding the theoretical issues, an analysis of modulator dynamics in SE-SMB systems is presented. This provides new insights into how such systems – both for binary and ternary separations - should be designed for productive and robust operations. Furthermore, the behaviour of SE-SMB systems under high feedloading conditions is also investigated. Regarding practical issues, experimental SMB separations of a challenging proteinaceous mixture are demonstrated, and simulated comparisons are used to investigate the comparative performance of various intensified processes. Finally, an exploration of SE-SMB fault detection and diagnosis methods is undertaken. The results suggest that SE-SMB chromatography may be ‘de-risked’ to such an extent that, with future development, it becomes an attractive option for incorporation into industrial bioprocesses

Global energy minimisation of arterial trees with application to embolic stroke

Computer generation of optimal arterial trees has previously been limited to the production of locally optimal configurations. The application of a global optimisation algorithm allows for the generation of vasculatures with consistent structure. Comparison of this structure to that of in-vivo vasculatures allows the determination of to what extent the vascular structure is the result of energy minimisation. In this thesis an algorithm capable of generation globally optimal vascular trees in geometries derived from medical imaging is developed. We begin by outlining a small set of constraints which capture physiological principles guiding the organisation of arterial trees. The constraints are then used to produce an algorithm capable of finding the minimal energy configuration of a given arterial tree. The algorithm is used to produce both coronary and cerebral vasculature, and the latter is generated in geometries segmented from MRI data of a human brain. The trees are compared both morphologically and structurally to those found in-vivo. The morphological comparisons for the coronary vasculature show excellent agreement with experiment. The positions of the larger coronary arteries in the generated trees agree extremely well with experiment, suggesting that structure of the coronary vasculature is the result of energy minimisation. The generated cerebral vasculature approximates the vascular territories of the major cerebral arteries, however the morphological comparisons show that the structure of the cerebral arteries is likely not the result of energy minimisation. The cerebral vasculatures is used to extend a statistical model of embolic stroke to include the effects of branching asymmetry, and an analytic approximation to the statistical model of embolic stroke is developed and validated. It is found that branching asymmetry produces an overall reduction in the level of blockage occuring during an embolic event