Process optimization under uncertainty

The ability of a production plant to be flexible by adjusting the operating conditions to changing demands, prices of the products and the raw materials is crucial to maintain a profitable operation. In this respect, the application of mathematical optimization techniques is unanimously recognized to be successful to improve the decision-making process. Typical examples are production planning, scheduling, real-time optimization and advanced process control. The more information are available to the optimization approach, the more "optimal" are the resulting decisions: the "optimal" production strategy cannot reduce the inventory costs if no supply-chain model is integrated into the production planning optimization. This thesis lies in the context of Enterprise-wide optimization with the goal of integrating decision layers and functions while accounting for uncertain information. A stochastic programming approach is adopted to integrate production scheduling with energy management and production planning with predictive maintenance. The approaches are analysed from a formulation perspective and from a computational point of view, which is necessary to deal with one of the challenges of the presented methods consisting in the size of the resulting optimization problems. To reduce the electricity cost that is generated by the uncertain peaks of the dayahead price, a two-stage risk-averse optimization is proposed to simultaneously define the optimal bidding curves for the day-ahead market and the optimal production schedule. The large-scale MILP problem is solved with a scenario-based decomposition technique, the progressive hedging algorithm. Heuristic procedures are applied to speed up the solution phase and to avoid the oscillatory behaviour due to the integer variables. Since large electricity consumers rely on Time-Of-Use power contracts to handle the volatility of the day-ahead price, the two-stage formulation is expanded into a multi-stage optimization to optimally purchase electricity from different sources and to generate electric power with a power plant. The unpractical size of the resulting problem is handled by approximating the multi-stage tree with a series of two-stage scenario-trees within a rolling horizon procedure. A mixed time grid handles the multi-scale nature of the problem by making short-term decisions with a detailed model and catching their effect on the long-term future with an aggregated model. While the electricity prices introduce exogenous uncertain information into the optimization problem, the predictive maintenance optimization carries endogenous uncertain sources into the production planning problem. Endogenous uncertainties, contrary to the exogenous ones, are uncertain information that can be modified (in the probability or in the timing of the realization) by the decision maker. The prognosis technique of the Cox model is embedded into a multi-stage stochastic program to consider an uncertain Remaining Useful Life of the equipment when the optimal operating conditions of the plant are defined. Two modelling approaches (based on superstructure-scenario trees and on conditional non-anticipativity constraints) are proposed to formulate the optimization problem with endogenous uncertainties. Two Benders-like decomposition techniques and several branching priority schemes are applied to handle the high complexity of the resulting optimization problems

Hyperpolarized 129Xe Magnetic Resonance Imaging of Radiation-Induced Lung Injury

Lung cancer is the largest contributor to cancer-related mortality worldwide. Only 20% of stage III non-small cell lung cancer patients survive after 5-years post radiation therapy (RT). Although RT is an important treatment modality for lung cancer, it is limited by Radiation-Induced Lung Injury (RILI). RILI develops in two phases: (i) the early phase (days-weeks) referred to radiation pneumonitis (RP), and (ii) the late phase (months). There is a strong interest in early detection of RP using imaging to improve outcomes of RT for lung cancer. This thesis describes a promising approach based on 129Xe gas as a contrast agent for Magnetic Resonance Imaging (MRI) of the lung airspace due to the large increase in signal possible by spin exchange optical pumping, or hyperpolarization (Hp). Additionally, 129Xe provides unique functional information due to its relatively high solubility and significant chemical shift in pulmonary tissue (PT) and red blood cell (RBC) compartments. In this thesis, a specialized Hp 129Xe MRI method was developed for detection of gas exchange abnormalities in the lungs associated with thoracic RT. In particular, the feasibility of quantifying the early phase of RILI is demonstrated in a rat model of RILI two weeks post-irradiation with a single fraction dose of 18 Gy. The challenge of low signal-to-noise ratio (SNR) in the dissolved phases was addressed in this work by development and construction of a Transmit-Only/Receive-Only radiofrequency coil. Another challenge addressed in the thesis was the lack of imaging techniques that provide sufficient spatial and temporal information for gas exchange. Therefore, a novel Hp 129Xe MRI technique was developed based on the multi-point IDEAL pulse sequence. The combination of these two developments enabled investigation of regional gas exchange changes associated with RP in the rat lung two weeks post-irradiation to assess the feasibility of early detection of RILI. Theoretical analysis of the gas exchange curves enabled measurements of average PT thickness (LPT) increases consistent with histology and relative blood volume (VRBC) reductions in the irradiated animal cohort compared to a non-irradiated cohort, and between irradiated right lungs compared to unirradiated left lungs in the irradiated cohort

Empirical investigation of global wildfire drivers and development of a new flammability parametrisation for the INFERNO fire model

Wildfires have a significant impact on the Earth’s vegetation, atmospheric composition, and climate. There is also growing evidence that fire behaviour has already been altered in response to climate change. Given the anticipated increase in climate conditions conducive to wildfires in many regions of the world, there is an urgent need to advance our understanding of the drivers of wildfires and improve their representation in global Earth system models. Recognising and predicting such responses to climate change and the associated feedbacks is one of the key challenges of the field, and integrated vegetation–fire models have the potential to accomplish this goal. However, the relationship between wildfire activity and past vegetation productivity is still unclear. It has been hypothesised that this strongly contributes to the poor performance of state-of-the-art fire models in representing observed vegetation–fire relationships. Thus, this thesis examines the role of seasonal and long-term vegetation dynamics (and fuel accumulation dynamics) that are of fundamental importance for global wildfires due to their impact on fuel availability during the fire season. In addition to instantaneous climatic conditions, the seasonality of antecedent vegetation and climate conditions controlling fuel build-up and fuel drying was found to be important for the prediction of burnt area in an empirical analysis of wildfire drivers. These were then used to modify the vegetation parametrisation of the INFERNO fire model. By using sigmoidal relationships to explicitly consider antecedent vegetation and climate conditions, global performance was improved. Finally, the new model was evaluated using sensitivity analyses, demonstrating the overarching importance of dryness and temperature while also disentangling the different impacts model parameters have on the magnitude and phase performance of the new parametrisation.Open Acces

The essential nature of on-the-job thinking: A phenomenological study of health and fitness professionals engaged in learning experiences.

For as long as learning is considered to include a cognitive element, then questions about how, and indeed, why, we think, remain crucial considerations for stakeholders in education, learning, and professional development. This study explores thinking in the specific context of on-the-job learning, or in other words, the essential nature of on-the-job thinking. Research generally portrays on-the-job learning, and the thinking assumed to take place therein, as an increasingly complex and poorly understood process. Beginning from a position rooted in health and fitness sector-specific research, and subsequently venturing into the wider landscape of fundamental theories in education and learning, a review of literature identifies the tendency for on-the-job learning to occur predominantly tacitly, as a main contributing factor to an evident impasse in our attempts to understand or study it further. The review subsequently traces this tacit-ness problem to its roots in cognitive science, or more specifically, in dual process theories which depict thinking as an action that is either conscious or unconscious (tacit). Despite a clear juxtaposition of doing and thinking, and the apportioning of comparative importance to the two, theories and models in education and learning seeking to expound the learning process, typically rely on definitions of thinking that are unclear or inconsistent, and fundamental concepts, typically originating from cognitive science, that are obscure and/or paradoxical. It is argued, therefore, that in order to further our understanding of on-the-job learning, a clearer and more robust definition of thinking is warranted, as an alternative theoretical foundation for modern education and learning theories, based not solely on explanations derived from cognitive science, but also on descriptions derived from more philosophical endeavours, namely, phenomenology. Following a deep and reflective phenomenological analysis of personal fitness trainers' accounts of their onthe- job thinking, using modern as well as classical phenomenological methods, the study aims to, first, uncover a re-conceptualised and less problematic description of on-the-job thinking, and second, to evaluate the actual implications of such a reconceptualisation. The description that results, which is also presented in the text as an analogy, casts light on the centrality of feelings, as well as concepts either general or pertaining to self, as key influential factors guiding on-the-job learning outcomes, portrays on-the-job thinking as an integrated activity that is not isolated or separated from interaction with self, other, or the world, and finally, challenges traditional conceptualisations of thinking in light of the challenging notions of conscious awareness and volition. In so doing, the results of this study provide an alternative view of thinking in the context of on-the-job learning by personal fitness trainers, or indeed other professionals, from a conceptual/theoretical standpoint, while also revealing specific features of the phenomenon with immediate and more practical applications as prospective constituents of existing initiatives or interventions designed to facilitate and enhance on-the-job learning in the health and fitness sector, and perhaps further afield

The effect of changing climate and vegetation on the topographic evolution of catchments in the Chilean Coastal Cordillera over millennial timescales

The complex interplay between climate change, the composition and density of surface vegetation cover and physical surface processes has been a focus of scientific research in the field of geomorphology for the last few decades. The classical approach for explaining differences in topography only considers the influence of tectonic processes and lithological material constants as endogenic forcings and the effect of precipitation as an exogenic forcing, where the stabilizing effect of vegetation cover has mostly been neglected. To develop a more complete view of the complex topographic system and incorporate the dynamics between climate and vegetation, a numerical model framework consisting of a dynamic vegetation model (LPJ-GUESS) and a landscape evolution model (Landlab) was developed and tested against four different study areas situated in the Chilean Coastal Cordillera on the basis of‚ available paleoclimate data. These study areas were chosen based on homogeneous endogenic forcings, with a comparable tectonic uplift rate and the same granodioritic lithology, all of which allow for better parameterization of surface processes. Three main simulation experiments were conducted: 1. LPJ-GUESS simulations, which gave insight about large-scale dynamic adjustments of vegetation cover and composition for the respective climate zones. 2. Landlab simulations which were designed to reproduce steady-state topographic metrics observed in the focus areas and to determine the reaction of the topographic system to transient, external changes in precipitation or vegetation cover 3. Coupled simulations with direct feedback during model-runtime between LPJ-GUESS and Landlab for the timeperiod since the last glacial maximum (21ka before present) to present day. The experiments show a complex reaction of both vegetation and topography to climatic forcings, with absolute changes in vegetation cover not exceeding 10%, but large-magnitude adjustments of plant composition due to changes in climate. Simulations show magnitudes and time-scales of adjustment are highly dependent on initial catchment vegetation cover and amount of annual precipitation received. Coupled simulations show large short-term variations in catchment-mean erosion rates for very arid and very humid focus areas, while areas with mediterranean climate show lesser magnitude fluctuations but a more pronounced long-term decrease in mean erosion rates. In summary, this thesis helps in understanding the complex climate-erosion interactions by showing the nature of the threshold-controlled system which governs the reactions of topography to natural changes in climate or vegetation cover

Exploring Diatoms Functional and Taxonomic Diversity on a Global Scale Through an Integrative Approach

Diatoms are a fundamental component of the oceanic ecosystem. Because of the massive primary production they are responsible for they play a pivotal role in several biogeochemical cycles as well as in the marine food web. This high relevance is due to their global distribution together with their seasonal dominance in many of the planktonic communities. This ecological ‘success’ is granted by their diversity, being diatoms the most diverse microalgae taxa. Notwithstanding the relevant role of this taxa, little is known about their biology and ecology, given the lack of large scale observations and the large number of uncultured species. Here I describe global scale diatom diversity exploring in parallel their taxonomic and their functional diversity. Different methodological frameworks were developed to measure both classes of diversity from meta-omic data. As results, herein it is described the first assessment of diatom taxonomic richness at a global scale together with its statistical modeling using a machine-learning approach. Moreover, a completely new approach to characterize the functional diversity is provided. It is based on the phylogenies of nitrogen transporter marker gene families that proved to be optimal markers of functional traits such as size and resource utilization traits. Finally, the outcome of a numerical modeling exercise was compared to omic taxonomic data with the aim of improving the diatom model types. The whole work has been developed exploiting the unprecedented amount of data provided by the Tara Oceans expedition

Functional pulmonary MRI with ultra-fast steady-state free precession

To date, computed tomography and nuclear medicine techniques are still the reference standard for lung imaging, but radiation exposure is a major concern; especially in case of longitudinal examinations and in children. Therefore, radiation-free imaging is an urgent necessity. Pulmonary magnetic resonance imaging (MRI) is radiation-free, but poses challenges since the low proton density and the presence of strong mesoscopic susceptibility variations considerably reduce the detectable MR signal. As a result, the lung typically appears as a “black hole” with conventional MRI techniques. Recently, ultra-fast balanced steady-state free precession (ufSSFP) methods were proposed for ameliorated lung morphological imaging. In this thesis, ufSSFP is employed to develop and improve several pulmonary functional imaging methods, which can be used in clinical settings using standard MR scanners and equipment. At every breath, the lung expands and contracts, and at every heartbeat, the blood is pumped through the arteries to reach the lung parenchyma. This creates signal modulations associated with pulmonary blood perfusion and ventilation that are detectable by MRI. The second chapter of this thesis focuses on the optimization of time-resolved two-dimensional (2D) ufSSFP for perfusion-weighted and ventilation-weighted imaging of the lung. Subsequently, in the third chapter, three-dimensional (3D) multi-volumetric ufSSFP breath-hold imaging is used to develop a lung model and retrieve the measure α, a novel ventilation-weighted quantitative parameter. Oxygen-enhanced MRI exploits the paramagnetic properties of oxygen dissolved in the blood, acting as a weak T1-shortening contrast agent. When breathing pure oxygen, it reaches only ventilated alveoli of the parenchyma and dissolves only in functional and perfused regions. How ufSSFP imaging in combination with a lung model can be used to calculate robust 3D oxygen enhancement maps is described in the fourth chapter. In addition, in the fifth chapter, 2D inversion recovery ufSSFP imaging is employed to map the T1 and T2 relaxation times of the lung, the change of the relaxation times after hyperoxic conditions, as well as the physiological oxygen wash-in and wash-out time (related to the time needed to shorten T1 after oxygen breathing). The objective of the last chapter of this thesis is the application of 3D ufSSFP imaging before and after intravenous gadolinium-based contrast agent administration for the investigation of signal enhancement ratio (SER) mapping: a rapid technique to visualize perfusion-related diseases of the lung parenchyma. The techniques presented in this thesis using optimized ufSSFP pulse sequences demonstrated potential to reveal new insights on pulmonary function as well as quantification, and might become part of the future standard for the evaluation and follow-up of several lung pathologies

Airway inflammation and omega 3 PUFA in mild to moderate asthma

Asthma is a chronic inflammatory disease characterised by reversible airflow obstruction. Based on the relationship between a lack of exercise and chronic diseases, the latest guidelines from the Department of Health (DH) recommend physical activity across the whole population (DH, 2011). Exercise Induced Bronchoconstriction (EIB) is a ‘sub-type’ of asthma which affects approximately 90% of all individuals with asthma and an additional 10% of the healthy normal population (ATS/ACCP, 2003; Anderson & Kippelen, 2012); thus, EIB may be an important limiting factor for physical activity and an important ‘barrier to exercise’ for a number of individuals. Asthma is identified primarily by the occurrence of symptoms (wheezing breathlessness and dyspnoea), peak expiratory flow rates (PEF) and spirometry (Pulmonary Function tests – PFT). The current spirometry guidelines for the characterisation of asthma include a fixed criteria for the ratio between forced expiratory volume in one second and forced vital capacity (FEV1/FVC) (Miller et al., 2005b). This fixed criteria approach lacks specificity and is likely to misdiagnose approximately 20% of patients (Miller et al., 2011). The American Thoracic Society (ATS) and the European Respiratory Society (ERS) guidelines have acknowledged these concerns and have issued position statements for the use of a different approach using a ‘lower limit of normality’ (LLN) derived from a matched healthy population (Miller et al., 2009). Based on the fixed criteria, it has been shown that there is under diagnosis of participants with mild-moderate symptoms participants in the younger age group (Cerveri et al., 2009; Hansen et al., 2007; Miller et al., 2011; Roberts et al., 2006; Swanney et al., 2008). The currently available pharmacological therapies for asthma and EIB are effective (corticosteroids and bronchodilators), however long-term usage of these medications is associated with issues of tachyphylaxis and negative side effects (Barnes, 2010; GINA, 2011). There is some evidence from observational and intervention studies to suggest a beneficial effect of fish oil (comprising of omega-3 (n-3) polyunsaturated fatty acids (PUFAs)) in inflammatory diseases, (specifically asthma). Marine based n-3 PUFA have therefore been proposed as a possible complimentary/alternative therapy for asthma. The proposed anti-inflammatory effects of fish oil may be linked to a change in cell membrane composition. This altered membrane composition following fish oil supplementation [continues ...]