Effect of liquid flow patterns on distillation trays

The thesis describes experimental work on sieve trays in an air-water simulator, 2.44 m in diameter. The liquid flow pattern, for flowrates similar to those used in commercial scale distillation, was observed experimentally by water cooling experiments, in which the temperature of the water is measured at over 100 positions over the tray area. The water is cooled by the rising air which is forced through the tray. A heat and mass transfer analogy is drawn whereby the water temperature is mapped to liquid concentration in mass transfer, and the water temperature profiles reveal how liquid channelling may reduce the tray efficiency. The first experiment was to observe the flow of water only over an unperforated tray. With the exception of very low weir loads, the flow separated at the ends of the inlet downcomer. This caused liquid to flow straight across the tray between the downcomers and large circulating regions to be formed in the side regions of the tray. The effect of the air crossflow on the flow pattern was then observed on a sieve tray of 10% free area with 1 mm diameter holes (such as is used in cryogenic distillation). The flow patterns developed on the tray were similar to those produced with water only on the unperforated tray, but at low weir loads the air crossflow prevented separation of the water flow and the associated circulating regions. At higher weir loads, liquid channelling down the centre of the tray and circulation in the side regions occurred. The percentage of the tray occupied by circulating liquid depended upon the velocity of the liquid entering the tray, which was set by the weir load and size of the gap under the inlet downcomer. The water cooling experiments showed that the temperature of the water in a circulating region is much lower than in other parts of the tray, indicating that the driving force for heat transfer is reduced. In a column section where trays (and circulating areas) are mounted on top of each other, the circulating regions will cause air (or vapour) passing through them to have a reduced change in temperature or concentration leading a loss in tray efficiency.

Peripheral vision and pattern recognition : a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

Peripheral vision and pattern recognition:a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

Groundwater flow patterns within the Triassic sandstone aquifers, Birmingham, United Kingdom

This paper presents the development of a modelling study for part of the Birmingham area. Restricted access and model resolutions have limited wide applications of some of the previously developed models. The study area covers approximately 221 km2, and is underlain geologically, by a multi-layer setup with varied hydraulic properties. The basal aquifer unit is the Kidderminster sandstone Formation, overlain by the Wildmoor and Bromsgrove sandstone Formations. The presence of the Birmingham fault which acts as low permeability barrier demarcates the eastern and southern boundaries. The western boundary is defined by the presence of crystallised rocks and coal measures, while a groundwater divide defines the northern boundary. The estimated recharge flux is 112 mm/yr. The ranges of calibrated values obtained for horizontal and vertical hydraulic conductivities are 5.787x10-6 - 2.315x10-5  m/s and 5.787x10-8  - 1.157x10-7  m/s, respectively. Corresponding values obtained for the specific yield and specific storage are 0.10 - 0.12, and 1x10 -4 - 5x10 -4. The calculated numerical error is generally much less than 0.1 %. Hydraulic layering within the Permo-Triassic sandstone aquifer is thought to account for the large vertical anisotropy. Although, uncertainties are associated with the use of a simplistic delay approach to characterise the effects of the unsaturated zone, the modelled values are comparable with those obtained in the literature, and the flow pattern predictions appear to be realistic. © Research India Publications.

Representing spray zone with cross flow as a well-mixed compartment in a high shear granulator

The spray zone is an important region to control nucleation of granules in a high shear granulator. In this study, a spray zone with cross flow is quantified as a well-mixed compartment in a high shear granulator. Granulation kinetics is quantitatively derived at both particle-scale and spray zone-scale. Two spatial decay rates, DGSDR (droplet-granule spatial decay rate) ζDG and DPSDR (droplet-primary particle spatial decay rate) ζDP, which are functions of volume fraction and diameter of particulate species within the powder bed, are defined to simplify the deduction. It is concluded that in cross flow, explicit analytical results show that the droplet concentration is subject to exponential decay with depth which produces a numerically infinite depth of spray zone in a real penetration process. In a well-mixed spray zone, the depth of the spray zone is 4/(ζDG + ζDP) and π2/3(ζDG + ζDP) in cuboid and cylinder shape, respectively. The first-order droplet-based collision rates of, nucleation rate B0 and rewetting rate RW0 are uncorrelated with the flow pattern and shape of the spray zone. The second-order droplet-based collision rate, nucleated granule-granule collision rate RGG, is correlated with the mixing pattern. Finally, a real formulation case of a high shear granulation process is used to estimate the size of the spray zone. The results show that the spray zone is a thin layer at the powder bed surface. We present, for the first time, the spray zone as a well-mixed compartment. The granulation kinetics of a well-mixed spray zone could be integrated into a Population Balance Model (PBM), particularly to aid development of a distributed model for product quality prediction.

Using accreditation criteria for collaborative quality enhancement

This paper describes a process to enhance the quality of higher education. At the heart of the process is a cross-sparring collaborative model, whereby institutions are critical friends. This is based on a prior self-evaluation, where the institution / programme identifies quality criteria it wants to improve. Part of the process is to ensure the documentation of best practices so that they can be shared with others in a so called market place. Linking the best practices to a criterion makes them searchable on a large scale. Optimal pairings of institutions can then take place for the cross-sparring activities.

Noradrenergic modulatoins of odor learning and odor representation in the rat

How experience alters neuronal ensemble dynamics and how locus coeruleus-mediated norepinephrine release facilitates memory formation in the brain are the topics of this thesis. Here we employed a visualization technique, cellular compartment analysis of temporal activity by fluorescence in situ hybridization (catFISH), to assess activation patterns of neuronal ensembles in the olfactory bulb (OB) and anterior piriform cortex (aPC) to repeated odor inputs. Two associative learning models were used, early odor preference learning in rat pups and adult rat go-no-go odor discrimination learning. With catFISH of an immediate early gene, Arc, we showed that odor representation in the OB and aPC was sparse (~5-10%) and widely distributed. Odor associative learning enhanced the stability of the rewarded odor representation in the OB and aPC. The stable component, indexed by the overlap between the two ensembles activated by the rewarded odor at two time points, increased from ~25% to ~50% (p = 0.004-1.43E⁻4; Chapter 3 and 4). Adult odor discrimination learning promoted pattern separation between rewarded and unrewarded odor representations in the aPC. The overlap between rewarded and unrewarded odor representations reduced from ~25% to ~14% (p = 2.28E⁻⁵). However, learning an odor mixture as a rewarded odor increased the overlap of the component odor representations in the aPC from ~23% to ~44% (p = 0.010; Chapter 4). Blocking both α- and β-adrenoreceptors in the aPC prevented highly similar odor discrimination learning in adult rats, and reduced OB mitral and granule ensemble stability to the rewarded odor. Similar treatment in the OB only slowed odor discrimination learning. However, OB adrenoceptor blockade disrupted pattern separation and ensemble stability in the aPC when the rats demonstrated deficiency in discrimination (Chapter 5). In another project, the role of α₂-adrenoreceptors in the OB during early odor preference learning was studied. OB α2-adrenoceptor activation was necessary for odor learning in rat pups. α₂-adrenoceptor activation was additive with β-adrenoceptor mediated signalling to promote learning (Chapter 2). Together, these experiments suggest that odor representations are highly adaptive at the early stages of odor processing. The OB and aPC work in concert to support odor learning and top-down adrenergic input exerts a powerful modulation on both learning and odor representation.

The state of the nations:current and future developments in technology enhanced learning

Right across Europe technology is playing a vital part in enhancing learning for an increasingly diverse population of learners. Learning is increasingly flexible, social and mobile and supported by high quality multi-media resources. Institutional VLEs are seeing a shift towards open source products and these core systems are supplemented by a range of social and collaborative learning tools based on web 2.0 technologies. Learners undertaking field studies and those in the workplace are coming to expect that these off-campus experiences will also be technology-rich whether supported by institutional or user-owned devices. As well as keeping European businesses competitive, learning is seen as a means of increasing social mobility and supporting an agenda of social justice. For a number of years the EUNIS E-Learning Task Force (ELTF) has conducted snapshot surveys of e-learning across member institutions, collected case studies of good practice in e-learning see (Hayes, et al., 2009) in references, supported a group looking at the future of e-learning, and showcased the best of innovation in its e-learning Award. Now for the first time the ELTF membership has come together to undertake an analysis of developments in the member states and to assess what this might mean for the future. The group applied the techniques of World Café conversation and Scenario Thinking to develop its thoughts. The analysis is unashamedly qualitative and draws on expertise from leading universities across eight of the EUNIS member states. What emerges is interesting in terms of the common trends in developments in all of the nations and similarities in hopes and concerns about the future development of learning.

Teaching morphology trough communicative approach and cooperative learning in Secondary Education: practical proposal

We will be presenting the following practical proposal that will consist of two sessions implemented with different courses of Secondary Education (ESO) of the Colegio Círculo Católico (Catholic School Group), located in the city of Burgos. Each session lasts 55 minutes. These sessions focus on the morphology of the Spanish language. Its design has been carried out by keeping in mind the theoretical basis of the communicative approach and cooperative learning.

Unraveling networked learning initiatives: an analytic framework

Networked learning happens naturally within the social systems of which we are all part. However, in certain circumstances individuals may want to actively take initiative to initiate interaction with others they are not yet regularly in exchange with. This may be the case when external influences and societal changes require innovation of existing practices. This paper proposes a framework with relevant dimensions providing insight into precipitated characteristics of designed as well as ‘fostered or grown’ networked learning initiatives. Networked learning initiatives are characterized as “goal-directed, interest-, or needs based activities of a group of (at least three) individuals that initiate interaction across the boundaries of their regular social systems”. The proposed framework is based on two existing research traditions, namely 'networked learning' and 'learning networks', comparing, integrating and building upon knowledge from both perspectives. We uncover some interesting differences between definitions, but also similarities in the way they describe what ‘networked’ means and how learning is conceptualized. We think it is productive to combine both research perspectives, since they both study the process of learning in networks extensively, albeit from different points of view, and their combination can provide valuable insights in networked learning initiatives. We uncover important features of networked learning initiatives, characterize actors and connections of which they are comprised and conditions which facilitate and support them. The resulting framework could be used both for analytic purposes and (partly) as a design framework. In this framework it is acknowledged that not all successful networks have the same characteristics: there is no standard ‘constellation’ of people, roles, rules, tools and artefacts, although there are indications that some network structures work better than others. Interactions of individuals can only be designed and fostered till a certain degree: the type of network and its ‘growth’ (e.g. in terms of the quantity of people involved, or the quality and relevance of co-created concepts, ideas, artefacts and solutions to its ‘inhabitants’) is in the hand of the people involved. Therefore, the framework consists of dimensions on a sliding scale. It introduces a structured and analytic way to look at the precipitation of networked learning initiatives: learning networks. Successive research on the application of this framework and feedback from the networked learning community is needed to further validate it’s usability and value to both research as well as practice.

Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts

Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.

Development of a Collaborative Platform for Art Education: From Pedagogy to Technology

We report on the development of a Java-based application devised to support collaborative learning of Art concepts and ideas over the Internet. Starting from an examination of the pedagogy of both Art education and collaborative learning we propose principles which are useful for the design and construction of a “lightweight” software application which supports interactive Art learning in groups. This application makes “dynamics” of an art work explicit, and supports group interaction with simple messaging and “chat” facilities. This application may be used to facilitate learning and teaching of Art, but also as a research tool to investigate the learning of Art and also the development and dynamics of collaborating groups. Evaluation of a pilot study of the use of our system with a group of 20 school children is presented.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

A STUDY OF HEAT TRANSFER AND FLOW CHARACTERISTICS OF RISING TAYLOR BUBBLES

Practical application of flow boiling to ground- and space-based thermal management systems hinges on the ability to predict the system’s heat removal capabilities under expected operating conditions. Research in this field has shown that the heat transfer coefficient within two-phase heat exchangers can be largely dependent on the experienced flow regime. This finding has inspired an effort to develop mechanistic heat transfer models for each flow pattern which are likely to outperform traditional empirical correlations. As a contribution to the effort, this work aimed to identify the heat transfer mechanisms for the slug flow regime through analysis of individual Taylor bubbles. An experimental apparatus was developed to inject single vapor Taylor bubbles into co-currently flowing liquid HFE 7100. The heat transfer was measured as the bubble rose through a 6 mm inner diameter heated tube using an infrared thermography technique. High-speed flow visualization was obtained and the bubble film thickness measured in an adiabatic section. Experiments were conducted at various liquid mass fluxes (43-200 kg/m2s) and gravity levels (0.01g-1.8g) to characterize the effect of bubble drift velocity on the heat transfer mechanisms. Variable gravity testing was conducted during a NASA parabolic flight campaign. Results from the experiments showed that the drift velocity strongly affects the hydrodynamics and heat transfer of single elongated bubbles. At low gravity levels, bubbles exhibited shapes characteristic of capillary flows and the heat transfer enhancement due to the bubble was dominated by conduction through the thin film. At moderate to high gravity, traditional Taylor bubbles provided small values of enhancement within the film, but large peaks in the wake heat transfer occurred due to turbulent vortices induced by the film plunging into the trailing liquid slug. Characteristics of the wake heat transfer profiles were analyzed and related to the predicted velocity field. Results were compared and shown to agree with numerical simulations of colleagues from EPFL, Switzerland. In addition, a preliminary study was completed on the effect of a Taylor bubble passing through nucleate flow boiling, showing that the thinning thermal boundary layer within the film suppressed nucleation, thereby decreasing the heat transfer coefficient.