Implementation of population balances in simulation of gas-liquid reactors using a CFD-PBM coupled approach

In bubbly flow simulations, bubble size distribution is an important factor in determination of hydrodynamics. Beside hydrodynamics, it is crucial in the prediction of interfacial area available for mass transfer and in the prediction of reaction rate in gas-liquid reactors such as bubble columns. Solution of population balance equations is a method which can help to model the size distribution by considering continuous bubble coalescence and breakage. Therefore, in Computational Fluid Dynamic simulations it is necessary to couple CFD and Population Balance Model (CFD-PBM) to get reliable distribution. In the current work a CFD-PBM coupled model is implemented as FORTRAN subroutines in ANSYS CFX 10 and it has been tested for bubbly flow. This model uses the idea of Multi Phase Multi Size Group approach which was previously presented by Sha et al. (2006) [18]. The current CFD-PBM coupled method considers inhomogeneous flow field for different bubble size groups in the Eulerian multi-dispersed phase systems. Considering different velocity field for bubbles can give the advantageof more accurate solution of hydrodynamics. It is also an improved method for prediction of bubble size distribution in multiphase flow compared to available commercial packages.

Demographic Modelling of Human Population Growth

This work presents models and methods that have been used in producing forecasts of population growth. The work is intended to emphasize the reliability bounds of the model forecasts. Leslie model and various versions of logistic population models are presented. References to literature and several studies are given. A lot of relevant methodology has been developed in biological sciences. The Leslie modelling approach involves the use of current trends in mortality,fertility, migration and emigration. The model treats population divided in age groups and the model is given as a recursive system. Other group of models is based on straightforward extrapolation of census data. Trajectories of simple exponential growth function and logistic models are used to produce the forecast. The work presents the basics of Leslie type modelling and the logistic models, including multi- parameter logistic functions. The latter model is also analysed from model reliability point of view. Bayesian approach and MCMC method are used to create error bounds of the model predictions.

Ankle-brachial index, high-sensitivity C-reactive protein and endothelial function in a cardiovascular risk population

Atherosclerotic vascular disease is the leading cause of death in the Western world. Its main three manifestations are coronary heart disease, cerebrovascular disease, and peripheral arterial disease. Asymptomatic peripheral arterial disease is usually diagnosed using the ankle brachial index, and values ≤ 0.90 are used to determine the diagnosis. The classical risk factors of peripheral arterial disease, such as smoking and diabetes, are well known and early interventions are mandatory to improve the prognosis. What is not well known is the role of inflammation as a risk factor. Yet, a novel approach to cardiovascular diseases is the measurement of endothelial function. In this thesis, we studied the ankle-brachial index, C-reactive protein and endothelial function in a cardiovascular risk population. A total of 2856 subjects were invited to the study and 2085 (73%) responded. From these subjects, a cohort of 1756 risk persons was screened. We excluded the subjects with previously known cardiovascular disease or diabetes, because they were already under systematic follow-up. Out of the study subjects, 983 (56%) were women and 773 (44%) men. The ankle brachial index and high-sensitivity C-reactive protein were measured from 1047 subjects. Endothelial function was assessed by measuring reactive hyperemia pulse amplitude tonometry from 66 subjects with borderline peripheral arterial disease. In this study, smoking was a crucial risk factor for peripheral arterial disease. Subclinical peripheral arterial disease seems to be more common in hypertensive patients even without comorbidities. The measurement of the ankle brachial index is an efficient method to identify patients at an increased cardiovascular risk. High-sensitivity C-reactive protein did not correlate with the ankle brachial index or peripheral arterial disease. Instead, it correlated with measures of obesity. In a cardiovascular risk population with borderline peripheral arterial disease, nearly every fourth subject had endothelial dysfunction. This might point out a subgroup of individuals in need of more intensive treatment for their risk factors.

From genotype to phenotype : diversity and population structure of Flavobacterium psychrophilum

Flavobacterium psychrophilum is the etiological agent of bacterial cold-water disease (BCWD) causing high fish mortalities and significant economic losses to the freshwater salmonid aquaculture industry around the world. Today BCWD outbreaks are mainly treated with environmentally hazardous antimicrobial agents and alternative preventative measures are urgently needed in order to ensure the well-being of animals and the sustainability of aquaculture. The diversity of pathogenic bacteria challenges the development of universal control strategies and in many cases the pathogen population structure, i.e. the total genetic diversity of the species must be taken into account. This work integrates the tools of modern molecular biology and conventional phenotypic microbiology to gain knowledge about the diversity and population structure of F. psychrophilum. The present work includes genetic characterization of a large collection of isolates collected from diverse origins and years, from aquaculture in a whole region including different countries, and provides the first international validation of a universal multilocus sequence typing (MLST) approach for unambiguous genetic typing of F. psychrophilum. Population structure analyses showed that the global F. psychrophilum population is subdivided into pathogenic species-specific clones, of which one particular genetic lineage, clonal complex CC-ST2, has been responsible for the majority of BCWD outbreaks in rainbow trout (Oncorhynchus mykiss) in European aquaculture facilities over several decades. Genotypic and phenotypic population heterogeneity affecting antimicrobial resistance in F. psychrophilum within BCWD outbreaks was discovered. Specific genotypes were associated with severe infections in farmed rainbow trout and Atlantic salmon (Salmo salar), and in addition to high adherence, antimicrobial resistance was strongly associated with outbreak strains. The study brought additional support for the hypothesis of an epidemic F. psychrophilum population structure, where recombination is an important force for the generation and maintenance of genetic diversity, and a significant contribution towards mapping the genetic diversity of this important fish pathogen. Evidence indicating dissemination of virulent strains with commercial movement of fish and fish products was strengthened.

A Bayesian Approach for Forecasting Heat Load in a District Heating System

The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.