Modeling of fireside deposit formation in two industrial furnaces

Fireside deposits can be found in many types of utility and industrial furnaces. The deposits in furnaces are problematic because they can reduce heat transfer, block gas paths and cause corrosion. To tackle these problems, it is vital to estimate the influence of deposits on heat transfer, to minimize deposit formation and to optimize deposit removal. It is beneficial to have a good understanding of the mechanisms of fireside deposit formation. Numerical modeling is a powerful tool for investigating the heat transfer in furnaces, and it can provide valuable information for understanding the mechanisms of deposit formation. In addition, a sub-model of deposit formation is generally an essential part of a comprehensive furnace model. This work investigates two specific processes of fireside deposit formation in two industrial furnaces. The first process is the slagging wall found in furnaces with molten deposits running on the wall. A slagging wall model is developed to take into account the two-layer structure of the deposits. With the slagging wall model, the thickness and the surface temperature of the molten deposit layer can be calculated. The slagging wall model is used to predict the surface temperature and the heat transfer to a specific section of a super-heater tube panel with the boundary condition obtained from a Kraft recovery furnace model. The slagging wall model is also incorporated into the computational fluid dynamics (CFD)-based Kraft recovery furnace model and applied on the lower furnace walls. The implementation of the slagging wall model includes a grid simplification scheme. The wall surface temperature calculated with the slagging wall model is used as the heat transfer boundary condition. Simulation of a Kraft recovery furnace is performed, and it is compared with two other cases and measurements. In the two other cases, a uniform wall surface temperature and a wall surface temperature calculated with a char bed burning model are used as the heat transfer boundary conditions. In this particular furnace, the wall surface temperatures from the three cases are similar and are in the correct range of the measurements. Nevertheless, the wall surface temperature profiles with the slagging wall model and the char bed burning model are different because the deposits are represented differently in the two models. In addition, the slagging wall model is proven to be computationally efficient. The second process is deposit formation due to thermophoresis of fine particles to the heat transfer surface. This process is considered in the simulation of a heat recovery boiler of the flash smelting process. In order to determine if the small dust particles stay on the wall, a criterion based on the analysis of forces acting on the particle is applied. Time-dependent simulation of deposit formation in the heat recovery boiler is carried out and the influence of deposits on heat transfer is investigated. The locations prone to deposit formation are also identified in the heat recovery boiler. Modeling of the two processes in the two industrial furnaces enhances the overall understanding of the processes. The sub-models developed in this work can be applied in other similar deposit formation processes with carefully-defined boundary conditions.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

Role of endothelium in angiotensin II formation by the rat aorta and mesenteric arterial bed

We investigated the angiotensin II (Ang II)-generating system by analyzing the vasoconstrictor effect of Ang II, angiotensin I (Ang I), and tetradecapeptide (TDP) renin substrate in the absence and presence of inhibitors of the renin-angiotensin system in isolated rat aortic rings and mesenteric arterial beds with and without functional endothelium. Ang II, Ang I, and TDP elicited a dose-dependent vasoconstrictor effect in both vascular preparations that was completely blocked by the Ang II receptor antagonist saralasin (50 nM). The angiotensin converting enzyme (ACE) inhibitor captopril (36 µM) completely inhibited the vasoconstrictor effect elicited by Ang I and TDP in aortic rings without affecting that of Ang II. In contrast, captopril (36 µM) significantly reduced (80-90%) the response to bolus injection of Ang I, without affecting those to Ang II and TDP in mesenteric arteries. Mechanical removal of the endothelium greatly potentiated (70-95%) the vasoconstrictor response to Ang II, Ang I, and TDP in aortic rings while these responses were unaffected by the removal of the endothelium of mesenteric arteries with sodium deoxycholate infusion. In addition, endothelium disruption did not change the pattern of response elicited by these peptides in the presence of captopril. These findings indicate that the endothelium may not be essential for Ang II formation in rat mesenteric arteries and aorta, but it may modulate the response to Ang II. Although Ang II formation from Ang I is essentially dependent on ACE in both vessels, our results suggest the existence of an alternative pathway in the mesenteric arterial bed that may play an important role in Ang II generation from TDP in resistance but not in large vessels during ACE inhibition

Involvement of hippocampal AMPA glutamate receptor changes and the cAMP/protein kinase A/CREB-P signalling pathway in memory consolidation of an avoidance task in rats

Training in step-down inhibitory avoidance (0.3-mA footshock) is followed by biochemical changes in rat hippocampus that strongly suggest an involvement of quantitative changes in glutamate AMPA receptors, followed by changes in the dopamine D1 receptor/cAMP/protein kinase A (PKA)/CREB-P signalling pathway in memory consolidation. AMPA binding to its receptor and levels of the AMPA receptor-specific subunit GluR1 increase in the hippocampus within the first 3 h after training (20-70%). Binding of the specific D1 receptor ligand, SCH23390, and cAMP levels increase within 3 or 6 h after training (30-100%). PKA activity and CREB-P levels show two peaks: a 35-40% increase 0 h after training, and a second increase 3-6 h later (35-60%). The results correlate with pharmacological findings showing an early post-training involvement of AMPA receptors, and a late involvement of the D1/cAMP/PKA/CREB-P pathway in memory consolidation of this task

Context-specific independence in graphical models

The theme of this thesis is context-speci c independence in graphical models. Considering a system of stochastic variables it is often the case that the variables are dependent of each other. This can, for instance, be seen by measuring the covariance between a pair of variables. Using graphical models, it is possible to visualize the dependence structure found in a set of stochastic variables. Using ordinary graphical models, such as Markov networks, Bayesian networks, and Gaussian graphical models, the type of dependencies that can be modeled is limited to marginal and conditional (in)dependencies. The models introduced in this thesis enable the graphical representation of context-speci c independencies, i.e. conditional independencies that hold only in a subset of the outcome space of the conditioning variables. In the articles included in this thesis, we introduce several types of graphical models that can represent context-speci c independencies. Models for both discrete variables and continuous variables are considered. A wide range of properties are examined for the introduced models, including identi ability, robustness, scoring, and optimization. In one article, a predictive classi er which utilizes context-speci c independence models is introduced. This classi er clearly demonstrates the potential bene ts of the introduced models. The purpose of the material included in the thesis prior to the articles is to provide the basic theory needed to understand the articles.

Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Nephrotoxicity of Bence-Jones proteins: interference in renal epithelial cell acidification

The aim of the present study was to evaluate the acidification of the endosome-lysosome system of renal epithelial cells after endocytosis of two human immunoglobulin lambda light chains (Bence-Jones proteins, BJP) obtained from patients with multiple myeloma. Renal epithelial cell handling of two BJP (neutral and acidic BJP) was evaluated by rhodamine fluorescence. Renal cells (MDCK) were maintained in culture and, when confluent, were incubated with rhodamine-labeled BJP for different periods of time. Photos were obtained with a fluorescence microscope (Axiolab-Zeiss). Labeling density was determined on slides with a densitometer (Shimadzu Dual-Wavelength Flying-Spot Scanner CS9000). Endocytosis of neutral and acidic BJP was correlated with acidic intracellular compartment distribution using acridine orange labeling. We compared the pattern of distribution after incubation of native neutral and acidic BJP and after complete deglycosylation of BJP by periodate oxidation. The subsequent alteration of pI converted neutral BJP to acidic BJP. There was a significant accumulation of neutral BJP in endocytic structures, reduced lysosomal acidification, and a diffuse pattern of acidification. This pattern was reversed after total deglycosylation and subsequent alteration of the pI to an acidic BJP. We conclude that the physicochemical characteristics of BJP interfere with intracellular acidification, possibly explaining the strong nephrotoxicity of neutral BJP. Lysosomal acidification is fundamental for adequate protein processing and catabolism.

Pharmacology and toxicology of diphenyl diselenide in several biological models

The pharmacology of synthetic organoselenium compounds indicates that they can be used as antioxidants, enzyme inhibitors, neuroprotectors, anti-tumor and anti-infectious agents, and immunomodulators. In this review, we focus on the effects of diphenyl diselenide (DPDS) in various biological model organisms. DPDS possesses antioxidant activity, confirmed in several in vitro and in vivo systems, and thus has a protective effect against hepatic, renal and gastric injuries, in addition to its neuroprotective activity. The activity of the compound on the central nervous system has been studied since DPDS has lipophilic characteristics, increasing adenylyl cyclase activity and inhibiting glutamate and MK-801 binding to rat synaptic membranes. Systemic administration facilitates the formation of long-term object recognition memory in mice and has a protective effect against brain ischemia and on reserpine-induced orofacial dyskinesia in rats. On the other hand, DPDS may be toxic, mainly because of its interaction with thiol groups. In the yeast Saccharomyces cerevisiae, the molecule acts as a pro-oxidant by depleting free glutathione. Administration to mice during cadmium intoxication has the opposite effect, reducing oxidative stress in various tissues. DPDS is a potent inhibitor of d-aminolevulinate dehydratase and chronic exposure to high doses of this compound has central effects on mouse brain, as well as liver and renal toxicity. Genotoxicity of this compound has been assessed in bacteria, haploid and diploid yeast and in a tumor cell line.

Expression of E-cadherin, Snail and Hakai in epithelial cells isolated from the primary tumor and from peritumoral tissue of invasive ductal breast carcinomas

Epithelial intercellular cohesion, mainly mediated by E-cadherin (CDH1) expression and function, may be deregulated during cancer cell invasion of adjacent tissues and lymphatic and vascular channels. CDH1 expression is down-modulated in invasive lobular breast carcinomas but its regulation in invasive ductal carcinomas (IDC) is less clear. CDH1 expression is repressed by transcription factors such as Snail (SNAI1) and its product is degraded after Hakai ubiquitination. We compared CDH1, SNAI1 and HAKAI mRNA expression in IDC and paired adjacent normal breast tissue and evaluated its relation with node metastasis and circulating tumor cells. Matched tumor/peritumoral and blood samples were collected from 30 patients with early IDC. Epithelial cells from each compartment (tumor/peritumoral) were recovered by an immunomagnetic method and gene expression was determined by real time RT-PCR. There were no differences in CDH1, SNAI1 and HAKAI mRNA expression between tumor and corresponding peritumoral samples and no differential tumoral gene expression according to nodal involvement. Another 30 patients with a long-term follow-up (at least 5 years) and a differential prognosis (good or poor, as defined by breast cancer death) had E-cadherin and Snail protein detected by immunohistochemistry in tumor samples. In this group, E-cadherin-positive expression, but not Snail, may be associated with a better prognosis. This is the first report simultaneously analyzing CDH1, SNAI1 and HAKAI mRNA expression in matched tumor and peritumoral samples from patients with IDC. However, no clear pattern of their expression could distinguish the invasive tumor compartment from its adjacent normal tissue.

Noncrystalline uric acid inhibits proteoglycan and glycosaminoglycan synthesis in distal tubular epithelial cells (MDCK)

Hyperuricemia is associated with renal stones, not only consisting of uric acid (UrAc) but also of calcium oxalate (CaOx). Glycosaminoglycans (GAGs) are well-known inhibitors of growth and aggregation of CaOx crystals. We analyzed the effect of noncrystalline UrAc on GAG synthesis in tubular distal cells. MDCK (Madin-Darby canine kidney) cells were exposed to noncrystalline UrAc (80 µg/mL) for 24 h. GAGs were labeled metabolically and characterized by agarose gel electrophoresis. The expression of proteoglycans and cyclooxygenase 2 (COX-2) was assessed by real-time PCR. Necrosis, apoptosis and prostaglandin E2 (PGE2) were determined by acridine orange, HOESCHT 33346, and ELISA, respectively. CaOx crystal endocytosis was evaluated by flow cytometry. Noncrystalline UrAc significantly decreased the synthesis and secretion of heparan sulfate into the culture medium (UrAc: 2127 ± 377; control: 4447 ± 730 cpm) and decreased the expression of perlecan core protein (UrAc: 0.61 ± 0.13; control: 1.07 ± 0.16 arbitrary units), but not versican. Noncrystalline UrAc did not induce necrosis or apoptosis, but significantly increased COX-2 and PGE2 production. The effects of noncrystalline UrAc on GAG synthesis could not be attributed to inflammatory actions because lipopolysaccharide, as the positive control, did not have the same effect. CaOx was significantly endocytosed by MDCK cells, but this endocytosis was inhibited by exposure to noncrystalline UrAc (control: 674.6 ± 4.6, CaOx: 724.2 ± 4.2, and UrAc + CaOx: 688.6 ± 5.4 geometric mean), perhaps allowing interaction with CaOx crystals. Our results indicate that UrAc decreases GAG synthesis in MDCK cells and this effect could be related to the formation of UrAc and CaOx stones.

Human sepsis-associated Escherichia coli (SEPEC) is able to adhere to and invade kidney epithelial cells in culture

The adhesins of extraintestinal pathogenic Escherichia coli are essential for mediating direct interactions between the microbes and the host cell surfaces that they infect. Using fluorescence microscopy and gentamycin protection assays, we observed that 49 sepsis-associated E. coli (SEPEC) strains isolated from human adults adhered to and invaded Vero cells in the presence of D-mannose (100%). In addition, bacteria concentrations of approximately 2 x 10(7) CFU/mL were recovered from Vero cells following an invasion assay. Furthermore, PCR analysis of adhesin genes showed that 98.0% of these SEPEC strains tested positive for fimH, 69.4% for flu, 53.1% for csgA, 38.8% for mat, and 32.7% for iha. Analysis of the invasin genes showed that 16.3% of the SEPEC strains were positive for tia, 12.3% for gimB, and 10.2% for ibeA. Therefore, these data suggest that SEPEC adhesion to cell surfaces occurs through non-fimH mechanisms. Scanning electron microscopy showed the formation of microcolonies on the Vero cell surface. SEPEC invasiveness was also confirmed by the presence of intracellular bacteria, and ultrastructural analysis using electron transmission microscopy revealed bacteria inside the Vero cells. Taken together, these results demonstrate that these SEPEC strains had the ability to adhere to and invade Vero cells. Moreover, these data support the theory that renal cells may be the predominant pathway through which SEPEC enters human blood vessels.

Sleep pattern and learning in knockdown mice with reduced cholinergic neurotransmission

Impaired cholinergic neurotransmission can affect memory formation and influence sleep-wake cycles (SWC). In the present study, we describe the SWC in mice with a deficient vesicular acetylcholine transporter (VAChT) system, previously characterized as presenting reduced acetylcholine release and cognitive and behavioral dysfunctions. Continuous, chronic ECoG and EMG recordings were used to evaluate the SWC pattern during light and dark phases in VAChT knockdown heterozygous (VAChT-KDHET, n=7) and wild-type (WT, n=7) mice. SWC were evaluated for sleep efficiency, total amount and mean duration of slow-wave, intermediate and paradoxical sleep, as well as the number of awakenings from sleep. After recording SWC, contextual fear-conditioning tests were used as an acetylcholine-dependent learning paradigm. The results showed that sleep efficiency in VAChT-KDHET animals was similar to that of WT mice, but that the SWC was more fragmented. Fragmentation was characterized by an increase in the number of awakenings, mainly during intermediate sleep. VAChT-KDHET animals performed poorly in the contextual fear-conditioning paradigm (mean freezing time: 34.4±3.1 and 44.5±3.3 s for WT and VAChT-KDHET animals, respectively), which was followed by a 45% reduction in the number of paradoxical sleep episodes after the training session. Taken together, the results show that reduced cholinergic transmission led to sleep fragmentation and learning impairment. We discuss the results on the basis of cholinergic plasticity and its relevance to sleep homeostasis. We suggest that VAChT-KDHET mice could be a useful model to test cholinergic drugs used to treat sleep dysfunction in neurodegenerative disorders.

Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

Chemical pattern of brazilian apples: a chemometric approach based on the Fuji and Gala varieties

The chemical composition of apple juices may be used to discriminate between the varieties for consumption and those for raw material. Fuji and Gala have a chemical pattern that can be used for this classification. Multivariate methods correlate independent continuous chemical descriptors with the categorical apple variety. Three main descriptors of apple juice were selected: malic acid, total reducing sugar and total phenolic compounds. A chemometric approach, employing PCA and SIMCA, was used to classify apple juice samples. PCA was performed with 24 juices from Fuji and Gala, and SIMCA, with 15 juices. The exploratory and predictive models recognized 88% and 64%, respectively, as belonging to a mixed domain. The apple juice from commercial fruits shows a pattern related to cv. Fuji and Gala with boundaries from 0.18 to 0.389 g.100 mL-1 (malic acid), from 8.65 to 15.18 g.100 mL-1 (total reducing sugar) and from 100 to 400 mg.L-1 (total phenolic compounds), but such boundaries were slightly shorter in the remaining set of commercial apple juices, specifically from 0.16 to 0.36 g.100 mL-1, from 9.25 to 15.5 g.100 mL-1 and from 180 to 606 mg.L-1 for acidity, reducing sugar and phenolic compounds, respectively, representing the acid, sweet and bitter tastes.

From epithelial to mesenchymal: regulation of invasive cancer cell motility

Metastasis is the main cause of death among cancer patients. In order to initiate the metastatic cascade cancer cells have to undergo epithelial-to-mesenchymal transition (EMT). In EMT epithelial cells lose their cell-cell and cell-extracellular matrix (ECM) contacts and become more motile. The expression of the transcription factor Slug and of the mesenchymal intermediate filament vimentin is induced during EMT. Vimentin is often overexpressed in malignant epithelial cancers but the functional role of vimentin remains incompletely understood. In addition, kinases such as AKT and ERK are known to be involved in the regulation of EMT and cancer cell motility but the mechanisms underlining their functions are often unclear. Integrins are heterodimeric receptors that attach cells to the surrounding tissue and participate in regulating cell migration and invasion. Changes in integrin activity are linked to increased cell motility and further cancer metastasis. The aim for my PhD studies was to investigate the role of cellular signalling pathways and vimentin in the regulation of cancer cell motility and EMT. Our results revealed that in prostate cancer the downregulation of AKT1 and AKT2, but not AKT3, induces activation of cell surface 1-integrins leading to enhanced cell adhesion, migration and invasion. In addition, our findings demonstrated a reciprocal regulatory interaction between vimentin and ERK2 facilitating ERK-mediated phosphorylation of Slug at serine-87 (S87) in breast cancer. Surprisingly, Slug S87 phosphorylation is dispensable for E-cadherin repression but essential for the induction of vimentin and Axl expression in early onset of EMT. Our findings reveal previously unknown mechanistic information of how prostate and breast cancer cell motility and disease progression is regulated