Modeling of high pressure pretreatment process for gold leaching

Investigation of high pressure pretreatment process for gold leaching is the objective of the present master's thesis. The gold ores and concentrates which cannot be easily treated by leaching process are called "refractory". These types of ores or concentrates often have high content of sulfur and arsenic that renders the precious metal inaccessible to the leaching agents. Since the refractory ores in gold manufacturing industry take a considerable share, the pressure oxidation method (autoclave method) is considered as one of the possible ways to overcome the related problems. Mathematical modeling is the main approach in this thesis which was used for investigation of high pressure oxidation process. For this task, available information from literature concerning this phenomenon, including chemistry, mass transfer and kinetics, reaction conditions, applied apparatus and application, was collected and studied. The modeling part includes investigation of pyrite oxidation kinetics in order to create a descriptive mathematical model. The following major steps are completed: creation of process model by using the available knowledge; estimation of unknown parameters and determination of goodness of the fit; study of the reliability of the model and its parameters.

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.

Computational modeling of the properties of TiO2 nanoparticles

In this study we discuss the electronic, structural, and optical properties of titanium dioxide nanoparticles, and also the properties of Ni(II) diimine dithiolato complexes as dyes in dye-sensitized TiO2 based solar cells. The abovementioned properties have been modeled by using computational codes based on the density functional theory. The results achieved show slight evidence on the structure-dependent band gap broadening, and clear blue-shifts in absorption spectra and refractive index functions of ultra-small TiO2 particles. It is also shown that these properties are strongly dependent on the shape of the nanoparticles. Regarding the Ni(II) diimine dithiolato complexes as dyes in dye-sensitized TiO2 based solar cells, it is shown that based on the experimental electrochemical investigation and DFT studies all studied diimine derivatives could serve as potential candidates for the light harvesting, but the e ciencies of the dyes studied are not very promising.

Defining of the material models for stainless steels to be used in FE modeling

The aim of this work is to study the results of tensile tests for austenitic stainless steel type 304 and make accurate FE-models according to the results of the tests. Tensile tests were made at Central Research Institute of Structural Material, Prometey at Saint Petersburg and Mariyenburg in Russia. The test specimens for the tensile tests were produced at Lappeenranta University of Technology in a Laboratory of Steel Structures. In total 4 different tests were made, two with base material specimens and two with transverse butt weld specimens. Each kind of a specimen was tested at room temperature and at low temperature. By comparing the results of room and low temperature tests of similar test specimen we get to study the results of work hardening that affect the austenitic steels at below room temperature. The produced specimens are to be modeled accurately and then imported for nonlinear FEM- analyzing. Using the data gained from the tensile tests the aim is to get the models work like the specimens did during the tests. By using the analyzed results of the FE-models the aim is to calculate and get the stress-strain curves that correspond to the results acquired from the tensile tests.

Modeling of Liquid-Solid Flow in Industrial Scale

In the present work, liquid-solid flow in industrial scale is modeled using the commercial software of Computational Fluid Dynamics (CFD) ANSYS Fluent 14.5. In literature, there are few studies on liquid-solid flow in industrial scale, but any information about the particular case with modified geometry cannot be found. The aim of this thesis is to describe the strengths and weaknesses of the multiphase models, when a large-scale application is studied within liquid-solid flow, including the boundary-layer characteristics. The results indicate that the selection of the most appropriate multiphase model depends on the flow regime. Thus, careful estimations of the flow regime are recommended to be done before modeling. The computational tool is developed for this purpose during this thesis. The homogeneous multiphase model is valid only for homogeneous suspension, the discrete phase model (DPM) is recommended for homogeneous and heterogeneous suspension where pipe Froude number is greater than 1.0, while the mixture and Eulerian models are able to predict also flow regimes, where pipe Froude number is smaller than 1.0 and particles tend to settle. With increasing material density ratio and decreasing pipe Froude number, the Eulerian model gives the most accurate results, because it does not include simplifications in Navier-Stokes equations like the other models. In addition, the results indicate that the potential location of erosion in the pipe depends on material density ratio. Possible sedimentation of particles can cause erosion and increase pressure drop as well. In the pipe bend, especially secondary flows, perpendicular to the main flow, affect the location of erosion.

Modeling and Parameter Estimation of Double-Star Permanent Magnet Synchronous Machines

The power rating of wind turbines is constantly increasing; however, keeping the voltage rating at the low-voltage level results in high kilo-ampere currents. An alternative for increasing the power levels without raising the voltage level is provided by multiphase machines. Multiphase machines are used for instance in ship propulsion systems, aerospace applications, electric vehicles, and in other high-power applications including wind energy conversion systems. A machine model in an appropriate reference frame is required in order to design an efficient control for the electric drive. Modeling of multiphase machines poses a challenge because of the mutual couplings between the phases. Mutual couplings degrade the drive performance unless they are properly considered. In certain multiphase machines there is also a problem of high current harmonics, which are easily generated because of the small current path impedance of the harmonic components. However, multiphase machines provide special characteristics compared with the three-phase counterparts: Multiphase machines have a better fault tolerance, and are thus more robust. In addition, the controlled power can be divided among more inverter legs by increasing the number of phases. Moreover, the torque pulsation can be decreased and the harmonic frequency of the torque ripple increased by an appropriate multiphase configuration. By increasing the number of phases it is also possible to obtain more torque per RMS ampere for the same volume, and thus, increase the power density. In this doctoral thesis, a decoupled d–q model of double-star permanent-magnet (PM) synchronous machines is derived based on the inductance matrix diagonalization. The double-star machine is a special type of multiphase machines. Its armature consists of two three-phase winding sets, which are commonly displaced by 30 electrical degrees. In this study, the displacement angle between the sets is considered a parameter. The diagonalization of the inductance matrix results in a simplified model structure, in which the mutual couplings between the reference frames are eliminated. Moreover, the current harmonics are mapped into a reference frame, in which they can be easily controlled. The work also presents methods to determine the machine inductances by a finite-element analysis and by voltage-source inverters on-site. The derived model is validated by experimental results obtained with an example double-star interior PM (IPM) synchronous machine having the sets displaced by 30 electrical degrees. The derived transformation, and consequently, the decoupled d–q machine model, are shown to model the behavior of an actual machine with an acceptable accuracy. Thus, the proposed model is suitable to be used for the model-based control design of electric drives consisting of double-star IPM synchronous machines.

Process modeling method for electric accessories manufacturing enterprise

The theoretical research of the study focused to business process management and business process modeling, the goal was to found a new business process modeling method for electrical accessories manufacturing enterprise. The focus was to find few options for business process modeling methods where company could have chosen the best one for its needs The study was carried out as a qualitative research with an action study and a case study as the most important ways collect data. In the empirical part of the study examples of company’s processes modeled with the new modeling method and process modeling process are presented. The new way of modeling processes improves especially visual presentation of the processes and improves the understanding how employees should work in the organizational interfaces of the process and in the interfaces between different processes. The results of the study is a new unified way to model company’s processes, which makes it easier to understand and create the process models. This improved readability makes it possible to reduce the costs that were created from the unclear old process models.

Modeling of temperature regimes in a furnace of bubbling fluidized bed boiler

The work is mainly focused on the technology of bubbling fluidized bed combustion. Heat transfer and hydrodynamics of the process were examined in the work in detail. Special emphasis was placed on the process of heat exchange in a freeboard zone of bubbling fluidized bed boiler. Operating mode of bubbling fluidized bed boiler depends on many parameters. To assess the influence of some parameters on a temperature regime inside the furnace a simplified method of zonal modeling was used in the work. Thus, effects of bed material fineness, excess air ratio and changes in boiler load were studied. Besides the technology of combustion in bubbling fluidized bed, other common technologies of solid fuels combustion were reviewed. In addition, brief survey of most widely used types of solid fuel was performed in the work.

Modal testing and numerical modeling of the dynamic properties of layered sheet-steel structure

Recently, due to the increasing total construction and transportation cost and difficulties associated with handling massive structural components or assemblies, there has been increasing financial pressure to reduce structural weight. Furthermore, advances in material technology coupled with continuing advances in design tools and techniques have encouraged engineers to vary and combine materials, offering new opportunities to reduce the weight of mechanical structures. These new lower mass systems, however, are more susceptible to inherent imbalances, a weakness that can result in higher shock and harmonic resonances which leads to poor structural dynamic performances. The objective of this thesis is the modeling of layered sheet steel elements, to accurately predict dynamic performance. During the development of the layered sheet steel model, the numerical modeling approach, the Finite Element Analysis and the Experimental Modal Analysis are applied in building a modal model of the layered sheet steel elements. Furthermore, in view of getting a better understanding of the dynamic behavior of layered sheet steel, several binding methods have been studied to understand and demonstrate how a binding method affects the dynamic behavior of layered sheet steel elements when compared to single homogeneous steel plate. Based on the developed layered sheet steel model, the dynamic behavior of a lightweight wheel structure to be used as the structure for the stator of an outer rotor Direct-Drive Permanent Magnet Synchronous Generator designed for high-power wind turbines is studied.

Computational modeling of stented coronary arteries

The application of computational fluid dynamics (CFD) and finite element analysis (FEA) has been growing rapidly in the various fields of science and technology. One of the areas of interest is in biomedical engineering. The altered hemodynamics inside the blood vessels plays a key role in the development of the arterial disease called atherosclerosis, which is the major cause of human death worldwide. Atherosclerosis is often treated with the stenting procedure to restore the normal blood flow. A stent is a tubular, flexible structure, usually made of metals, which is driven and expanded in the blocked arteries. Despite the success rate of the stenting procedure, it is often associated with the restenosis (re-narrowing of the artery) process. The presence of non-biological device in the artery causes inflammation or re-growth of atherosclerotic lesions in the treated vessels. Several factors including the design of stents, type of stent expansion, expansion pressure, morphology and composition of vessel wall influence the restenosis process. Therefore, the role of computational studies is crucial in the investigation and optimisation of the factors that influence post-stenting complications. This thesis focuses on the stent-vessel wall interactions followed by the blood flow in the post-stenting stage of stenosed human coronary artery. Hemodynamic and mechanical stresses were analysed in three separate stent-plaque-artery models. Plaque was modeled as a multi-layer (fibrous cap (FC), necrotic core (NC), and fibrosis (F)) and the arterial wall as a single layer domain. CFD/FEA simulations were performed using commercial software packages in several models mimicking the various stages and morphologies of atherosclerosis. The tissue prolapse (TP) of stented vessel wall, the distribution of von Mises stress (VMS) inside various layers of vessel wall, and the wall shear stress (WSS) along the luminal surface of the deformed vessel wall were measured and evaluated. The results revealed the role of the stenosis size, thickness of each layer of atherosclerotic wall, thickness of stent strut, pressure applied for stenosis expansion, and the flow condition in the distribution of stresses. The thicknesses of FC, and NC and the total thickness of plaque are critical in controlling the stresses inside the tissue. A small change in morphology of artery wall can significantly affect the distribution of stresses. In particular, FC is the most sensitive layer to TP and stresses, which could determine plaque’s vulnerability to rupture. The WSS is highly influenced by the deflection of artery, which in turn is dependent on the structural composition of arterial wall layers. Together with the stenosis size, their roles could play a decisive role in controlling the low values of WSS (<0.5 Pa) prone to restenosis. Moreover, the time dependent flow altered the percentage of luminal area with WSS values less than 0.5 Pa at different time instants. The non- Newtonian viscosity model of the blood properties significantly affects the prediction of WSS magnitude. The outcomes of this investigation will help to better understand the roles of the individual layers of atherosclerotic vessels and their risk to provoke restenosis at the post-stenting stage. As a consequence, the implementation of such an approach to assess the post-stented stresses will assist the engineers and clinicians in optimizing the stenting techniques to minimize the occurrence of restenosis.

CFD modeling of dispersion water feed in wastewater cleaning application

Fluid particle breakup and coalescence are important phenomena in a number of industrial flow systems. This study deals with a gas-liquid bubbly flow in one wastewater cleaning application. Three-dimensional geometric model of a dispersion water system was created in ANSYS CFD meshing software. Then, numerical study of the system was carried out by means of unsteady simulations performed in ANSYS FLUENT CFD software. Single-phase water flow case was setup to calculate the entire flow field using the RNG k-epsilon turbulence model based on the Reynolds-averaged Navier-Stokes (RANS) equations. Bubbly flow case was based on a computational fluid dynamics - population balance model (CFD-PBM) coupled approach. Bubble breakup and coalescence were considered to determine the evolution of the bubble size distribution. Obtained results are considered as steps toward optimization of the cleaning process and will be analyzed in order to make the process more efficient.

Modeling of fractal antennas

In this Master Thesis the characteristics of the chosen fractal microstrip antennas are investigated. For modeling has been used the structure of the square Serpinsky fractal curves. During the elaboration of this Master thesis the following steps were undertaken: 1) calculation and simulation of square microstrip antennа, 2) optimizing for obtaining the required characteristics on the frequency 2.5 GHz, 3) simulation and calculation of the second and third iteration of the Serpinsky fractal curves, 4) radiation patterns and intensity distribution of these antennas. In this Master’s Thesis the search for the optimal position of the port and fractal elements was conducted. These structures can be used in perspective for creation of antennas working at the same time in different frequency range.

Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Modeling Front End Electronics for a Gas Electron Multiplier Detector

The Large Hadron Collider (LHC) in The European Organization for Nuclear Research (CERN) will have a Long Shutdown sometime during 2017 or 2018. During this time there will be maintenance and a possibility to install new detectors. After the shutdown the LHC will have a higher luminosity. A promising new type of detector for this high luminosity phase is a Triple-GEM detector. During the shutdown these detectors will be installed at the Compact Muon Solenoid (CMS) experiment. The Triple-GEM detectors are now being developed at CERN and alongside also a readout ASIC chip for the detector. In this thesis a simulation model was developed for the ASICs analog front end. The model will help to carry out more extensive simulations and also simulate the whole chip before the whole design is finished. The proper functioning of the model was tested with simulations, which are also presented in the thesis.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.