Differential Evolution approach and parameter estimation of chaotic

Parameter estimation still remains a challenge in many important applications. There is a need to develop methods that utilize achievements in modern computational systems with growing capabilities. Owing to this fact different kinds of Evolutionary Algorithms are becoming an especially perspective field of research. The main aim of this thesis is to explore theoretical aspects of a specific type of Evolutionary Algorithms class, the Differential Evolution (DE) method, and implement this algorithm as codes capable to solve a large range of problems. Matlab, a numerical computing environment provided by MathWorks inc., has been utilized for this purpose. Our implementation empirically demonstrates the benefits of a stochastic optimizers with respect to deterministic optimizers in case of stochastic and chaotic problems. Furthermore, the advanced features of Differential Evolution are discussed as well as taken into account in the Matlab realization. Test "toycase" examples are presented in order to show advantages and disadvantages caused by additional aspects involved in extensions of the basic algorithm. Another aim of this paper is to apply the DE approach to the parameter estimation problem of the system exhibiting chaotic behavior, where the well-known Lorenz system with specific set of parameter values is taken as an example. Finally, the DE approach for estimation of chaotic dynamics is compared to the Ensemble prediction and parameter estimation system (EPPES) approach which was recently proposed as a possible solution for similar problems.

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

On state and parameter estimation in chaotic systems

State-of-the-art predictions of atmospheric states rely on large-scale numerical models of chaotic systems. This dissertation studies numerical methods for state and parameter estimation in such systems. The motivation comes from weather and climate models and a methodological perspective is adopted. The dissertation comprises three sections: state estimation, parameter estimation and chemical data assimilation with real atmospheric satellite data. In the state estimation part of this dissertation, a new filtering technique based on a combination of ensemble and variational Kalman filtering approaches, is presented, experimented and discussed. This new filter is developed for large-scale Kalman filtering applications. In the parameter estimation part, three different techniques for parameter estimation in chaotic systems are considered. The methods are studied using the parameterized Lorenz 95 system, which is a benchmark model for data assimilation. In addition, a dilemma related to the uniqueness of weather and climate model closure parameters is discussed. In the data-oriented part of this dissertation, data from the Global Ozone Monitoring by Occultation of Stars (GOMOS) satellite instrument are considered and an alternative algorithm to retrieve atmospheric parameters from the measurements is presented. The validation study presents first global comparisons between two unique satellite-borne datasets of vertical profiles of nitrogen trioxide (NO3), retrieved using GOMOS and Stratospheric Aerosol and Gas Experiment III (SAGE III) satellite instruments. The GOMOS NO3 observations are also considered in a chemical state estimation study in order to retrieve stratospheric temperature profiles. The main result of this dissertation is the consideration of likelihood calculations via Kalman filtering outputs. The concept has previously been used together with stochastic differential equations and in time series analysis. In this work, the concept is applied to chaotic dynamical systems and used together with Markov chain Monte Carlo (MCMC) methods for statistical analysis. In particular, this methodology is advocated for use in numerical weather prediction (NWP) and climate model applications. In addition, the concept is shown to be useful in estimating the filter-specific parameters related, e.g., to model error covariance matrix parameters.

OL1/OL2-suojarakennuksen jäähdytteenmenetysonnettomuusanalyysit APROS 5.09 -ohjelmistolla

Tässä työssä on tutkittu OL1/OL2-suojarakennuksen käyttäytymistä jäähdytteenmenetysonnettomuuden eli LOCA:n aikana. Onnettomuuden simulointiin on kehitetty suojarakennusmalli suomalaiseen APROS 5.09 - ohjelmistoon sisältyvällä LP-koodilla (Lumped Parameter Code). Työssä on keskitytty suojarakennuksen kannalta oleellisimpien suureiden: kaasutilavuuksien paineen sekä lämpötilan ja lauhdutusaltaan lämpötilan ja pinnankorkeuden ajalliseen käyttäytymiseen. Mallinnetut LOCA:t ovat päähöyrylinjan ja sammutetun reaktorin jäähdytysjärjestelmän putkikatkoksia. Simulointeja on tehty laitoksen täyden tehon ja kuumavalmiuden lähtötiloissa ja tarkasteltavien suureiden käyttäytymistä on tutkittu erikseen valituilla konservatiivisilla oletuksilla. Laskentatapaukset rajoittuvat lyhyeen aikaväliin 27.78 tuntia kuvitellusta putkirikosta eteenpäin. Tuloksia on verrattu OL1/OL2- laitostoimittajan, Westinghouse Electric Sweden AB:n, tekemiin lisensiointianalyyseihin. Työssä on myös kuvattu APROS-laskennan epävarmuustekijöitä. Suojarakennuksen on todettu käyttäytyvän fysikaalisesti yhtenevästi lisensiointianalyyseissä ja APROS:lla tehdyissä vertailulaskuissa.

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.

Microcracks and Vortices in Superconducting Thin Films

In this work, superconducting YBa₂ Cu₃O_6+x (YBCO) thin films have been studied with the experimental focus on the anisotropy of BaZrO₃ (BZO) doped YBCOthin films and the theoretical focus on modelling flux pinning by numerically solving Ginzburg- Landau equations. Also, the structural properties of undoped YBCO thin films grown on NdGaO₃ (NGO) and MgO substrates were investigated. The thin film samples were made by pulsed laser ablation on single crystal substrates. The structural properties of the thin films were characterized by X-ray diffraction and atomic force microscope measurements. The superconducting properties were investigated with a magnetometer and also with transport measurements in pulsed magnetic field up to 30 T. Flux pinning was modelled by restricting the value of the order parameter inside the columnar pinning sites and then solving the Ginzburg-Landau equations numerically with the restrictions in place. The computations were done with a parallel code on a supercomputer. The YBCO thin films were seen to develop microcracks when grown on NGO or MgO substrates. The microcrack formation was connected to the structure of the YBCO thin films in both cases. Additionally, the microcracks can be avoided by careful optimization of the deposition parameters and the film thickness. The BZO doping of the YBCO thin films was seen to decrease the effective electron mass anisotropy, which was seen by fitting the Blatter scaling to the angle dependence of the upper critical field. The Ginzburg-Landau simulations were able to reproduce the measured magnetic field dependence of the critical current density for BZO doped and undoped YBCO. The simulations showed that in addition to the large density also the large size of the BZO nanorods is a key factor behind the change in the power law behaviour between BZO doped and undoped YBCO. Additionally, the Ginzburg-Landau equations were solved for type I thin films where giant vortices were seen to appear depending on the film thickness. The simulations predicted that singly quantized vortices are stable in type I films up to quite large thicknesses and that the size of the vortices increases with decreasing film thickness, in a way that is similar to the behaviour of the interaction length of Pearl vortices.

Liquid cooling solutions for rotating permanent magnet synchronous machines

In the design of electrical machines, efficiency improvements have become very important. However, there are at least two significant cases in which the compactness of electrical machines is critical and the tolerance of extremely high losses is valued: vehicle traction, where very high torque density is desired at least temporarily; and direct-drive wind turbine generators, whose mass should be acceptably low. As ever higher torque density and ever more compact electrical machines are developed for these purposes, thermal issues, i.e. avoidance of over-temperatures and damage in conditions of high heat losses, are becoming of utmost importance. The excessive temperatures of critical machine components, such as insulation and permanent magnets, easily cause failures of the whole electrical equipment. In electrical machines with excitation systems based on permanent magnets, special attention must be paid to the rotor temperature because of the temperature-sensitive properties of permanent magnets. The allowable temperature of NdFeB magnets is usually significantly less than 150 ˚C. The practical problem is that the part of the machine where the permanent magnets are located should stay cooler than the copper windings, which can easily tolerate temperatures of 155 ˚C or 180 ˚C. Therefore, new cooling solutions should be developed in order to cool permanent magnet electrical machines with high torque density and because of it with high concentrated losses in stators. In this doctoral dissertation, direct and indirect liquid cooling techniques for permanent magnet synchronous electrical machines (PMSM) with high torque density are presented and discussed. The aim of this research is to analyse thermal behaviours of the machines using the most applicable and accurate thermal analysis methods and to propose new, practical machine designs based on these analyses. The Computational Fluid Dynamics (CFD) thermal simulations of the heat transfer inside the machines and lumped parameter thermal network (LPTN) simulations both presented herein are used for the analyses. Detailed descriptions of the simulated thermal models are also presented. Most of the theoretical considerations and simulations have been verified via experimental measurements on a copper tooth-coil (motorette) and on various prototypes of electrical machines. The indirect liquid cooling systems of a 100 kW axial flux (AF) PMSM and a 110 kW radial flux (RF) PMSM are analysed here by means of simplified 3D CFD conjugate thermal models of the parts of both machines. In terms of results, a significant temperature drop of 40 ̊C in the stator winding and 28 ̊C in the rotor of the AF PMSM was achieved with the addition of highly thermally conductive materials into the machine: copper bars inserted in the teeth, and potting material around the end windings. In the RF PMSM, the potting material resulted in a temperature decrease of 6 ̊C in the stator winding, and in a decrease of 10 ̊C in the rotor embedded-permanentmagnets. Two types of unique direct liquid cooling systems for low power machines are analysed herein to demonstrate the effectiveness of the cooling systems in conditions of highly concentrated heat losses. LPTN analysis and CFD thermal analysis (the latter being particularly useful for unique design) were applied to simulate the temperature distribution within the machine models. Oil-immersion cooling provided good cooling capability for a 26.6 kW PMSM of a hybrid vehicle. A direct liquid cooling system for the copper winding with inner stainless steel tubes was designed for an 8 MW directdrive PM synchronous generator. The design principles of this cooling solution are described in detail in this thesis. The thermal analyses demonstrate that the stator winding and the rotor magnet temperatures are kept significantly below their critical temperatures with demineralized water flow. A comparison study of the coolant agents indicates that propylene glycol is more effective than ethylene glycol in arctic conditions.

Chromium mass balance in sodium chlorate process

Kuudenarvoista kromia käytetään natriumkloraatin valmistuksessa prosessin tuotantotehokkuuden ja turvallisuuden parantamiseksi. Kromia kuitenkin poistuu prosessista muutamaa reittiä pitkin. Koska kuuudenarvoisella kromilla on syöpää aiheuttavia, mutageenisiä sekä lisääntymiselle myrkyllisiä ominaisuuksia, olisi tärkeää ymmärtää, miten kromi kulkeutuu prosessin eri osiin, ja kuinka paljon sitä poistuu prosessista. Tämä on tärkeää, jotta osataan hallita kromin käytöstä aiheutuvat riskit, sekä toisaalta myös tehostaa kromin käyttöä prosessissa. Työn tarkoituksena oli tuottaa tietoa kromin käytöstä natriumkloraattiprosessissa. Työssä tutkittiin kromitasetta prosessin keskeisimmissä yksikköoperaatioissa. Myös kromin saostumista katodien pinnalle arvioitiin määrällisesti. Eri prosessinäytteistä tutkittiin lisäksi kromin hapetusasteita. Edellä mainittuja tutkimuskohteita varten määritettiin prosessinäytteiden kromipitoisuus. Eri prosessioperaatioille suoritettiin lisäksi taselaskelmat. Työn tuloksena esitettiin kromitase sekä yksikköoperaatioille että koko prosessille. Erinäisten epätarkkuustekijöiden vuoksi tasetta ei kuitenkaan pystytty määrittämään halutulla tarkkuudella, ja siksi työssä esitettyä tasetta voidaan pitää vain suuntaa antavana laskelmana. Katodien pinnalle saostunutta kromin määrää pidettiin kuitenkin oikean suuruusluokan tuloksena. Prosessinäytteiden hapetusasteita ei voitu arvioida, sillä saadut kokonaiskromitulokset eivät olleet täysin luotettavia. Huolimatta tulosten epätarkkuudesta, työ tuotti tärkeää tietoa prosessin toiminnasta kromin suhteen. Työtä voidaan hyödyntää jatkossa monin tavoin prosessin kromitaseen seurannassa.

Heterogeneous mass transfer in fluidized beds by computational fluid dynamics

The main objective of this research is to estimate and characterize heterogeneous mass transfer coefficients in bench- and pilot-scale fluidized bed processes by the means of computational fluid dynamics (CFD). A further objective is to benchmark the heterogeneous mass transfer coefficients predicted by fine-grid Eulerian CFD simulations against empirical data presented in the scientific literature. First, a fine-grid two-dimensional Eulerian CFD model with a solid and gas phase has been designed. The model is applied for transient two-dimensional simulations of char combustion in small-scale bubbling and turbulent fluidized beds. The same approach is used to simulate a novel fluidized bed energy conversion process developed for the carbon capture, chemical looping combustion operated with a gaseous fuel. In order to analyze the results of the CFD simulations, two one-dimensional fluidized bed models have been formulated. The single-phase and bubble-emulsion models were applied to derive the average gas-bed and interphase mass transfer coefficients, respectively. In the analysis, the effects of various fluidized bed operation parameters, such as fluidization, velocity, particle and bubble diameter, reactor size, and chemical kinetics, on the heterogeneous mass transfer coefficients in the lower fluidized bed are evaluated extensively. The analysis shows that the fine-grid Eulerian CFD model can predict the heterogeneous mass transfer coefficients quantitatively with acceptable accuracy. Qualitatively, the CFD-based research of fluidized bed process revealed several new scientific results, such as parametrical relationships. The huge variance of seven orders of magnitude within the bed Sherwood numbers presented in the literature could be explained by the change of controlling mechanisms in the overall heterogeneous mass transfer process with the varied process conditions. The research opens new process-specific insights into the reactive fluidized bed processes, such as a strong mass transfer control over heterogeneous reaction rate, a dominance of interphase mass transfer in the fine-particle fluidized beds and a strong chemical kinetic dependence of the average gas-bed mass transfer. The obtained mass transfer coefficients can be applied in fluidized bed models used for various engineering design, reactor scale-up and process research tasks, and they consequently provide an enhanced prediction accuracy of the performance of fluidized bed processes.

Social Solidarity and Fear of Crime - Community and Individual Approaches in Responses to Mass Violence after School Shootings

The aim of this study was to examine community and individual approaches in responses to mass violence after the school shooting incidents in Jokela (November 2007) and Kauhajoki (September 2008), Finland. In considering the community approach, responses to any shocking criminal event may have integrative, as well as disintegrative effects, within the neighborhood. The integration perspective argues that a heinous criminal event within one’s community is a matter of offence to collectively held feelings and beliefs, and increases perceived solidarity; whereas the disintegration perspective suggests that a criminal event weakens the social fabric of community life by increasing fear of crime and mistrust among locals. In considering the individual approach, socio-demographic factors, such as one’s gender, are typically significant indicators, which explain variation in fear of crime. Beyond this, people are not equally exposed to violent crime and therefore prior victimization and event related experiences may further explain why people differ in their sensitivity to risk from mass violence. Finally, factors related to subjective mental health, such as depressed mood, are also likely to moderate individual differences in responses to mass violence. This study is based on the correlational design of four independent cross-sectional postal surveys. The sampling frames (N=700) for the surveys were the Finnish speaking adult population aged 18–74-years. The first mail survey in Jokela (n=330) was conducted between May and June 2008, approximately six months from the shooting incident at the local high-school. The second Jokela survey (n=278) was conducted in May–June of 2009, 18 months removed from the incident. The first survey in Kauhajoki (n=319) was collected six months after the incident at the local University of Applied Sciences, March– April 2009, and the second (n=339) in March–April 2010, approximately 18 months after the event. Linear and ordinal regression and path analysis are used as methods of analyses. The school shootings in Jokela and Kauhajoki were extremely disturbing events, which deeply affected the communities involved. However, based on the results collected, community responses to mass violence between the two localities were different. An increase in social solidarity appears to apply in the case of the Jokela community, but not in the case of the Kauhajoki community. Thus a criminal event does not necessarily impact the wider community. Every empirical finding is most likely related to different contextual and event-specific factors. Beyond this, community responses to mass violence in Jokela also indicated that the incident was related to a more general sense of insecurity and was also associating with perceived community deterioration and further suggests that responses to mass violence may have both integrating and disintegrating effects. Moreover, community responses to mass violence should also be examined in relation to broader social anxieties and as a proxy for generalized insecurity. Community response is an emotive process and incident related feelings are perhaps projected onto other identifiable concerns. However, this may open the door for social errors and, despite integrative effects, this may also have negative consequences within the neighborhood. The individual approach suggests that women are more fearful than men when a threat refers to violent crime. Young women (aged 18–34) were the most worried age and gender group as concerns perception of threat from mass violence at schools compared to young men (aged 18–34), who were also the least worried age and gender group when compared to older men. It was also found that concerns about mass violence were stronger among respondents with the lowest level of monthly household income compared to financially better-off respondents. Perhaps more importantly, responses to mass violence were affected by the emotional proximity to the event; and worry about the recurrence of school shootings was stronger among respondents who either were a parent of a school-aged child, or knew a victim. Finally, results indicate that psychological wellbeing is an important individual level factor. Respondents who expressed depressed mood consistently expressed their concerns about mass violence and community deterioration. Systematic assessments of the impact of school shooting events on communities are therefore needed. This requires the consolidation of community and individual approaches. Comparative study designs would further benefit from international collaboration across disciplines. Extreme school violence has also become a national concern and deeper understanding of crime related anxieties in contemporary Finland also requires community-based surveys.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Chemical changes in biomass during torrefaction

Torrefaction is moderate thermal treatment (~200-300 °C) of biomass in an inert atmosphere. The torrefied fuel offers advantages to traditional biomass, such as higher heating value, reduced hydrophilic nature, increased its resistance to biological decay, and improved grindability. These factors could, for instance, lead to better handling and storage of biomass and increased use of biomass in pulverized combustors. In this work, we look at several aspects of changes in the biomass during torrefaction. We investigate the fate of carboxylic groups during torrefaction and its dependency to equilibrium moisture content. The changes in the wood components including carbohydrates, lignin, extractable materials and ashforming matters are also studied. And at last, the effect of K on torrefaction is investigated and then modeled. In biomass, carboxylic sites are partially responsible for its hydrophilic characteristic. These sites are degraded to varying extents during torrefaction. In this work, methylene blue sorption and potentiometric titration were applied to measure the concentration of carboxylic groups in torrefied spruce wood. The results from both methods were applicable and the values agreed well. A decrease in the equilibrium moisture content at different humidity was also measured for the torrefied wood samples, which is in good agreement with the decrease in carboxylic group contents. Thus, both methods offer a means of directly measuring the decomposition of carboxylic groups in biomass during torrefaction as a valuable parameter in evaluating the extent of torrefaction. This provides new information to the chemical changes occurring during torrefaction. The effect of torrefaction temperature on the chemistry of birch wood was investigated. The samples were from a pilot plant at Energy research Center of the Netherlands (ECN). And in that way they were representative of industrially produced samples. Sugar analysis was applied to analyze the hemicellulose and cellulose content during torrefaction. The results show a significant degradation of hemicellulose already at 240 °C, while cellulose degradation becomes significant above 270 °C torrefaction. Several methods including Klason lignin method, solid state NMR and Py-GC-MS analyses were applied to measure the changes in lignin during torrefaction. The changes in the ratio of phenyl, guaiacyl and syringyl units show that lignin degrades already at 240 °C to a small extent. To investigate the changes in the extractives from acetone extraction during torrefaction, gravimetric method, HP-SEC and GC-FID followed by GC-MS analysis were performed. The content of acetone-extractable material increases already at 240 °C torrefaction through the degradation of carbohydrate and lignin. The molecular weight of the acetone-extractable material decreases with increasing the torrefaction temperature. The formation of some valuable materials like syringaresinol or vanillin is also observed which is important from biorefinery perspective. To investigate the change in the chemical association of ash-forming elements in birch wood during torrefaction, chemical fractionation was performed on the original and torrefied birch samples. These results give a first understanding of the changes in the association of ashforming elements during torrefaction. The most significant changes can be seen in the distribution of calcium, magnesium and manganese, with some change in water solubility seen in potassium. These changes may in part be due to the destruction of carboxylic groups. In addition to some changes in water and acid solubility of phosphorous, a clear decrease in the concentration of both chlorine and sulfur was observed. This would be a significant additional benefit for the combustion of torrefied biomass. Another objective of this work is studying the impact of organically bound K, Na, Ca and Mn on mass loss of biomass during torrefaction. These elements were of interest because they have been shown to be catalytically active in solid fuels during pyrolysis and/or gasification. The biomasses were first acid washed to remove the ash-forming matters and then organic sites were doped with K, Na, Ca or Mn. The results show that K and Na bound to organic sites can significantly increase the mass loss during torrefaction. It is also seen that Mn bound to organic sites increases the mass loss and Ca addition does not influence the mass loss rate on torrefaction. This increase in mass loss during torrefaction with alkali addition is unlike what has been found in the case of pyrolysis where alkali addition resulted in a reduced mass loss. These results are important for the future operation of torrefaction plants, which will likely be designed to handle various biomasses with significantly different contents of K. The results imply that shorter retention times are possible for high K-containing biomasses. The mass loss of spruce wood with different content of K was modeled using a two-step reaction model based on four kinetic rate constants. The results show that it is possible to model the mass loss of spruce wood doped with different levels of K using the same activation energies but different pre-exponential factors for the rate constants. Three of the pre-exponential factors increased linearly with increasing K content, while one of the preexponential factors decreased with increasing K content. Therefore, a new torrefaction model was formulated using the hemicellulose and cellulose content and K content. The new torrefaction model was validated against the mass loss during the torrefaction of aspen, miscanthus, straw and bark. There is good agreement between the model and the experimental data for the other biomasses, except bark. For bark, the mass loss of acetone extractable material is also needed to be taken into account. The new model can describe the kinetics of mass loss during torrefaction of different types of biomass. This is important for considering fuel flexibility in torrefaction plants.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.

Mass transfer and reaction kinetics in single droplet systems

As reactive extraction grown more and more popular in a variety of technological applications, optimizing its performance becomes more and more important. The process of complex formation is affected by a great number of both physical and chemical properties of all the components involved, and sometimes their interference with one another makes improving the effectiveness of such processes very difficult. In this Master’s Theses, the processes of complex formation between the aqueous phase - represented by copper sulfate water solution, and organic phase – represented by Acorga M5640 solvent extractor, were studied in order to establish the effect these components have on reactive extraction performance and to determine which step is bottlenecking the process the most.