SEPARATION OF SATURED AND UNSATURATED FATTY ACIDS FROM PALM FATTY ACIDS DISTILLATES IN CONTINUOUS MULTISTAGE COUNTERCURRENT COLUMNS WITH SUPERCRITICAL CARBON DIOXIDE AS SOLVENT: A PROCESS DESIGN METHODOLOGY

In this work the separation of multicomponent mixtures in counter-current columns with supercritical carbon dioxide has been investigated using a process design methodology. First the separation task must be defined, then phase equilibria experiments are carried out, and the data obtained are correlated with thermodynamic models or empirical functions. Mutual solubilities, Ki-values, and separation factors aij are determined. Based on this data possible operating conditions for further extraction experiments can be determined. Separation analysis using graphical methods are performed to optimize the process parameters. Hydrodynamic experiments are carried out to determine the flow capacity diagram. Extraction experiments in laboratory scale are planned and carried out in order to determine HETP values, to validate the simulation results, and to provide new materials for additional phase equilibria experiments, needed to determine the dependence of separation factors on concetration. Numerical simulation of the separation process and auxiliary systems is carried out to optimize the number of stages, solvent-to-feed ratio, product purity, yield, and energy consumption. Scale-up and cost analysis close the process design. The separation of palmitic acid and (oleic+linoleic) acids from PFAD-Palm Fatty Acids Distillates was used as a case study.

Estimation of wind storm impacts over Western Germany under future climate conditions using a statistical-dynamical downscaling approach

A statistical–dynamical regionalization approach is developed to assess possible changes in wind storm impacts. The method is applied to North Rhine-Westphalia (Western Germany) using the FOOT3DK mesoscale model for dynamical downscaling and ECHAM5/OM1 global circulation model climate projections. The method first classifies typical weather developments within the reanalysis period using K-means cluster algorithm. Most historical wind storms are associated with four weather developments (primary storm-clusters). Mesoscale simulations are performed for representative elements for all clusters to derive regional wind climatology. Additionally, 28 historical storms affecting Western Germany are simulated. Empirical functions are estimated to relate wind gust fields and insured losses. Transient ECHAM5/OM1 simulations show an enhanced frequency of primary storm-clusters and storms for 2060–2100 compared to 1960–2000. Accordingly, wind gusts increase over Western Germany, reaching locally +5% for 98th wind gust percentiles (A2-scenario). Consequently, storm losses are expected to increase substantially (+8% for A1B-scenario, +19% for A2-scenario). Regional patterns show larger changes over north-eastern parts of North Rhine-Westphalia than for western parts. For storms with return periods above 20 yr, loss expectations for Germany may increase by a factor of 2. These results document the method's functionality to assess future changes in loss potentials in regional terms.

Estimativa da irradiação total sobre uma superfície inclinada a partir da irradiação global na horizontal

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Sphere of influence and gravitational capture radius: a dynamical approach

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimativa da irradiação total sobre uma superfície inclinada a partir da irradiação global na horizontal

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Separation of satured and unsaturated fatty acids from palm fatty acids distillates in continuous multistage countercurrent columns with supercritical carbon dioxide as solvent: a process design methodology

In this work the separation of multicomponent mixtures in counter-current columns with supercritical carbon dioxide has been investigated using a process design methodology. First the separation task must be defined, then phase equilibria experiments are carried out, and the data obtained are correlated with thermodynamic models or empirical functions. Mutual solubilities, Ki-values, and separation factors aij are determined. Based on this data possible operating conditions for further extraction experiments can be determined. Separation analysis using graphical methods are performed to optimize the process parameters. Hydrodynamic experiments are carried out to determine the flow capacity diagram. Extraction experiments in laboratory scale are planned and carried out in order to determine HETP values, to validate the simulation results, and to provide new materials for additional phase equilibria experiments, needed to determine the dependence of separation factors on concetration. Numerical simulation of the separation process and auxiliary systems is carried out to optimize the number of stages, solvent-to-feed ratio, product purity, yield, and energy consumption. Scale-up and cost analysis close the process design. The separation of palmitic acid and (oleic+linoleic) acids from PFAD-Palm Fatty Acids Distillates was used as a case study.

Dynamical Systems in Description of Nonlinear Recursive Regression Transformers

The task of approximation-forecasting for a function, represented by empirical data was investigated. Certain class of the functions as forecasting tools: so called RFT-transformers, – was proposed. Least Square Method and superposition are the principal composing means for the function generating. Besides, the special classes of beam dynamics with delay were introduced and investigated to get classical results regarding gradients. These results were applied to optimize the RFT-transformers. The effectiveness of the forecast was demonstrated on the empirical data from the Forex market.

Continuous VM-ADCP (vessel-mounted acoustic Doppler current profiler) profiles of horizontal velocities and raw acoustic gain control data during Polarstern cruise ANT-XVIII/2

The mixing regime of the upper 180 m of a mesoscale eddy in the vicinity of the Antarctic Polar Front at 47° S and 21° E was investigated during the R.V. Polarstern cruise ANT-XVIII/2 within the scope of the iron fertilization experiment EisenEx. On the basis of hydrographic CTD and ADCP profiles we deduced the vertical diffusivity Kz from two different parameterizations. Since these parameterizations bear the character of empirical functions, based on theoretical and idealized assumptions, they were inter alia compared with Cox-number and Thorpe-scale related diffusivities deduced from microstructure measurements, which supplied the first direct insights into turbulence of this ocean region. Values of Kz in the range of 10**-4 - 10**-3 m**2/s appear as a rather robust estimate of vertical diffusivity within the seasonal pycnocline. Values in the mixed layer above are more variable in time and reach 10**-1 m**2/s during periods of strong winds. The results confirm a close agreement between the microstructure-based eddy diffusivities and eddy diffusivities calculated after the parameterization of Pacanowski and Philander [1981, Journal of Physical Oceanography 11, 1443-1451, doi:10.1175/1520-0485(1981)011<1443:POVMIN>2.0.CO;2].

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

Variability of the Atlantic thermohaline circulation described by three-dimensional empirical orthogonal functions

We describe the use of bivariate 3d empirical orthogonal functions (EOFs) in characterising low frequency variability of the Atlantic thermohaline circulation (THC) in the Hadley Centre global climate model, HadCM3. We find that the leading two modes are well correlated with an index of the meridional overturning circulation (MOC) on decadal timescales, with the leading mode alone accounting for 54% of the decadal variance. Episodes of coherent oscillations in the sub-space of the leading EOFs are identified; these episodes are of great interest for the predictability of the THC, and could indicate the existence of different regimes of natural variability. The mechanism identified for the multi-decadal variability is an internal ocean mode, dominated by changes in convection in the Nordic Seas, which lead the changes in the MOC by a few years. Variations in salinity transports from the Arctic and from the North Atlantic are the main feedbacks which control the oscillation. This mode has a weak feedback onto the atmosphere and hence a surface climatic influence. Interestingly, some of these climate impacts lead the changes in the overturning. There are also similarities to observed multi-decadal climate variability.

Empirical Orthogonal Functions: The Medium is the Message

Empirical orthogonal function (EOF) analysis is a powerful tool for data compression and dimensionality reduction used broadly in meteorology and oceanography. Often in the literature, EOF modes are interpreted individually, independent of other modes. In fact, it can be shown that no such attribution can generally be made. This review demonstrates that in general individual EOF modes (i) will not correspond to individual dynamical modes, (ii) will not correspond to individual kinematic degrees of freedom, (iii) will not be statistically independent of other EOF modes, and (iv) will be strongly influenced by the nonlocal requirement that modes maximize variance over the entire domain. The goal of this review is not to argue against the use of EOF analysis in meteorology and oceanography; rather, it is to demonstrate the care that must be taken in the interpretation of individual modes in order to distinguish the medium from the message.

Testing production functions used in empirical growth studies

We estimate and test two alternative functional forms, which have been used in the growth literature, representing the aggregate production function for a panel of countries: the model of Mankiw, Romer and Weil (Quarterly Journal of Economics, 1992), and a mincerian formulation of schooling-returns to skills. Estimation is performed using instrumental-variable techniques, and both functional forms are confronted using a Box-Cox test, since human capital inputs enter in levels in the mincerian specification and in logs in the extended neoclassical growth model.

Testing production functions used in empirical growth studies

We estimate and test two alternative functional forms representing the aggregate production function for a panel of countries: the extended neoclassical growth model, and a mincerian formulation of schooling-returns to skills. Estimation is performed using instrumentalvariable techniques, and both functional forms are confronted using a Box-Cox test, since human capital inputs enter in levels in the mincerian specification and in logs in the extended neoclassical growth model. Our evidence rejects the extended neoclassical growth model in favor of the mincerian specification, with an estimated capital share of about 42%, a marginal return to education of about 7.5% per year, and an estimated productivity growth of about 1.4% per year. Differences in productivity cannot be disregarded as an explanation of why output per worker varies so much across countries: a variance decomposition exercise shows that productivity alone explains 54% of the variation in output per worker across countries.