Novel technology for preparation of optically active chemicals

Asymmetric synthesis using modified heterogeneous catalysts has gained lots of interest in the production of optically pure chemicals, such as pharmaceuticals, nutraceuticals, fragrances and agrochemicals. Heterogeneous modified catalysts capable of inducing high enantioselectivities are preferred in industrial scale due to their superior separation and handling properties. The topic has been intensively investigated both in industry and academia. The enantioselective hydrogenation of ethyl benzoylformate (EBF) to (R)-ethyl mandelate over (-)-cinchonidine (CD)-modified Pt/Al2O3 catalyst in a laboratory-scale semi-batch reactor was studied as a function of modifier concentration, reaction temperature, stirring rate and catalyst particle size. The main product was always (R)-ethyl mandelate while small amounts of (S)-ethyl mandelate were obtained as by product. The kinetic results showed higher enantioselectivity and lower initial rates approaching asymptotically to a constant value as the amount of modifier was increased. Additionally, catalyst deactivation due to presence of impurities in the feed was prominent in some cases; therefore activated carbon was used as a cleaning agent of the raw material to remove impurities prior to catalyst addition. Detailed characterizations methods (SEM, EDX, TPR, BET, chemisorption, particle size distribution) of the catalysts were carried out. Solvent effects were also studied in the semi-batch reactor. Solvents with dielectric constant (e) between 2 and 25 were applied. The enantiomeric excess (ee) increased with an increase of the dielectric coefficient up to a maximum followed by a nonlinear decrease. A kinetic model was proposed for the enantioselectivity dependence on the dielectric constant based on the Kirkwood treatment. The non-linear dependence of ee on (e) successfully described the variation of ee in different solvents. Systematic kinetic experiments were carried out in the semi-batch reactor. Toluene was used as a solvent. Based on these results, a kinetic model based on the assumption of different number of sites was developed. Density functional theory calculations were applied to study the energetics of the EBF adsorption on pure Pt(1 1 1). The hydrogenation rate constants were determined along with the adsorption parameters by non-linear regression analysis. A comparison between the model and the experimental data revealed a very good correspondence. Transient experiments in a fixed-bed reactor were also carried out in this work. The results demonstrated that continuous enantioselective hydrogenation of EBF in hexane/2-propanol 90/10 (v/v) is possible and that continuous feeding of (-)-cinchonidine is needed to maintain a high steady-state enantioselectivity. The catalyst showed a good stability and high enantioselectivity was achieved in the fixed-bed reactor. Chromatographic separation of (R)- and (S)-ethyl mandelate originating from the continuous reactor was investigated. A commercial column filled with a chiral resin was chosen as a perspective preparative-scale adsorbent. Since the adsorption equilibrium isotherms were linear within the entire investigated range of concentrations, they were determined by pulse experiments for the isomers present in a post-reaction mixture. Breakthrough curves were measured and described successfully by the dispersive plug flow model with a linear driving force approximation. The focus of this research project was the development of a new integrated production concept of optically active chemicals by combining heterogeneous catalysis and chromatographic separation technology. The proposed work is fundamental research in advanced process technology aiming to improve efficiency and enable clean and environmentally benign production of enantiomeric pure chemicals.

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.

Osinkosuhteen, osinkotuoton ja omavaraisuusasteen sijoitusstrateginen hyödynnettävyys Suomen osakemarkkinoilla

Tutkielman tavoitteena on selvittää osinkosuhteen, osinkotuoton ja omavaraisuusasteen vaikutus osakkeesta saatavaan kokonaistuottoon Suomenosakemarkkinoilla vuosina 2002–2013. Muuttujien kausaliteettisuhde kokonaistuottoon selvitetään regressioanalyysilla. Portfolioanalyysin avulla tutkitaan valittujen tunnuslukujen toimivuutta sijoitusstrategiana. Tutkimuksessa muodostetaan myös osinkosuhteen ja osinkotuoton yhdistelmänä tunnusluku, jolla pyritään maksimoimaan sijoittajan saama tuotto. Empiiriset tulokset osoittivat, että sijoittaja pystyy saavuttamaan ylituottoja hyödyntämällä edellä mainittuja tunnuslukuja osakevalinnassa. Osinkotuoton ja osakkeen kokonaistuoton välillä havaittiin positiivinen lineaarinen korrelaatio. Portfolioanalyysin perusteella sekä omavaraisuusasteen että osinkosuhteen osalta vaikutus sijoittajan saamaan riskisuhteutettuun kokonaistuottoon on ei-lineaarinen. Valittuja tunnuslukuja ja menetelmiä hyödyntäen sijoittaja saa parhaimman riskisuhteutetun tuoton valitsemalla sijoitussalkkuunsa osakkeita, joiden osinkosuhteen arvo sijoittuu toiseksi ylimpään kvartiiliin sekä osakkeita, joiden osinkotuotto on korkea ja omavaraisuusaste on samanaikaisesti alhainen.

Toward including the effect of manufacturing processes in the pre-estimated losses of the switched reluctance motor

The estimation of losses plays a key role in the process of building any electrical machine. How to estimate those losses while designing any machine; by obtaining the characteristic of the electrical steel from the catalogue and calculate the losses. However, this way is inaccurate since the electrical steel performs several manufacturing processes during the process of building any machine, which affects directly the magnetic property of the electrical steel and accordingly the characteristic of the electrical steel will be affected. That means the B–H curve of the steel that was obtained from the catalogue will be changed. Moreover, during loading and rotating the machine, some important changes occur to the B–H characteristic of the electrical steel such as the stress on the laminated iron. Accordingly, the pre-estimated losses are completely far from the actual losses because they were estimated based on the data of the electrical steel obtained from the catalogue. So in order to estimate the losses precisely significant factors of the manufacturing processes must be included. The paper introduces the systematic estimation of the losses including the effect of one of the manufacturing factors. Similarly, any other manufacturing factor can be included in the pre-designed losses estimations.

Modeling a Network of Heat Exchangers

This research work addresses the problem of building a mathematical model for the given system of heat exchangers and to determine the temperatures, pressures and velocities at the intermediate positions. Such model could be used in nding an optimal design for such a superstructure. To limit the size and computing time a reduced network model was used. The method can be generalized to larger network structures. A mathematical model which includes a system of non-linear equations has been built and solved according to the Newton-Raphson algorithm. The results obtained by the proposed mathematical model were compared with the results obtained by the Paterson approximation and Chen's Approximation. Results of this research work in collaboration with a current ongoing research at the department will optimize the valve positions and hence, minimize the pumping cost and maximize the heat transfer of the system of heat exchangers.

Spectrally Tuned Lanthanide Photoluminescence for Point-of-Care Testing

Point-of-care (POC) –diagnostics is a field with rapidly growing market share. As these applications become more widely used, there is an increasing pressure to improve their performance to match the one of a central laboratory tests. Lanthanide luminescence has been widely utilized in diagnostics because of the numerous advantages gained by the utilization of time-resolved or anti-Stokes detection. So far the use of lanthanide labels in POC has been scarce due to limitations set by the instrumentation required for their detection and the shortcomings, e.g. low brightness, of these labels. Along with the advances in the research of lanthanide luminescence, and in the field of semiconductors, these materials are becoming a feasible alternative for the signal generation also in the future POC assays. The aim of this thesis was to explore ways of utilizing time-resolved detection or anti-Stokes detection in POC applications. The long-lived fluorescence for the time-resolved measurement can be produced with lanthanide chelates. The ultraviolet (UV) excitation required by these chelates is cumbersome to produce with POC compatible fluorescence readers. In this thesis the use of a novel light-harvesting ligand was studied. This molecule can be used to excite Eu(III)-ions at wavelengths extending up to visible part of the spectrum. An enhancement solution based on this ligand showed a good performance in a proof-of-concept -bioaffinity assay and produced a bright signal upon 365 nm excitation thanks to the high molar absorptivity of the chelate. These features are crucial when developing miniaturized readers for the time-resolved detection of fluorescence. Upconverting phosphors (UCPs) were studied as an internal light source in glucose-sensing dry chemistry test strips and ways of utilizing their various emission wavelengths and near-infrared excitation were explored. The use of nanosized NaYF :Yb3+,Tm3+-particles enabled the replacement of an external UV-light source with a NIR-laser and gave an additional degree of freedom in the optical setup of the detector instrument. The new method enabled a blood glucose measurement with results comparable to a current standard method of measuring reflectance. Microsized visible emitting UCPs were used in a similar manner, but with a broad absorbing indicator compound filtering the excitation and emission wavelengths of the UCP. This approach resulted in a novel way of benefitting from the non-linear relationship between the excitation power and emission intensity of the UCPs, and enabled the amplification of the signal response from the indicator dye.

Optimizing the Performance of a Heat Exchanger Network

This research is the continuation and a joint work with a master thesis that has been done in this department recently by Hemamali Chathurangani Yashika Jayathunga. The mathematical system of the equations in the designed Heat Exchanger Network synthesis has been extended by adding a number of equipment; such as heat exchangers, mixers and dividers. The solutions of the system is obtained and the optimal setting of the valves (Each divider contains a valve) is calculated by introducing grid-based optimization. Finding the best position of the valves will lead to maximization of the transferred heat in the hot stream and minimization of the pressure drop in the cold stream. The aim of the following thesis will be achieved by practicing the cost optimization to model an optimized network.

Modelling the exothermic heat and the demand of cooling in paper and pulp industry biological wastewater treatment

This master thesis presents a study on the requisite cooling of an activated sludge process in paper and pulp industry. The energy consumption of paper and pulp industry and it’s wastewater treatment plant in particular is relatively high. It is therefore useful to understand the wastewater treatment process of such industries. The activated sludge process is a biological mechanism which degrades carbonaceous compounds that are present in waste. The modified activated sludge model constructed here aims to imitate the bio-kinetics of an activated sludge process. However, due to the complicated non-linear behavior of the biological process, modelling this system is laborious and intriguing. We attempt to find a system solution first using steady-state modelling of Activated Sludge Model number 1 (ASM1), approached by Euler’s method and an ordinary differential equation solver. Furthermore, an enthalpy study of paper and pulp industry’s vital pollutants was carried out and applied to revise the temperature shift over a period of time to formulate the operation of cooling water. This finding will lead to a forecast of the plant process execution in a cost-effective manner and management of effluent efficiency. The final stage of the thesis was achieved by optimizing the steady state of ASM1.