Towards optimal fractionation of lignocellulosic biomass using switchable ionic liquids

En ny familj av reversibla (switchable) joniska vätskor (SIL) innehållande 1,8-diazobicyklo-[5.4.0]-undek-7-en (DBU), en molekyl innehållande en eller flera hydroxyl- grupper (t.ex. glycerol) och en sur gas (CO2, SO2) syntetiserades via en enkel procedur samt karakteriserades. [DBU][karbonat] eller [sulfonat] bildades ur en respektive icke-jonisk blandning av en molekylär, organisk polyol (eller ennan molekyl innehållande en OH-grupp) och en amidinbas under bubblandet av en sur gas. Därtill kunde den joniska vätskan omvandlas tillbaka till sina beståndsdelar med hjälp av att upphetta och/eller bubbla en inert gas såsom kväve genom vätskan. SIL- strukturerna kartlades med bl.a. NMR- och FTIR- spektroskopi. Omvandlingen från lågpolära (molekylära) vätskor till högpolära joniska vätskor (SIL) bekräftades även genom att observera förändringar i deras fysikaliska egenskaper, såsom viskositet och färg. Nedbrytningstemperaturerna hos SILs bestämdes med hjälp av termogravimetrisk analys (TGA) som antydde att nedbrytningstemperaturen hos de syntetiserade föreningarna log mellan 50 och 200oC. De nya joniska vätskorna uppvisade högre nedbrytningstemperaturer jämfört med i litteraturen tidigare förekommande exempel och kunde därför tillämpas på flera ändamål. Därtill, reversibla (switchable) joniska vätskor uppbyggda av bl.a. alkoholer, antingen hexanol eller butanol, och CO2 samt en amidin (DBU) användes vid upplösning och fraktionering av ved. Joniska vätskor syntetiserade ur glycerol och sura gaser tillsammans med amidiner användes även för fraktionering av andra lignocellulosor såsom färsk björk (Betula pendula). Björkflis utsattes för behandling, för en period på en till fem dagar vid 100oC och under atmosfäriskt tryck. Alla syntetiserade joniska vätskor visade sig vara relativt neutrala i avseende på upplösning och avlägsnandet av lignin. Slutligen, optimala fraktioneringprocessbetingelser för ved med reversibla joniska vätskor kartlades. Fraktionering av vedbiomassa med dessa joniska vätskor uppvisade sig att vara en selektiv och effektiv metod för extraktion av olika komponenter från lignocellulosa. Den olösta fraktionen hos en vedflis, närmast cellulosa, fibrillerades. -------------------------------------------------------------------------------------------------------------------- Tässä työssä kehitettiin perhe uuden tyyppisiä, reversiibeleitä (switchable) ioninesteitä ( SIL ) joka koostuvat orgaanisesta super-emäksestä kuten 1,8- diatsabisyklo [ 5.4.0] undek- 7-eeni (DBU ) ja yhden tai useampia hydroksyyliryhmiä sisältältävästä molekyylistä (esim. glyseroli) ja happamasta kaasusta (CO2 , SO2) yksinkertaisen menetelmän avulla. [DBU] [ karbonaatti] tai [sulfonaatti] syntetisoitiin kunkin lähtöaineen seoksista kuplittamalla seosta happamalla kaasulla jolloin eksoterminen reaktio tapahtui ja ioninen neste syntyi. Ioniset nesteet voitiin palauttaa takaisin lähtöaineseokseksi kuumentamalla ja/tai kuplittamalla neutraalia kaasua (esim. typpi) seoksen läpi. SIL rakenteet määritettiin ja niiden ominaisuudet kartoitettiin eri menetelmillä, mukaan lukien NMR- ja FTIR -spektroskopia. Ionisen, korkeapoläärisen nesteen syntyminen todennettiin myös viskositeettimittauksilla ja värinmuutoksilla käyttäen hyväksi polariteetti-indikaattoria (Nile red). Myös hajoamislämpötilat määritettiin termogravimetrisellä analyysillä (TGA) ja todettiin että syntetisoitujen yhdisteiden hajoamislämpötila oli välillä 50 ja 200oC . Näiden uusien reversiibeleiden ioninesteiden hajoamisämpötilat olivat korkeammat verrattuna kirjallisuudessa aikaisemmin mainittuihin esimerkkeihin joten niitä voidaan soveltaa useisiin tarkoituksiin. Myös ioninesteitä jotka sisälsivät primäärejä alkoholeja rakennusaineina syntetisoitiin ja hyödynnettiin puun fraktioinnissa. Männyn ja kuusen lisäksi tuoreita koivulastuja onnistuttiin fraktioimaan miedoissa olosuhteissa. Kaikkien syntetisoitujen ioninesteiden todettiin olevan suhteellisen neutraaleja ligniinin liuotuksen suhteen. Vielä, optimaaliset fraktiointiolosuhteet määritettiin ryhmälle reversiibeleitä ioninesteitä ja näiden uudenlaisten ioninesteiden todettiin olevan tehokkaita puun ja muiden lignoselluloosien eri fraktioiden liuotuksessa. Liukenematon osa puulastua joka oli lähinnä selluloosaa fibrilloitui.

Excessive functions, Appell polynomials and optimal stopping

The main topic of the thesis is optimal stopping. This is treated in two research articles. In the first article we introduce a new approach to optimal stopping of general strong Markov processes. The approach is based on the representation of excessive functions as expected suprema. We present a variety of examples, in particular, the Novikov-Shiryaev problem for Lévy processes. In the second article on optimal stopping we focus on differentiability of excessive functions of diffusions and apply these results to study the validity of the principle of smooth fit. As an example we discuss optimal stopping of sticky Brownian motion. The third research article offers a survey like discussion on Appell polynomials. The crucial role of Appell polynomials in optimal stopping of Lévy processes was noticed by Novikov and Shiryaev. They described the optimal rule in a large class of problems via these polynomials. We exploit the probabilistic approach to Appell polynomials and show that many classical results are obtained with ease in this framework. In the fourth article we derive a new relationship between the generalized Bernoulli polynomials and the generalized Euler polynomials.

Simulation of the Interaction Mean Free Path between Neutrons and TRISO Particles

The interaction mean free path between neutrons and TRISO particles is simulated using scripts written in MATLAB to solve the increasing error present with an increase in the packing factor in the reactor physics code Serpent. Their movement is tracked both in an unbounded and in a bounded space. Their track is calculated, depending on the program, linearly directly using the position vectors of the neutrons and the surface equations of all the fuel particles; by dividing the space in multiple subspaces, each of which contain a fraction of the total number of particles, and choosing the particles from those subspaces through which the neutron passes through; or by choosing the particles that lie within an infinite cylinder formed on the movement axis of the neutron. The estimate from the current analytical model, based on an exponential distribution, for the mean free path, utilized by Serpent, is used as a reference result. The results from the implicit model in Serpent imply a too long mean free path with high packing factors. The received results support this observation by producing, with a packing factor of 17 %, approximately 2.46 % shorter mean free path compared to the reference model. This is supported by the packing factor experienced by the neutron, the simulation of which resulted in a 17.29 % packing factor. It was also observed that the neutrons leaving from the surfaces of the fuel particles, in contrast to those starting inside the moderator, do not follow the exponential distribution. The current model, as it is, is thus not valid in the determination of the free path lengths of the neutrons.

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 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.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Optimal processing chain for waste power plant

This study is done to examine waste power plant’s optimal processing chain and it is important to consider from several points of view on why one option is better than the other. This is to insure that the right decision is made. Incineration of waste has devel-oped to be one decent option for waste disposal. There are several legislation matters and technical options to consider when starting up a waste power plant. From the tech-niques pretreatment, burner and flue gas cleaning are the biggest ones to consider. The treatment of incineration residues is important since it can be very harmful for the envi-ronment. The actual energy production from waste is not highly efficient and there are several harmful compounds emitted. Recycling of waste before incineration is not very typical and there are not many recycling options for materials that cannot be easily re-cycled to same product. Life cycle assessment is a good option for studying the envi-ronmental effect of the system. It has four phases that are part of the iterative study process. In this study the case environment is a waste power plant. The modeling of the plant is done with GaBi 6 software and the scope is from gate-to-grave. There are three different scenarios, from which the first and second are compared to each other to reach conclusions. Zero scenario is part of the study to demonstrate situation without the power plant. The power plant in this study is recycling some materials in scenario one and in scenario two even more materials and utilize the bottom ash more ways than one. The model has the substitutive processes for the materials when they are not recycled in the plant. The global warming potential results show that scenario one is the best option. The variable costs that have been considered tell the same result. The conclusion is that the waste power plant should not recycle more and utilize bottom ash in a number of ways. The area is not ready for that kind of utilization and production from recycled materials.

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.