Global warming is becoming ever increasing concern in our society. As such the likelihood of a carbon tax in the US is becoming increasingly likely. A carbon tax will be expensive enough that coal-based power plants will either have to install carbon capture technology or close. The two front runner technologies for carbon capture are amine scrubbing, and oxyfuel combustion. The downside is that both of these technologies increase power generation cost in a new plant by about 80% and have up to a 30% parasitic load, which reduces the cycle efficiency, that is, the power production per unit fuel consumed, by the same 30%. Retrofitting existing plants by either of these technologies is even more expensive and inefficient since it requires major modifications or replacement of the existing plant in addition to the new capture technology. Sustainable Energy Solutions (SES) has developed a carbon capture technology named cryogenic carbon capture (CCC). CCC is a process by which the flue gas cools to the point that CO2 desublimates. This process is more efficient, cheaper, and has about half of the parasitic load of other technologies, approaching the theoretical minimum in CO2 separation within heat exchanger and compressor efficiencies. This thesis conceptually describes, experimentally characterizes, and theoretically models one desublimating heat exchanger as an integral part of the CCC process. A spray tower conceptually developed by SES and theoretically and experimentally explored in previous work at lab scale is developed at bench scale in this work with accompanying major modifications to the theoretical model. It sprays a cold contact liquid to cool warm gas (relative to the contact liquid) that travels up the tower. Nominal operating temperatures are around -120 to -130 °C for 90% and 99% capture, respectively. Once the flue gas cools enough, CO2 desublimates on the liquid droplet surfaces and forms a slurry with the contact liquid. This spray tower can achieve arbitrarily high CO2 capture efficiency, depending on the temperature of the exiting gas and other operational variables. The experimental data outlined here varied these operational parameters over broad ranges to achieve capture efficiencies of 55% to greater than 95%, providing a robust data set for model comparison. The operational parameters explored include liquid temperature, liquid flow rate, gas flow rate, and droplet size. These data validated a transport and design model that predicts capture for future scale-up and design of the project. The data and model indicate expected behaviors with most of these variables and a dependence on internal droplet temperature profiles that may be higher than expected. This project significantly advanced the experimental database and the model capabilities that describe the spray tower.

Motion capture(“MoCap”) has been used for a long time in the movie and videogame industries to animate digital characters. This technology commonly requires a studio and expensive stationary equipment. However, in recent years markerless MoCap has emerged. This is a technology that uses machine learning to estimate and reproduce the movements of humans. This technology can be used with a single video camera thus making it more accessible. This study relates to research on motion capture, machine learning and computer animation. The study examines a selection of markerless MoCap tools available on the market with amateurs and small businesses as target audiences. This to explore to which extent markerless MoCap for amateurs is suitable for use. The research questions asked in this study are: How well do these tools recreate motions from an animation? How are these results affected by aggravating circumstances? How do the results of the tools differ from each other? To explore these questions, a selection of five markerless MoCap services was made. These five services were then tested to study their performances in different aggravating circumstances. An original animation was created and used in these tests. The results from these tests were analyzed using a qualitative visual analysis and a numerical analysis of extreme values. The study found the tools could not accurately reproduce the animation they were given to process. The most prominent problem being that of depth perception, which resulted in the processed animations often deviating in depth. The services also had obvious problems with recreating arms. The study also found that some of the different aggravating circumstances affected the results more than others. The results of this study shows that markerless MoCap for amateurs still has development ahead of it before the technology can be considered an effective tool. / Motion Capture (“MoCap”) har länge använts inom film- och spelindustrin för att animera digitala karaktärer. MoCap i storskalig produktion kräver dock vanligtvis en studio och dyr utrustning. Men på senare år har Markerless MoCap vuxit fram. Det är en teknik som använder sig av maskininlärning för att estimera och avbilda en persons rörelser. Denna kan användas med enbart en videokamera vilket gör tekniken lättillgänglig. Denna studie relaterar till forskning som berör Motion Capture, 3D-datoranimation, AI och maskininlärning. Studien undersöker ett urval av Markerless MoCap-verktyg som finns tillgängliga allmänheten, med amatörer och småföretag som målgrupp. Detta i syfte att undersöka i vilken utsträckning markerless MoCap för amatörer är lämplig för bruk. Problemformuleringen i denna studie är: Hur väl återskapar programmen rörelserna från en animation? Hur påverkas detta resultat av försvårande omständigheter? Hur skiljer sig dessa programs resultat från varandra? För att undersöka dessa frågor gjordes ett urval av fem Markerless MoCap-verktyg. Dessa fem verktyg testades för att studera verktygens prestationer under olika försvårande omständigheter. En egenproducerad animation användes i dessa tester. Resultaten från dessa tester analyserades med en kvalitativ visuell analys och en numerisk analys av extremvärden. Studien fann att verktygen inte med precision kunde återge den animation de fått att avbilda. Tydligast var problemet med djupseendet, vilket resulterade i att de bearbetade animationerna ofta avvek i djupled. Verktygen hade också påtagliga problem med att avbilda armar. Studien fann även att vissa försvårande omständigheter hade större effekt än andra. Den här studiens resultat visar att Markerless MoCap för amatörer fortfarande har utveckling kvar innan tekniken kan betraktas som ett effektivt verktyg.

Carbon capture, utilization and storage (CCUS) in confined saline aquifers in sedimentary formations has the potential to reduce the impact of fossil fuel combustion on climate change by storing CO2 in geologic formations in perpetuity. At PT conditions relevant to CCUS, CO2 is less dense than the pre-existing brine in the formation, and the more buoyant CO2 will migrate to the top of the formation where it will be in contact with cap rock. A typical cap rock is clay-rich shale, and interactions between shales and CO2 are poorly understood at PT conditions appropriate for CCUS in saline formations. In this study, the interaction of CO2 with clay minerals in the cap rock overlying a saline formation has been examined, using Na-rich montmorillonite as an analog for clay-rich shale. Attenuated Total Reflectance -- Fourier Transform Infrared Spectroscopy (ATR -FTIR) was used to identify potential crystallographic sites (AlAlOH, AlMgOH and interlayer space) where CO2 could interact with montmorillonite at 35"C and 50"C and from 0-1200 psi.  Analysis of the data indicates that CO2 that is preferentially incorporated into the interlayer space, with dehydrated montmorillonite capable of incorporating more CO2 than hydrated montmorillonite. No evidence of chemical interactions between CO2 and montmorillonite were identified, and no spectroscopic evidence for carbonate mineral formation was observed.  Further work is needed to determine if reservoir seal quality is more likely to be degraded or enhanced by CO2 - montmorillonite interactions. / Master of Science

No description available.

No description available.

yes / The non-isothermal kinetic and thermal behaviour of a naturally formed dolomite in conditions that approach in situ CO2 capture in chemical looping reforming, were investigated. The performance of this dolomite was studied at micro-scale in ‘dry’ conditions, as well as at macro-scale in ‘dry’ and ‘wet’ conditions to investigate the effects of scale (3 mg, 2.5 g), partial pressures of CO2 (<15 kPa) and steam, and deactivation upon limited cycling. The carbonation and calcination kinetics were modelled using an improved iterative Coats–Redfern method. Increasing CO2 partial pressures on the ‘dry’ macroscale exacerbated the experimental carbonation conversions in an inversely proportional trend when compared with those at micro-scale. The presence of steam had a positive effect on CO2 chemisorption. Steam had a negligible influence on the calcination activation energies. The activation energies of carbonation were increased for the experiments at the highest CO2 partial pressures under wet conditions.

Noninvasive methods for monitoring wildlife species have revolutionized the way population parameters, such as population density and survival and recruitment rates, are estimated while accounting for imperfect detection using capture-recapture models. Reliable estimates of these parameters are vital information required for making sound conservation decisions; however to date, noninvasive sampling methods have been of limited use for a vast number of species which are difficult to identify to the individual level–a general requirement of capture-recapture models. Capture-recapture models that utilize partial identity information have only recently been introduced and have not been extended to most types of noninvasive sampling scenarios in a manner that uses the spatial location where noninvasive samples were collected to further inform complete identity (i.e. spatial partial identity models). Herein, I extend the recently introduced spatial partial identity models to the noninvasive methods of remote cameras for species that are difficult to identify from photographs and DNA from hair or scat samples. The ability of these novel models to improve parameter estimation and extend study design options are investigated and the methods are made accessible to applied ecologists via statistical software. This research has the potential to greatly improve wildlife conservation decisions by improving our knowledge of parameters related to population structure and dynamics that inform those decisions. Unfortunately, many species of conservation concern (e.g., Florida panthers, Andean bears) are managed without having the necessary information on population status or trends, largely a result of the cost and difficulty of studying species in decline and because of the difficulty of applying statistical models to sparse data, which can produce imprecise and biased estimates of population parameters. By leveraging partial identity information in noninvasive samples, the models I developed will improve these parameter estimates and allow noninvasive methods to be used for more species, leading to more informed conservation decisions, and a more efficient allocation of conservation resources across species and populations. / Ph. D. / Noninvasive methods for monitoring wildlife species have revolutionized the way population parameters, such as population density and survival and recruitment rates, are estimated while accounting for imperfect detection using capture-recapture models. Reliable estimates of these parameters are vital information required for making sound conservation decisions; however to date, noninvasive sampling methods have been of limited use for a vast number of species which are difficult to identify to the individual levela general requirement of capture-recapture models. Capture-recapture models that utilize partial identity information have only recently been introduced and have not been extended to most types of noninvasive sampling scenarios in a manner that uses the spatial location where noninvasive samples were collected to further inform complete identity (i.e. spatial partial identity models). Herein, I extend the recently introduced spatial partial identity models to the noninvasive methods of remote cameras for species that are difficult to identify from photographs and DNA from hair or scat samples. The ability of these novel models to improve parameter estimation and extend study design options are investigated and the methods are made accessible to applied ecologists via statistical software. This research has the potential to greatly improve wildlife conservation decisions by improving our knowledge of parameters related to population structure and dynamics that inform those decisions. Unfortunately, many species of conservation concern (e.g., Florida panthers, Andean bears) are managed without having the necessary information on population status or trends, largely a result of the cost and difficulty of studying species in decline and because of the difficulty of applying statistical models to sparse data, which can produce imprecise and biased estimates of population parameters. By leveraging partial identity information in noninvasive samples, the models I developed will improve these parameter estimates and allow noninvasive methods to be used for more species, leading to more informed conservation decisions, and a more efficient allocation of conservation resources across species and populations.

Purpose  The purpose of this study is to extend current literature on the concept of value capture for AI start-ups, focusing on the challenges they face and how to maximize value capture. By investigating relational and economical value capture dimensions, this study aims to identify opportunities for start-ups to extract value from their AI digital solutions. The study further aims to contribute valuable insights to the literature, by building on the link between digital revenue models and value capture.  Method  To fulfill the stated purpose, this study has adopted a qualitative, abductive single case study approach with a focus on an AI start-up in the wind energy industry. The analysis was based on 20 semi-structured interviews which were conducted with different companies active in the wind energy industry. All data was analyzed through a 5-step thematic analysis process.  Findings  Two main challenges a start-up may face were identified which were “Difficulties getting access to partnering companies” and “Difficulties selling as a start-up”. Additionally, it was found that relational value capture can be maximized using pilot studies, which is possible by building trust and close relationships. Regarding economical value capture, this study showcases the importance of adapting the choice of revenue model to the customer where the perceived risk of the investment, in the customers’ point of view, plays a big role.  Theoretical contributions  Previous literature has mainly established a connection between the concept of value capture and revenue models. However, this study further bridges the two, and more in depth displays how revenue models could affect the captured value regarding AI start-ups. Additionally, this study further elaborates on the literature regarding relational value capture, showcasing how it can differ for a start-up and the challenges that arise when AI is involved.  Practical contributions  This study contributes with concrete examples of what challenges a start-up needs to consider when trying to capture value from their product. Additionally, the study contributes with a practical understanding on how a start-up can maximize value capture, by showcasing important factors to consider, both when it comes to relational and economical value capturing. Moreover, a decision tree has been formed, which can support AI start-ups when choosing a suitable revenue model.  Limitations and future research  Firstly, the study's findings may not be applicable to other industries, highlighting the need for multi-industry case studies for generalization and cross-industry comparisons. Secondly, more in-depth research is needed to explore the specific steps and strategies for building relationships, especially in the context of start-ups. Thirdly, this study primarily focuses on the revenue model aspect of value capture, overlooking the concept of value proposition which limits the depth of the findings and contributions and would be of interest to further investigate. / Syfte Syftet med studien är att berika litteraturen kring begreppet värde-fångande för AI start-ups, genom att fokusera på utmaningarna de står inför, samt hur de kan maximera värde-fångande. Genom att undersöka relationella och ekonomiska dimensioner av värde-fångande strävar denna studie efter att identifiera möjligheter för start-ups att utvinna värde från sina AI-baserade digitala lösningar. Studien syftar också till att bidra med värdefulla insikter till litteraturen genom att bygga på sambandet mellan digitala intäktsmodeller och värde-fångande.    Metod För att uppfylla det angivna syftet, har studien antagit en kvalitativ, abduktiv enskild fallstudieansats med fokus på en AI start-up inom vindkraftsbranschen. Analysen baserades på 20 semistrukturerade intervjuer som genomfördes med olika företag verksamma inom vindkraftsbranschen. All data analyserades genom en 5-stegs tematisk analysprocess.  Resultat Studien identifierade två huvudsakliga utmaningar som en start-up kan ställas inför, vilka var “Svårigheter att få tillgång till företag att samarbeta med” och “Svårigheter att sälja som en start-up”. Dessutom visade det sig att relationellt värde-fångande kan maximeras genom att använda pilotstudier, vilket möjliggörs genom att bygga tillit och nära relationer. När det gäller ekonomiskt värde-fångande visar denna studie vikten av att anpassa valet av intäktsmodell till kunden, där den upplevda risken för investeringen, sett ur kundens perspektiv, spelar stor roll.  Teoretiska bidrag Tidigare litteratur har främst etablerat en koppling mellan begreppet värde-fångande och intäktsmodeller. Denna studie går ett steg längre och visar mer ingående hur intäktsmodeller kan påverka det fångade värdet för AI start-ups. Dessutom utvecklar denna studie den befintliga litteraturen ytterligare kring relationellt värde-fångande och visar hur det kan skilja sig för en start-up och de utmaningar som uppstår då AI är inblandat. Praktiska bidrag Denna studie bidrar med konkreta exempel på vilka utmaningar en start-up behöver ta hänsyn till när de försöker fånga värde från sin produkt. Dessutom bidrar studien med en praktisk förståelse för hur en start-up kan maximera värde-fångande genom att visa på viktiga faktorer att beakta både när det gäller relationella och ekonomiska dimensioner. Utöver detta har ett beslutsträd utformats, med syfte att stödja start-ups vid val av lämplig intäktsmodell.  Begränsningar och framtida forskning För det första är denna studie begränsad till en specifik bransch, vilket understryker behovet av studier i fler branscher för en ökad generaliserbarhet och jämförelse. För det andra behövs mer ingående forskning för att utforska de specifika stegen och strategierna som krävs för att bygga relationer, särskilt när det gäller start-ups. För det tredje fokuserar denna studie primärt på intäktsmodell aspekten av värde-fångande och bortser från konceptet värdeerbjudande. Detta begränsar djupet i resultatet och skulle därför vara intressant för framtida forskare att undersöka.

Dans un monde de détection et prospection minière, une multitude de techniques existent. Une des techniques les plus puissantes est l’activation par neutrons (NAA et PGAA). Toutefois, dans le cadre de la prospection minière, l’utilisation de cette technique nécessite du forage. La motivation du projet GREYSTAR est de permettre l’analyse élémentaire par activation en limitant l’impact environnemental. Les facteurs limitant sont l’activation d’un volume distant et la détection de la radiation émise par ce volume. Ce mémoire examine l’activation par neutrons thermiques et la détection de gammas provenant de la désexcitation des noyaux activés. Une approche expérimentale est présentée avec des simulations pour venir appuyer les données expérimentales. Il en résulte que le projet GREYSTAR tel que décrit dans ce mémoire est prometteur et que davantage de recherche est à prescrire. Les résultats initiaux indiquent que selon le prototype proposé, les limites de détections sont de l’ordre de 2-3 m dans un matériel semblable au granite. On conclut que d’un point de vue de prospection minière, il est intéressant de poursuivre la recherche. De plus, plusieurs autres applications dans les domaines militaire, civil et policier sont prometteuses. / In the world of prospecting and detection, various techniques exist. One of the most powerful techniques is neutron activation analysis (both NAA and PGAA). For prospecting, however, this technique requires drilling. The motivation for the GREYSTAR project is to make elemental analysis via neutron analysis possible with little or no environmental impact. The limiting factors are the activation of remote volumes and detection of emitted radiation. This thesis looks at thermal neutron activation and delayed gamma decay from the activated nuclei. An experimental approach is proposed with simulations to back up the results. The resulting impression is that the GREYSTAR project as described is promising and further research is commended. Initial results indicate that depending on the prototypal setup, detection limits are of the order of 2-3 m in a material similar to granite. We conclude that from a prospecting point of view, it is worth continuing the research. Furthermore, other fields such as military, civil and law enforcement could benefit from an eventual prototype as well.

Les récentes recherches en robotique ont étendu l’utilisation des robots au-delà des environnements industriels traditionnels. Les robots humanoïdes sont bien adaptés pour effectuer des tâches accomplies par l’homme, en raison de leurs formes et leurs capacités de mouvements «humaines». En général, les robots humanoïdes ont un torse, une tête, deux bras et deux jambes. Ils ont été développés pour effectuer des tâches humaines telles que l’assistance personnelle, où ils devraient être en mesure d’aider les personnes âgées ou malades, ou effectuer des missions dangereuses, etc. C’est pourquoi les chercheurs ont besoin d’extraire des connaissances sur le mouvement humain grâce à l’observation continue du comportement humain. Ceci aidera les robots à être capables d’effectuer et d’accomplir des tâches en interagissant avec un humain. Le mouvement humain peut être analysé grâce à des systèmes de capture de mouvement, par exemple le système Vicon. L’objectif principal du projet de recherche est de développer des algorithmes afin qu’un robot soit capable d’interagir avec un partenaire humain en temps réel. Dans le présent travail, nous proposons et validons un algorithme probabiliste complet pour la prédiction du mouvement humain, nous montrerons que le modèle d’inférence peut anticiper vigoureusement les actions de l’être humain. Notre approche est basée sur un algorithme intégrant les paramètres des GMMs (Gaussian Mixture Models) dans un filtre de Kalman.