Matching Items (4)
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- All Subjects: Carbon Sequestration
- All Subjects: decision making
- Genre: Academic theses
- Creators: Chester, Mikhail
Description
Life Cycle Assessment (LCA) quantifies environmental impacts of products in raw material extraction, processing, manufacturing, distribution, use and final disposal. The findings of an LCA can be used to improve industry practices, to aid in product development, and guide public policy. Unfortunately, existing approaches to LCA are unreliable in the cases of emerging technologies, where data is unavailable and rapid technological advances outstrip environmental knowledge. Previous studies have demonstrated several shortcomings to existing practices, including the masking of environmental impacts, the difficulty of selecting appropriate weight sets for multi-stakeholder problems, and difficulties in exploration of variability and uncertainty. In particular, there is an acute need for decision-driven interpretation methods that can guide decision makers towards making balanced, environmentally sound decisions in instances of high uncertainty. We propose the first major methodological innovation in LCA since early establishment of LCA as the analytical perspective of choice in problems of environmental management. We propose to couple stochastic multi-criteria decision analytic tools with existing approaches to inventory building and characterization to create a robust approach to comparative technology assessment in the context of high uncertainty, rapid technological change, and evolving stakeholder values. Namely, this study introduces a novel method known as Stochastic Multi-attribute Analysis for Life Cycle Impact Assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.
ContributorsPrado, Valentina (Author) / Seager, Thomas P (Thesis advisor) / Landis, Amy E. (Committee member) / Chester, Mikhail (Committee member) / White, Philip (Committee member) / Arizona State University (Publisher)
Created2013
Description
Quantum resilience is a pragmatic theory that allows systems engineers to formally characterize the resilience of systems. As a generalized theory, it not only clarifies resilience in the literature, but also can be applied to all disciplines and domains of discourse. Operationalizing resilience in this manner permits decision-makers to compare and contrast system deployment options for suitability in a variety of environments and allows for consistent treatment of resilience across domains. Systems engineers, whether planning future infrastructures or managing ecosystems, are increasingly asked to deliver resilient systems. Quantum resilience provides a way forward that allows specific resilience requirements to be specified, validated, and verified.
Quantum resilience makes two very important claims. First, resilience cannot be characterized without recognizing both the system and the valued function it provides. Second, resilience is not about disturbances, insults, threats, or perturbations. To avoid crippling infinities, characterization of resilience must be accomplishable without disturbances in mind. In light of this, quantum resilience defines resilience as the extent to which a system delivers its valued functions, and characterizes resilience as a function of system productivity and complexity. System productivity vis-à-vis specified “valued functions” involves (1) the quanta of the valued function delivered, and (2) the number of systems (within the greater system) which deliver it. System complexity is defined structurally and relationally and is a function of a variety of items including (1) system-of-systems hierarchical decomposition, (2) interfaces and connections between systems, and (3) inter-system dependencies.
Among the important features of quantum resilience is that it can be implemented in any system engineering tool that provides sufficient design and specification rigor (i.e., one that supports standards like the Lifecycle and Systems Modeling languages and frameworks like the DoD Architecture Framework). Further, this can be accomplished with minimal software development and has been demonstrated in three model-based system engineering tools, two of which are commercially available, well-respected, and widely used. This pragmatic approach assures transparency and consistency in characterization of resilience in any discipline.
Quantum resilience makes two very important claims. First, resilience cannot be characterized without recognizing both the system and the valued function it provides. Second, resilience is not about disturbances, insults, threats, or perturbations. To avoid crippling infinities, characterization of resilience must be accomplishable without disturbances in mind. In light of this, quantum resilience defines resilience as the extent to which a system delivers its valued functions, and characterizes resilience as a function of system productivity and complexity. System productivity vis-à-vis specified “valued functions” involves (1) the quanta of the valued function delivered, and (2) the number of systems (within the greater system) which deliver it. System complexity is defined structurally and relationally and is a function of a variety of items including (1) system-of-systems hierarchical decomposition, (2) interfaces and connections between systems, and (3) inter-system dependencies.
Among the important features of quantum resilience is that it can be implemented in any system engineering tool that provides sufficient design and specification rigor (i.e., one that supports standards like the Lifecycle and Systems Modeling languages and frameworks like the DoD Architecture Framework). Further, this can be accomplished with minimal software development and has been demonstrated in three model-based system engineering tools, two of which are commercially available, well-respected, and widely used. This pragmatic approach assures transparency and consistency in characterization of resilience in any discipline.
ContributorsRoberts, Thomas Wade (Author) / Allenby, Braden (Thesis advisor) / Chester, Mikhail (Committee member) / Anderies, John M (Committee member) / Arizona State University (Publisher)
Created2015
Description
Increasing concentrations of carbon dioxide in the atmosphere will inevitably lead to long-term changes in climate that can have serious consequences. Controlling anthropogenic emission of carbon dioxide into the atmosphere, however, represents a significant technological challenge. Various chemical approaches have been suggested, perhaps the most promising of these is based on electrochemical trapping of carbon dioxide using pyridine and derivatives. Optimization of this process requires a detailed understanding of the mechanisms of the reactions of reduced pyridines with carbon dioxide, which are not currently well known. This thesis describes a detailed mechanistic study of the nucleophilic and Bronsted basic properties of the radical anion of bipyridine as a model pyridine derivative, formed by one-electron reduction, with particular emphasis on the reactions with carbon dioxide. A time-resolved spectroscopic method was used to characterize the key intermediates and determine the kinetics of the reactions of the radical anion and its protonated radical form. Using a pulsed nanosecond laser, the bipyridine radical anion could be generated in-situ in less than 100 ns, which allows fast reactions to be monitored in real time. The bipyridine radical anion was found to be a very powerful one-electron donor, Bronsted base and nucleophile. It reacts by addition to the C=O bonds of ketones with a bimolecular rate constant around 1* 107 M-1 s-1. These are among the fastest nucleophilic additions that have been reported in literature. Temperature dependence studies demonstrate very low activation energies and large Arrhenius pre-exponential parameters, consistent with very high reactivity. The kinetics of E2 elimination, where the radical anion acts as a base, and SN2 substitution, where the radical anion acts as a nucleophile, are also characterized by large bimolecular rate constants in the range ca. 106 - 107 M-1 s-1. The pKa of the bipyridine radical anion was measured using a kinetic method and analysis of the data using a Marcus theory model for proton transfer. The bipyridine radical anion is found to have a pKa of 40±5 in DMSO. The reorganization energy for the proton transfer reaction was found to be 70±5 kJ/mol. The bipyridine radical anion was found to react very rapidly with carbon dioxide, with a bimolecular rate constant of 1* 108 M-1 s-1 and a small activation energy, whereas the protonated radical reacted with carbon dioxide with a rate constant that was too small to measure. The kinetic and thermodynamic data obtained in this work can be used to understand the mechanisms of the reactions of pyridines with carbon dioxide under reducing conditions.
ContributorsRanjan, Rajeev (Author) / Gould, Ian R (Thesis advisor) / Buttry, Daniel A (Thesis advisor) / Yarger, Jeff (Committee member) / Seo, Dong-Kyun (Committee member) / Arizona State University (Publisher)
Created2015
Description
Carbon capture and sequestration (CCS) is one of the important mitigation options for climate change. Numerous technologies to capture carbon dioxide (CO2) are in development but currently, capture using amines is the predominant technology. When the flue gas reacts with amines (Monoethanaloamine) the CO2 is absorbed into the solution and forms an intermediate product which then releases CO2 at higher temperature. The high temperature necessary to strip CO2 is provided by steam extracted from the powerplant thus reducing the net output of the powerplant by 25% to 35%. The reduction in electricity output for the same input of coal increases the emissions factor of Nitrogen Oxides, Mercury, Particulate matter, Ammonia, Volatile organic compounds for the same unit of electricity produced. The thesis questions if this tradeoff between CO2 and other emissions is beneficial or not. Three different methodologies, Life Cycle Assessment, Valuation models and cost benefit analysis are used to identify if there is a net benefit to the society on implementation of CCS to a Pulverized coal powerplant. These methodologies include the benefits due to reduction of CO2 and the disbenefits due to the increase of other emissions. The life cycle assessment using ecoindicator'99 methodology shows the CCS is not beneficial under Hierarchical and Egalitarian perspective. The valuation model shows that the inclusion of the other emissions reduces the benefit associated with CCS. For a lower CO2 price the valuation model shows that CCS is detrimental to the environment. The cost benefit analysis shows that a CO2 price of at least $80/tCO2 is required for the cost benefit ratio to be 1. The methodology integrates Montecarlo simulation to characterize the uncertainties associated with the valuation models.
ContributorsSekar, Ashok (Author) / Williams, Eric (Thesis advisor) / Chester, Mikhail (Thesis advisor) / Allenby, Braden (Committee member) / Arizona State University (Publisher)
Created2012