Current Search: Modeling uncertainty (x)
View All Items
- Title
- LANDFILL GAS TO ENERGY: INCENTIVES & BENEFITS.
- Creator
-
Amini, Hamid, Reinhart, Debra, University of Central Florida
- Abstract / Description
-
Municipal solid waste (MSW) management strategies typically include a combination of three approaches, recycling, combustion, and landfill disposal. In the US approximately 54% of the generated MSW was landfilled in 2008, mainly because of its simplicity and cost-effectiveness. However, landfills remain a major concern due to potential landfill gas (LFG) emissions, generated from the chemical and biological processes occurring in the disposed waste. The main components of LFG are methane (50...
Show moreMunicipal solid waste (MSW) management strategies typically include a combination of three approaches, recycling, combustion, and landfill disposal. In the US approximately 54% of the generated MSW was landfilled in 2008, mainly because of its simplicity and cost-effectiveness. However, landfills remain a major concern due to potential landfill gas (LFG) emissions, generated from the chemical and biological processes occurring in the disposed waste. The main components of LFG are methane (50-60%) and carbon dioxide (40-50%). Although LFG poses a threat to the environment, if managed properly it is a valuable energy resource due to the methane content. Currently there are over 550 active LFG to energy (LFGTE) facilities in the US, producing renewable energy from LFG. A major challenge in designing/operating a LFGTE facility is the uncertainty in LFG generation rate predictions. LFG generation rates are currently estimated using models that are dependent upon the waste disposal history, moisture content, cover type, and gas collection system, which are associated with significant uncertainties. The objectives of this research were to: (1) Evaluate various approaches of estimating LFG generation and to quantify the uncertainty of the model outcomes based on case-study analysis, (2) Present a methodology to predict long-term LFGTE potential under various operating practices on a regional scale, and (3) Investigate costs and benefits of emitting vs. collecting LFG emissions with regards to operation strategies and regulations. The first-order empirical model appeared to be insensitive to the approach taken in quantifying the model parameters, suggesting that the model may be inadequate to accurately describe LFG generation and collection. The uncertainty values for the model were, in general, at their lowest within five years after waste placement ended. Because of the exponential nature, the uncertainty increased as LFG generation declined to low values decades after the end of waste placement. A methodology was presented to estimate LFGTE potential on a regional scale over a 25-year timeframe with consideration of modeling uncertainties. The methodology was demonstrated for the US state of Florida, and showed that Florida could increase the annual LFGTE production by more than threefold by 2035 through installation of LFGTE facilities at all landfills. Results showed that diverting food waste could significantly reduce fugitive LFG emissions, while having minimal effect on the LFGTE potential. Estimates showed that with enhanced landfill operation and energy production practices, LFGTE power density could be comparable to technologies such as wind, tidal, and geothermal. More aggressive operations must be considered to avoid fugitive LFG emissions, which could significantly affect the economic viability of landfills. With little economic motivation for US landfill owners to voluntarily reduce fugitive emissions, regulations are necessary to increase the cost of emitting GHGs. In light of the recent economic recession, it is not likely that a carbon tax will be established; while a carbon trading program will enforce emission caps and provide a tool to offset some costs and improve emission-reduction systems. Immediate action establishing a US carbon trading market with carbon credit pricing and trading supervised by the federal government may be the solution. Costs of achieving high lifetime LFG collection efficiencies are unlikely to be covered with revenues from tipping fee, electricity sales, tax credits, or carbon credit trading. Under scenarios of highly regulated LFG emissions, sustainable landfilling will require research, development, and application of technologies to reduce the marginal abatement cost, including: (1) Diverting rapidly decomposable waste to alternative treatment methods, (2) Reducing fugitive emissions through usage daily/intermediate covers with high oxidation potential, (3) Increasing the lifetime LFG collection efficiency, and (4) Increasing LFG energy value - for instance by producing high-methane gas through biologically altering the LFG generation pathway.
Show less - Date Issued
- 2011
- Identifier
- CFE0003960, ucf:48682
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003960
- Title
- Structural Identification through Monitoring, Modeling and Predictive Analysis under Uncertainty.
- Creator
-
Gokce, Hasan, Catbas, Fikret, Chopra, Manoj, Mackie, Kevin, Yun, Hae-Bum, DeMara, Ronald, University of Central Florida
- Abstract / Description
-
Bridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior...
Show moreBridges are critical components of highway networks, which provide mobility and economical vitality to a nation. Ensuring the safety and regular operation as well as accurate structural assessment of bridges is essential. Structural Identification (St-Id) can be utilized for better assessment of structures by integrating experimental and analytical technologies in support of decision-making. St-Id is defined as creating parametric or nonparametric models to characterize structural behavior based on structural health monitoring (SHM) data. In a recent study by the ASCE St-Id Committee, St-Id framework is given in six steps, including modeling, experimentation and ultimately decision making for estimating the performance and vulnerability of structural systems reliably through the improved simulations using monitoring data. In some St-Id applications, there can be challenges and considerations related to this six-step framework. For instance not all of the steps can be employed; thereby a subset of the six steps can be adapted for some cases based on the various limitations. In addition, each step has its own characteristics, challenges, and uncertainties due to the considerations such as time varying nature of civil structures, modeling and measurements. It is often discussed that even a calibrated model has limitations in fully representing an existing structure; therefore, a family of models may be well suited to represent the structure's response and performance in a probabilistic manner.The principle objective of this dissertation is to investigate nonparametric and parametric St-Id approaches by considering uncertainties coming from different sources to better assess the structural condition for decision making. In the first part of the dissertation, a nonparametric St-Id approach is employed without the use of an analytical model. The new methodology, which is successfully demonstrated on both lab and real-life structures, can identify and locate the damage by tracking correlation coefficients between strain time histories and can locate the damage from the generated correlation matrices of different strain time histories. This methodology is found to be load independent, computationally efficient, easy to use, especially for handling large amounts of monitoring data, and capable of identifying the effectiveness of the maintenance. In the second part, a parametric St-Id approach is introduced by developing a family of models using Monte Carlo simulations and finite element analyses to explore the uncertainty effects on performance predictions in terms of load rating and structural reliability. The family of models is developed from a parent model, which is calibrated using monitoring data. In this dissertation, the calibration is carried out using artificial neural networks (ANNs) and the approach and results are demonstrated on a laboratory structure and a real-life movable bridge, where predictive analyses are carried out for performance decrease due to deterioration, damage, and traffic increase over time. In addition, a long-span bridge is investigated using the same approach when the bridge is retrofitted. The family of models for these structures is employed to determine the component and system reliability, as well as the load rating, with a distribution that incorporates various uncertainties that were defined and characterized. It is observed that the uncertainties play a considerable role even when compared to calibrated model-based predictions for reliability and load rating, especially when the structure is complex, deteriorated and aged, and subjected to variable environmental and operational conditions. It is recommended that a family-of-models approach is suitable for structures that have less redundancy, high operational importance, are deteriorated, and are performing under close capacity and demand levels.
Show less - Date Issued
- 2012
- Identifier
- CFE0004232, ucf:48997
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0004232
- Title
- Development of Regional Optimization and Market Penetration Models For the Electric Vehicles in the United States.
- Creator
-
Noori, Mehdi, Tatari, Omer, Oloufa, Amr, Nam, Boo Hyun, Xanthopoulos, Petros, University of Central Florida
- Abstract / Description
-
Since the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation...
Show moreSince the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation sector is to support alternative fuel technologies, including electric vehicles (EVs). As vehicles become electrified, the transportation fleet will rely on the electric grid as well as traditional transportation fuels for energy. The life cycle cost and environmental impacts of EVs are still very uncertain, but are nonetheless extremely important for making policy decisions. Moreover, the use of EVs will help to diversify the fuel mix and thereby reduce dependence on petroleum. In this respect, the United States has set a goal of a 20% share of EVs on U.S. roadways by 2030. However, there is also a considerable amount of uncertainty in the market share of EVs that must be taken into account. This dissertation aims to address these inherent uncertainties by presenting two new models: the Electric Vehicles Regional Optimizer (EVRO), and Electric Vehicle Regional Market Penetration (EVReMP). Using these two models, decision makers can predict the optimal combination of drivetrains and the market penetration of the EVs in different regions of the United States for the year 2030.First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different EV types (gasoline plug-in hybrid EVs, gasoline extended-range EVs, and all-electric EVs) are evaluated with their inherent uncertainties duly considered. Then, the environmental damage costs and water footprints of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties related to the life cycle costs, environmental damage costs, and water footprints of the studied vehicle types are modeled for different U.S. electricity grid regions. Next, an optimization model is used in conjunction with this Exploratory Modeling and Analysis method to find the ideal combination of different vehicle types in each U.S. region for the year 2030. Finally, an agent-based model is developed to identify the optimal market shares of the studied vehicles in each of 22 electric regions in the United States. The findings of this research will help policy makers and transportation planners to prepare our nation's transportation system for the future influx of EVs.The findings of this research indicate that the decision maker's point of view plays a vital role in selecting the optimal fleet array. While internal combustion engine vehicles have the lowest life cycle cost, the highest environmental damage cost, and a relatively low water footprint, they will not be a good choice in the future. On the other hand, although all-electric vehicles have a relatively low life cycle cost and the lowest environmental damage cost of the evaluated vehicle options, they also have the highest water footprint, so relying solely on all-electric vehicles is not an ideal choice either. Rather, the best fleet mix in 2030 will be an electrified fleet that relies on both electricity and gasoline. From the agent-based model results, a deviation is evident between the ideal fleet mix and that resulting from consumer behavior, in which EV shares increase dramatically by the year 2030 but only dominate 30 percent of the market. Therefore, government subsidies and the word-of-mouth effect will play a vital role in the future adoption of EVs.
Show less - Date Issued
- 2015
- Identifier
- CFE0005852, ucf:50927
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0005852