Browsing by Subject "Optimization model"

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An optimization-based biomechanical model of the thoracic spine
(Texas Tech University, 2009-05) Thaxton, Sherry S.; Smith, James L.; Woldstad, Jeffrey C.; Kobza, John E.; Patterson, Patrick E.
The major objective of this dissertation was to examine formulations of optimization-based models of the thoracic spine, comparing model predictions to EMG data collected during lifting tasks. The models employed here were developed based on traditional lumbar spine modeling techniques, but were expanded to include a representation of the rib cage and to predict forces at multiple vertebral levels. Optical motion tracking data were used in conjunction with known forces at the hands to calculate reaction moments at vertebrae T8 through T12. These moments were used to generate muscle force predictions using linear and nonlinear models, with and without rib cage representation included, and with and without limitations preventing muscle forces from varying too widely between adjacent vertebral levels. Tasks performed for data collection consisted of symmetric and asymmetric lifts of low and high force loads. Model predictions were compared to EMG data in order to examine model and test parameters. Though none of the model formulations provided good agreement between model predictions and EMG data, differences in model predictions allowed for comparisons in order to select parameters producing the best results. During this research, the simplest model formulations actually provided the best results. The linear objective function performed better, as did model formulations not including rib cage representation and model formulations not including limitations between vertebral levels. Testing parameters impacted model agreement with EMG as well, with models performing better for male subjects. Better model performance was also found for symmetric lifts and lifts of lower force loads.â€ƒ
Developing an optimization model for a cap and trade system to control methane emissions in the oil and natural gas industry : application to the Permian Basin
(2016-12) Correa Vivar, Luciano Livio; Fisher, W. L. (William Lawrence), 1932-; Dyer, James S; Scanlon, Bridget R
Development of unconventional oil and natural gas in the U.S., particularly the exploitation of shale gas, has been highly controversial with significant geopolitical implications. It is unquestionable that this so-called “golden era” of natural gas has brought not only significant new technologies and economic growth but has also raised important environmental concerns, including air pollution from methane emissions. Methane (CH₄) emissions from the oil and natural gas industry have been of critical and increasing concern for public policy. New evidence (Zeebe, et al. 2016) has confirmed record high levels of carbon dioxide (CO₂) in 66 million years, with CH₄ emissions considered a significant risk for global warming and climate change. For this reason, the U.S. Environmental Protection Agency (EPA) issued in 2016 a new “methane rule” to control emissions from the oil and gas industry by obligating the use of specific abatement measures to reduce pollution. This study analyzes the application of an optimization model to represent a market-based strategy of a cap and trade system as an alternative approach to regulating emissions. This option is more efficient than traditional command and control regulations at achieving the same levels of methane reduction in the oil and gas sector, and this hypothesis is verified by applying the optimization model to a sample of oil and gas production facilities operating in the Permian Basin. In spite of all the political-scientific efforts and discussions, we are still far from the knowledge needed to achieve a public policy strategy that balances sustainability with economic development, and I hope this research helps to reduce that gap.