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Browsing by Author "Bradley, Tom, committee member"

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    Applying model-based systems engineering to architecture optimization and selection during system acquisition
    (Colorado State University. Libraries, 2018) LaSorda, Michael, author; Sega, Ronald M., advisor; Borky, Mike, advisor; Bradley, Tom, committee member; Quinn, Jason, committee member
    The architecture selection process early in a major system acquisition is a critical step in determining the overall affordability and technical performance success of a program. There are recognized deficiencies that frequently occur in this step such as poor transparency into the final selection decision and excessive focus on lowest cost, which is not necessarily the best value for all of the stakeholders. This research investigates improvements to the architecture selection process by integrating Model-Based Systems Engineering (MBSE) techniques, enforcing rigorous, quantitative evaluation metrics with a corresponding understanding of uncertainties, and stakeholder feedback in order to generate an architecture that is more optimized and trusted to provide better value for the stakeholders. Three case studies were analyzed to demonstrate this proposed process. The first focused on a satellite communications System of Systems (SoS) acquisition to demonstrate the overall feasibility and applicability of the process. The second investigated an electro-optical remote sensing satellite system to compare this proposed process to a current architecture selection process typified by the United States Department of Defense (U.S. DoD) Analysis of Alternatives (AoA). The third case study analyzed the evaluation of a service-oriented architecture (SOA) providing satellite command and control with cyber security protections in order to demonstrate rigorous accounting of uncertainty through the architecture evaluation and selection. These case studies serve to define and demonstrate a new, more transparent and trusted architecture selection process that consistently provides better value for the stakeholders of a major system acquisition. While the examples in this research focused on U.S. DoD and other major acquisitions, the methodology developed is broadly applicable to other domains where this is a need for optimization of enterprise architectures as the basis for effective system acquisition. The results from the three case studies showed the new process outperformed the current methodology for conducting architecture evaluations in nearly all criteria considered and in particular selects architectures of better value, provides greater visibility into the actual decision making, and improves trust in the decision through a robust understanding of uncertainty. The primary contribution of this research then is improved information support to an architecture selection in the early phases of a system acquisition program. The proposed methodology presents a decision authority with an integrated assessment of each alternative, traceable to the concerns of the system's stakeholders, and thus enables a more informed and objective selection of the preferred alternative. It is recommended that the methodology proposed in this work is considered for future architecture evaluations.
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    Development of an electrocoagulation based treatment train for produced water with high concentrations of organic matter
    (Colorado State University. Libraries, 2016) Caschette, Richard Andrew, author; Carlson, Kenneth, advisor; Sharvelle, Sybil, committee member; Bradley, Tom, committee member
    Well stimulation in the form of hydraulic fracturing has made unconventional oil and gas extraction economically feasible, significantly increasing the number of producing oil and gas wells in the United States in the last several decades. Both the hydraulic fracturing process and shale play development has created a large amount of oil and gas associated wastewater. Deep well injection or disposal wells are the preferred and most widely used method for managing produced water. This industry standard both eliminates valuable water resources from the hydrologic cycle and can be linked to the increasing frequency of seismic events in parts of the United States. This paper investigates water treatment processes in the context of beneficial reuse towards irrigation. Treating produced water on well pad locations followed by agricultural use within close proximity minimizes trucking costs and environmental impacts as well as recycles industrial wastewater back into the hydrologic cycle. High concentrations of salts and organic matter must be removed in addition to other contaminants (Benzene, Boron, Calcium, and Magnesium) from produced water collected from Noble Energy's Wells Ranch Central Processing Facility (CPF) before being applied towards a secondary use. Electrocoagulation coupled with a strong oxidant creates a more effective coagulation process prior to ultrafiltration, granular activated carbon and reverse osmosis processes. Organic matter removal and its potential for significant fouling of reverse osmosis membranes remains a major challenge as concentrations of total organic carbon in Noble Energy CPF produced water are typically around 1,500 mg/L after ultrafiltration. Four treated produced water effluent qualities generated in the CSU Environmental Engineering lab, in addition to freshwater were used to irrigate two non-food crops. Switchgrass and canola were arranged at the CSU greenhouse and watered using a drip irrigation system. The fate of regulated volatile organics and impact of salt accumulation are the primary parameters of interest for impaired water usage. This study is constructed to provide a baseline for the development of a larger scale pilot designed to treat produced water from an operator's storage tanks and used to irrigate nearby agricultural land. The concentration of dissolved organic carbon can be linked directly to the economic feasibility and operational challenges of treatment, both in the context of pretreatment and required maintenance for reverse osmosis. Although produced water from gel-based hydraulic fracturing in the Denver-Juleseburg can be very difficult to treat, beneficial reuse should be an important consideration for future shale play development in Colorado and other parts of the United States.
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    Raw material optimization and CO₂ sensitivity-predictive analytics in cement manufacturing: a case study at Union Bridge Plant, Heidelberg Materials, Maryland
    (Colorado State University. Libraries, 2024) Boakye, Kwaku, author; Simske, Steve, advisor; Bradley, Tom, committee member; Troxell, Wade, committee member; Goemans, Chris, committee member
    Cement has been in use by humans throughout history, and its manufacturing process has undergone many changes. The high increase in economic growth around the world and the demand for rapid infrastructure development due to population growth are the underlying reasons for the globally high cement demand. Cement is produced by grinding clinker together with a mixture of ground gypsum. The clinker is produced using a rotary kiln which burns a mixture of limestone, clay, magnesium, silica, and iron with desired atomic percentages through the calcination process. The quarry serves as the main source of raw material for the rotary kiln in cement production. Over the years cement manufacturing has hurt environmental, social, and political aspects of society. This negative impact includes the overuse of raw material which is obtained by mining resulting in disturbed landmass, overproduction of rock waste material, and the emission of CO2 resulting from the calcination of limestone in the pyro process. The study looks at three cement manufacturing systems and uses different methodologies to achieve results that can be implemented in the cement industry. These three systems were (1) the quarry (2) the preheat tower and (3) the kiln. Ensuring the consistency of material feed chemistry, with the quarry playing a pivotal role, is essential for optimizing the performance of a rotary kiln. The optimization of the raw material also allows limited use of raw materials for cement manufacturing, cutting down waste. The study employed a six-step methodology, incorporating a modified 3D mining software modeling tool, a database computer loop prediction tool, and other resources to enhance mining sequencing, optimize raw material utilization, and ensure a consistent chemistry mix for the kiln. By using overburden as a raw material in the mix, the quarry nearly universally reduced the environmental impact of squandering unwanted material in the quarry. This has a significant environmental impact since it requires less space to manage the overburdened waste generated during mining. In addition, raw material usage was optimized for clinker production, causing a reduction of 4% in sand usage as raw material, a reduction in raw material purchase cost, a reduction of the variability of kiln feed chemistry, and the production of high-quality clinker. The standard deviation of kiln feed LSF experienced a 45 percent improvement, leading to a 65 percent reduction in the variability of kiln feed. The study also uses machine learning methods to model different stages of the calcination process in cement and to improve knowledge of the generation of CO2 during cement manufacturing. Calcination plays a crucial role in assessing clinker quality, energy requirements, and CO2 emissions within a cement-producing facility. However, due to the complexity of the calcination process, accurately predicting CO2 emissions has historically been challenging. The objective of this study is to establish a direct relationship between CO2 generation during the raw material manufacturing process and various process factors. In this paper, six machine-learning techniques are explored to analyze two output variables: (1) the apparent degree of oxidation, and (2) the apparent degree of calcination. Sensitivity analysis of CO2 molecular composition (on a dry basis) utilizes over 6000 historical manufacturing health data points as input variables, and the findings are utilized to train the algorithms. The Root Mean Squared Error (RMSE) of various regression models was examined, and the models were then run to ascertain which independent variables in cement manufacturing had the largest impact on the dependent variables. To establish which independent variable had the biggest impact on CO2 emissions, the significance of the other factors was also assessed.