You searched for +publisher:"University of Texas – Austin" +contributor:("Edgar, Thomas F").
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University of Texas – Austin
1.
Kim, Jong Suk.
Modeling, control, and optimization of combined heat and power plants.
Degree: PhD, Chemical Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/24830 
► Combined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal…
(more)
▼ Combined heat and power (CHP) is a technology that decreases total fuel consumption and related greenhouse gas emissions by producing both electricity and useful thermal energy from a single energy source. In the industrial and commercial sectors, a typical CHP site relies upon the electricity distribution network for significant periods, i.e., for purchasing power from the grid during periods of high demand or when off-peak electricity tariffs are available. On the other hand, in some cases, a CHP plant is allowed to sell surplus power to the grid during on-peak hours when electricity prices are highest while all operating constraints and local demands are satisfied. Therefore, if the plant is connected with the external grid and allowed to participate in open energy markets in the future, it could yield significant economic benefits by selling/buying power depending on market conditions. This is achieved by solving the power system generation scheduling problem using mathematical programming. In this work, we present the application of mixed-integer nonlinear programming (MINLP) approach for scheduling of a CHP plant in the day-ahead wholesale energy markets. This work employs first principles models to describe the nonlinear dynamics of a CHP plant and its individual components (gas and steam turbines, heat recovery steam generators, and auxiliary boilers). The MINLP framework includes practical constraints such as minimum/maximum power output and steam flow restrictions, minimum up/down times, start-up and shut-down procedures, and fuel limits. We provide case studies involving the Hal C. Weaver power plant complex at the
University of
Texas at
Austin to demonstrate this methodology. The results show that the optimized operating strategies can yield substantial net incomes from electricity sales and purchases. This work also highlights the application of a nonlinear model predictive control scheme to a heavy-duty gas turbine power plant for frequency and temperature control. This scheme is compared to a classical PID/logic based control scheme and is found to provide superior output responses with smaller settling times and less oscillatory behavior in response to disturbances in electric loads.
Advisors/Committee Members: Edgar, Thomas F. (advisor).
Subjects/Keywords: Scheduling; Unit commitment; Economic dispatch; Combined heat and power; Day-ahead wholesale energy market; Mixed-integer nonlinear programming; Emergency response service; Model predictive control
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APA (6th Edition):
Kim, J. S. (2014). Modeling, control, and optimization of combined heat and power plants. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/24830
Chicago Manual of Style (16th Edition):
Kim, Jong Suk. “Modeling, control, and optimization of combined heat and power plants.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/24830.
MLA Handbook (7th Edition):
Kim, Jong Suk. “Modeling, control, and optimization of combined heat and power plants.” 2014. Web. 24 Jan 2021.
Vancouver:
Kim JS. Modeling, control, and optimization of combined heat and power plants. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/24830.
Council of Science Editors:
Kim JS. Modeling, control, and optimization of combined heat and power plants. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/24830
University of Texas – Austin
2.
Cole, Wesley Joseph.
Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment.
Degree: PhD, Chemical Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/24908
► This work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees…
(more)
▼ This work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees of freedom by incorporating some form of energy storage (in this work, thermal energy storage), and the increasing penetration of smart grid technologies provides a wealth of data for both modeling and management. Data used for the system models here come primarily from the Pecan Street Smart Grid Demonstration Project in
Austin,
Texas, USA. Other data are from the
Austin Energy Mueller Energy Center and the
University of
Texas Hal C. Weaver combined heat and power plant. Systems considered in this work include thermal energy storage, chiller plants, combined heat and power plants, turbine inlet cooling, residential air conditioning, and solar photovoltaics. These systems are modeled and controlled in integrated environments in order to provide system benefits. In a district cooling system with thermal energy storage, combined heat and power, and turbine inlet cooling, model-based optimization strategies are able to reduce peak demand and decrease cooling electricity costs by 79%. Smart grid data are employed to consider a system of 900 residential homes in
Austin. In order to make the system model tractable for a model predictive controller, a reduced-order home modeling strategy is developed that maps thermostat set points to air conditioner electricity consumption. When the model predictive controller is developed for the system, the system is able to reduce total peak demand by 9%. Further work with the model of 900 residential homes presents a modified dual formulation for determining the optimal prices that produce a desired result in the residential homes. By using the modified dual formulation, it is found that the optimal pricing strategy for peak demand reduction is a critical peak pricing rate structure, and that those prices can be used in place of centralized control strategies to achieve peak reduction goals.
Advisors/Committee Members: Edgar, Thomas F. (advisor).
Subjects/Keywords: Thermal energy storage; Smart grid; Model predictive control; Residential air conditioning; Peak demand
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APA (6th Edition):
Cole, W. J. (2014). Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/24908
Chicago Manual of Style (16th Edition):
Cole, Wesley Joseph. “Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/24908.
MLA Handbook (7th Edition):
Cole, Wesley Joseph. “Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment.” 2014. Web. 24 Jan 2021.
Vancouver:
Cole WJ. Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/24908.
Council of Science Editors:
Cole WJ. Dynamic modeling, optimization, and control of integrated energy systems in a smart grid environment. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/24908
University of Texas – Austin
3.
Kitaka, Richard Herbertson.
Underground coal gasification : overview of an economic and environmental evaluation.
Degree: MSin Engineering, Chemical Engineering, 2011, University of Texas – Austin
URL: http://hdl.handle.net/2152/ETD-UT-2011-12-4535
► This paper examines an overview of the economic and environmental aspects of Underground Coal Gasification (UCG) as a viable option to the above ground Surface…
(more)
▼ This paper examines an overview of the economic and environmental aspects of Underground Coal Gasification (UCG) as a viable option to the above ground Surface Coal Gasification (SCG). In addition, some highlights, hurdles and opportunities from early investment to successful commercial application of some worldwide UCG projects will be discussed. Global energy demands have prompted continual crude oil consumption at an astronomical pace. As such, the most advanced economies are looking for local and bountiful resources to challenge crude oil's dependence for which coal provides the best alternative so far. In the U.S, the Department of Energy (DOE), the National Energy Transportation Laboratory (NETL) along with the Lawrence Livermore National Laboratory (LLNL) continue to support pilot programs that develop improved methods for clean coal technologies to produce coal derived fuels competitive with crude oil fuels at about $30 per barrel. Lignite, the softest of the four types of coal, is the best candidate for underground coal gasification due to its abundance, high volatility and water to carbon content in its rock formation. The biggest challenge of modern humans is to find a balance of energy consumption, availability of resources, production costs and environmental conservation. Additionally, UCG has environmental benefits that include mitigating CO₂ emissions through Carbon Capture and Storage (CCS) and reduced overall surface pollutants, making it the preferred choice over SCG.
Advisors/Committee Members: Edgar, Thomas F. (advisor), Sanchez, Isaac C. (committee member).
Subjects/Keywords: Economic and environmental evaluation; Underground coal gasification; Surface coal gasification; UCG; SCG; UCG economic evaluation
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APA (6th Edition):
Kitaka, R. H. (2011). Underground coal gasification : overview of an economic and environmental evaluation. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-12-4535
Chicago Manual of Style (16th Edition):
Kitaka, Richard Herbertson. “Underground coal gasification : overview of an economic and environmental evaluation.” 2011. Masters Thesis, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/ETD-UT-2011-12-4535.
MLA Handbook (7th Edition):
Kitaka, Richard Herbertson. “Underground coal gasification : overview of an economic and environmental evaluation.” 2011. Web. 24 Jan 2021.
Vancouver:
Kitaka RH. Underground coal gasification : overview of an economic and environmental evaluation. [Internet] [Masters thesis]. University of Texas – Austin; 2011. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4535.
Council of Science Editors:
Kitaka RH. Underground coal gasification : overview of an economic and environmental evaluation. [Masters Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-12-4535
University of Texas – Austin
4.
-4966-8063.
Energy focused modeling and optimization of a radiant tube roller hearth austenization furnace.
Degree: MSin Engineering, Chemical Engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/31750
► In this thesis, we develop a two-dimensional energy-focused model of a roller hearth heat treating furnace. The two-dimensional model is based on first-principles, detailed representations…
(more)
▼ In this thesis, we develop a two-dimensional energy-focused model of a roller hearth heat treating furnace. The two-dimensional model is based on first-principles, detailed representations of radiation with non-participating gas and convective heat transfer. The model computes the exit temperature profile of the treated steel parts while calculating the energy consumption and efficiency of the furnace. We propose a dual iterative numerical scheme to solve the model, and validate its efficacy by simulating the dynamics of the furnace during startup and cool-down, as well as for steady-state operation. We first present two case studies to show the capability of the model in simulating the furnace system with constant fuel input to the burners. We then implement feedback control on the model to maintain furnace temperatures by manipulating the fuel feed rate to the furnace burners. A case study using suggested temperature set points from the plant details energy consumption within the furnace under control. We then use the model to find the optimal set points to minimize energy consumption while ensuring certain part temperature properties are met when part processing is complete. With optimized set points, 8.5% less energy per part is required versus the heuristic set points.
Advisors/Committee Members: Baldea, Michael (advisor), Edgar, Thomas F. (advisor).
Subjects/Keywords: Heat treating furnace; Furnace energy
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APA (6th Edition):
-4966-8063. (2015). Energy focused modeling and optimization of a radiant tube roller hearth austenization furnace. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31750
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-4966-8063. “Energy focused modeling and optimization of a radiant tube roller hearth austenization furnace.” 2015. Masters Thesis, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/31750.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-4966-8063. “Energy focused modeling and optimization of a radiant tube roller hearth austenization furnace.” 2015. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-4966-8063. Energy focused modeling and optimization of a radiant tube roller hearth austenization furnace. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/31750.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-4966-8063. Energy focused modeling and optimization of a radiant tube roller hearth austenization furnace. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31750
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
5.
James, Corey Matthew, 1976-.
Reducing the cost of operational water on military bases through modeling, optimization, and control.
Degree: PhD, Chemical Engineering, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/62970
► Military municipal water systems provide safe and clean water to the surrounding community while also supporting the intense and often unpredictable training schedules of the…
(more)
▼ Military municipal water systems provide safe and clean water to the surrounding community while also supporting the intense and often unpredictable training schedules of the tenant units. Much like their civilian counterparts, military water systems are also consumers of great amounts of energy and capital. As a part of the Army Net Zero program in 2011, an annual water inventory conducted on eight U.S. Army installations concluded that consumption was 5.5 billion gallons. Using the Environmental Protection Agency’s average national estimate of 1,500 kWh of energy consumed for every 1,000 gallons of treated water, it is readily apparent that the department of defense is a heavy consumer of both water and energy. Because the scale of the military’s usage is so vast, so too is their waste. Waste in water systems is common and commonly neglected, as many were initially constructed decades ago and the commodity that they transport is relatively inexpensive. However, recent droughts affecting regions of the United States highlighted the need to conserve and avoid waste, regardless of the commodity price. The efficiency of water systems is highly dependent upon developing accurate models and using those models to accurately deal with disturbances such as demand and chlorine concentration. This work extends water distribution system modeling, optimization, and control to a military setting where constraints are tighter for resiliency purposes, demands are often unpredictable, and saving money and water improves defense capabilities. First, a discretized nonlinear, equation based model of a known system at an existing U.S. Army installation that accurately predicts system behavior under typical demand considerations. The model is calibrated for accuracy using actual system data from a military installation and employed in a nonlinear optimization program to study reduction of costs, minimizing waste, and improvements in energy efficiency. Demand profiles were constructed from residential data and scaled to better represent demand on military bases. With very little adjustment, this model can be used to optimize similar systems in the military inventory. Water and energy savings exceed 10% in the optimized system, which predicts the Army could save greater than $1.5 million per year in the continental United States if rigorous optimization was conducted on storage and pumping at every base. It is shown that a reduced order empirical model is a viable alternative to the computationally expensive equation based approach. The empirical model is used to implement model predictive control, providing the system protection against large and unpredictable disturbances. This method adds an additional manipulated variable, chlorine injection, to ensure efficient constraint compliance. Experimental results show this method further supports the aforementioned savings in the optimized system alone, while efficiently handling disturbances. This research closes previous gaps in research, particularly on military installations. First,…
Advisors/Committee Members: Edgar, Thomas F. (advisor), Webber, Michael E., 1971- (advisor), Rochelle, Gary T (committee member), Baldea, Michael (committee member), Werth, Charles J (committee member).
Subjects/Keywords: Control; Optimization; Water; Energy; Military
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APA ·
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APA (6th Edition):
James, Corey Matthew, 1. (2017). Reducing the cost of operational water on military bases through modeling, optimization, and control. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62970
Chicago Manual of Style (16th Edition):
James, Corey Matthew, 1976-. “Reducing the cost of operational water on military bases through modeling, optimization, and control.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/62970.
MLA Handbook (7th Edition):
James, Corey Matthew, 1976-. “Reducing the cost of operational water on military bases through modeling, optimization, and control.” 2017. Web. 24 Jan 2021.
Vancouver:
James, Corey Matthew 1. Reducing the cost of operational water on military bases through modeling, optimization, and control. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/62970.
Council of Science Editors:
James, Corey Matthew 1. Reducing the cost of operational water on military bases through modeling, optimization, and control. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62970
University of Texas – Austin
6.
Perez, Krystian Xavier.
Analysis, modeling and optimization of residential energy use from smart meter data.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/46454
► Approximately 38% of electricity consumption within the United States can be attributed to residential buildings, a vast share of which is in heating, ventilation and…
(more)
▼ Approximately 38% of electricity consumption within the United States can be attributed to residential buildings, a vast share of which is in heating, ventilation and cooling. The load placed on the grid by residential consumers is highly variable and strongly influenced by weather and human activity patterns. Meeting fluctuations in demand is challenging and expensive for electricity producers and grid operators. Reducing variability in residential energy use can contribute significantly to increasing the uniformity of energy demand on the grid and diminish reliance on inefficient, polluting “peaking” plants that are used to meet extremely high demands. Achieving this goal requires tight coordination between energy consumption and generation, as well as the means to store energy generated in periods of low demand for use during the time intervals when consumer demand peaks. There is a common perception that a single home has a minor impact on the entire grid. However, owing to the fact that consumption patterns of homes are similar, while a single home does not have a large impact on the grid, entire neighborhoods do. Motivated by the above, this work explores the interaction between residential energy consumption and the electric grid. An analysis, modeling and optimization framework on smart meter data is developed to anticipate and modulate energy usage of ensembles of residential homes in order to reduce peak power demand. Much of the data used in this work come from Pecan Street, Inc., a smart grid demonstration project in
Austin, TX. First, a nonintrusive load monitoring algorithm is developed to isolate air-conditioning (A/C) energy use from whole-house energy consumption data. Subsequently, a simplified reduced-order model is derived from smart meter data and thermostat set-point data to predict A/C energy use. The models of an ensemble of homes are placed within a centralized model predictive control scheme to minimize peak community A/C energy use. Reductions in peak energy use are achieved by shifting the thermostat set-points of individual homes. The approach is further expanded by simultaneously scheduling the operation of time-shiftable appliances to further reduce the community peak load. This integrated operation reduces peak loads by an average of 25.5%. This work also considers the impact of control and optimization techniques on designing a micro-grid that operates near autonomously from the electric power grid. Lastly, this work presents a tool to compare energy demand patterns of houses from smart meter data and indicates that high-energy houses would benefit from energy audits to improve energy efficiency.
Advisors/Committee Members: Edgar, Thomas F. (advisor), Baldea, Michael (advisor), Novoselac, Atila (committee member), Webber, Michael E (committee member), Rochelle, Gary T (committee member).
Subjects/Keywords: Residential energy; Home energy management
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APA (6th Edition):
Perez, K. X. (2016). Analysis, modeling and optimization of residential energy use from smart meter data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46454
Chicago Manual of Style (16th Edition):
Perez, Krystian Xavier. “Analysis, modeling and optimization of residential energy use from smart meter data.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/46454.
MLA Handbook (7th Edition):
Perez, Krystian Xavier. “Analysis, modeling and optimization of residential energy use from smart meter data.” 2016. Web. 24 Jan 2021.
Vancouver:
Perez KX. Analysis, modeling and optimization of residential energy use from smart meter data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/46454.
Council of Science Editors:
Perez KX. Analysis, modeling and optimization of residential energy use from smart meter data. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46454
University of Texas – Austin
7.
-0020-5212.
Dynamic modeling of post-combustion amine scrubbing for process control strategy development.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/39612
► Intensified process designs with advanced solvents have been proposed to decrease both capital and operating costs of post-combustion carbon capture with amine scrubbing. These advanced…
(more)
▼ Intensified process designs with advanced solvents have been proposed to decrease both capital and operating costs of post-combustion carbon capture with amine scrubbing. These advanced flowsheets create process control challenges because process variables are designed to operate near constraints and the degrees of freedom are increased due to heat recovery. Additionally, amine scrubbing is tightly integrated with the upstream power plant and downstream enhanced oil recovery (EOR) facility. This work simulated an amine scrubbing plant that uses an intercooled absorber and advanced flash stripper configuration with aqueous piperazine to capture CO2 from a 550 MWe coal-fired power plant. The objective of this research was to develop a process control strategy that resulted in favorable closed-loop dynamics and near-optimal conditions in response to disturbances and off-design operation. Two models were created for dynamic simulation of the amine scrubbing system: a medium-order model of an intercooled absorber column and a low-order model of the entire plant. The purpose of the medium-order model was to accurately predict the absorber temperature profile in order to identify a column temperature that can be controlled by manipulating the solvent circulation rate to maintain a constant liquid to gas ratio. The low-order model, which was shown to sufficiently represent dynamic process behavior through validation with pilot plant data, was used to develop a plantwide control strategy. A regulatory control layer was implemented and tested with bounding cases that represent either electricity generation requirements, CO2 emission regulations, or EOR constraints dominating the control strategy. Satisfying the operational and economic objectives of one system component was found to result in unfavorable dynamic performance for the remainder of the system. Self-optimizing control variables were identified for the energy recovery flowrates of the advanced flash stripper that maintained good energy performance in off-design conditions. Regulatory control alone could not satisfactorily achieve the set point for CO2 removal rate from the flue gas. A supervisory model predictive controller was developed that manipulates the set point for the stripper pressure controller in order to control removal. The straightforward single-input, single-output constrained linear model predictive controller exhibited a significant improvement compared to PI control alone.
Advisors/Committee Members: Rochelle, Gary T. (advisor), Edgar, Thomas F. (advisor), Baldea, Michael (committee member), Akella, Maruthi R (committee member), Chen, Eric (committee member).
Subjects/Keywords: Amine scrubbing; Dynamic modeling; Process control; Carbon capture
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APA (6th Edition):
-0020-5212. (2016). Dynamic modeling of post-combustion amine scrubbing for process control strategy development. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39612
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-0020-5212. “Dynamic modeling of post-combustion amine scrubbing for process control strategy development.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/39612.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-0020-5212. “Dynamic modeling of post-combustion amine scrubbing for process control strategy development.” 2016. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-0020-5212. Dynamic modeling of post-combustion amine scrubbing for process control strategy development. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/39612.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-0020-5212. Dynamic modeling of post-combustion amine scrubbing for process control strategy development. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/39612
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
8.
Abdulla, Thaer Adnan.
An experimental investigation of batch distillation column control.
Degree: PhD, Chemical Engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/5871
► The development of an inferential soft sensor for a pilot-plant distillation column separating an ethanol-water mixture using neural network (NN) models has been investigated in…
(more)
▼ The development of an inferential soft sensor for a pilot-plant distillation column separating an ethanol-water mixture using neural network (NN) models has been investigated in this work. Inferential sensors are increasingly used in the process industries to infer the value of the main quality variable while utilizing much easier to measure secondary variables of the process. The lags between the input variables and the output variables vary due to changes in operating conditions. Previous studies have introduced different methods to estimate lags for input and output variables, but all of them have assumed these lags to be constant regardless of the changes in the operating conditions.
In this work, an inferential sensor that can predict the composition of ethanol at the top product using time lags for the input variables and varied first-order time constant lags with the output variable has been developed. The developed inferential sensor is based on a neural network (NN) model. Principal Component Analysis (PCA) and Projection to Latent Structures (PLS) methods are used in this work to remove the outliers from the input variables set and to determine the most correlated values of the input variables and their lags with the output variable Xa (ethanol composition of distillate product) respectively. The model adaptively selects the correct first-order time constant lags of an output variable according to the instantaneous operating condition (the composition of ethanol is increased or decreased) and assigns a best value for each case. The experimental data resulting from the operation of pilot-scale batch distillation column of ethanol-water system has been used to build these NN models first and then to validate their performance. The proposed NN model structure with time lags for input variables and varied first-order time constant lags for output variable gave higher accuracy compared with the NN model without any time lag for input and output variables.
This new developed NN based soft sensor has been used in an inferential proportional-integral (PI) control scheme to control the ethanol composition of the distillate. The initial inferential control results of using one tuning parameter set during the whole operation showed imperfect control results. So, using updated tuning parameter sets (gain scheduling/adaptive tuning) within this inferential PI control scheme based on the ethanol mole fraction region is necessary to improve the control performance. The results of this new developed PI control scheme showed a good control performance compared with the initial control results of this inferential controller using one set of tuning parameters.
Then, this new developed NN based soft sensor has also been used in an advanced control scheme (model predictive control or MPC scheme). Two DeltaV MPC control schemes (MPC11 and MPC22) have been developed in this work. The control results of DeltaV MPC22 control scheme showed better control performance compared with other control schemes (inferential PI and MPC11…
Advisors/Committee Members: Edgar, Thomas F. (advisor), Baldea, Michael (committee member), Rochelle, Gary T. (committee member), Akella, Maruthi R. (committee member).
Subjects/Keywords: Data-driven soft sensors; ANN; Batch distillation; Inferential PI control; Model predictive control (MPC)
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APA ·
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APA (6th Edition):
Abdulla, T. A. (2019). An experimental investigation of batch distillation column control. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/5871
Chicago Manual of Style (16th Edition):
Abdulla, Thaer Adnan. “An experimental investigation of batch distillation column control.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://dx.doi.org/10.26153/tsw/5871.
MLA Handbook (7th Edition):
Abdulla, Thaer Adnan. “An experimental investigation of batch distillation column control.” 2019. Web. 24 Jan 2021.
Vancouver:
Abdulla TA. An experimental investigation of batch distillation column control. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Jan 24].
Available from: http://dx.doi.org/10.26153/tsw/5871.
Council of Science Editors:
Abdulla TA. An experimental investigation of batch distillation column control. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/5871
University of Texas – Austin
9.
DeRosa, Sean Edward.
Impact of natural gas and natural gas liquids on chemical manufacturing in the United States.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/39644
► Natural gas and natural gas liquids production in the United States has increased dramatically since 2005, due primarily to recent advancements in horizontal drilling and…
(more)
▼ Natural gas and natural gas liquids production in the United States has increased dramatically since 2005, due primarily to recent advancements in horizontal drilling and hydraulic fracturing. As raw materials for chemical production, the increased availability, at low cost, of these materials has the potential to change the structure of the United States chemical manufacturing industry. Industry-wide modeling, coupled with region-specific analysis, was used to map potential changes in chemical manufacturing as natural gas liquids continue to expand their influence in the chemical manufacturing industry. A network model was used to analyze technology development and to evaluate trends in the industry based on material flows throughout supply chains. Agent-based modeling and simulation was used for analysis of individual chemical markets and to determine the viability of emerging markets. The network model was used to quantify how downstream chemical supply chains respond to changes in natural gas and natural gas liquid prices. The model was also used to identify new reaction pathways that may become viable as the industry evolves and how those new pathways will impact costs and utility consumption in the system of chemical manufacturing technologies. Using the Four Corners region as a case study, an analytic process was developed and implemented to evaluate greenfield manufacturing based on regional feedstock availability and global chemical markets. Conceptual development of a comprehensive model of the natural gas liquids industry was also completed to map the challenges in developing chemical manufacturing system models that will include the impacts of exports, midstream infrastructure, supply, and new chemical demand.
Advisors/Committee Members: Allen, David T. (advisor), Edgar, Thomas F (committee member), Baldea, Michael (committee member), Webber, Michael E (committee member), Olmstead, Sheila M (committee member).
Subjects/Keywords: Petrochemical; Network model; Agent-based model; Life cycle assessment
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APA (6th Edition):
DeRosa, S. E. (2016). Impact of natural gas and natural gas liquids on chemical manufacturing in the United States. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39644
Chicago Manual of Style (16th Edition):
DeRosa, Sean Edward. “Impact of natural gas and natural gas liquids on chemical manufacturing in the United States.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/39644.
MLA Handbook (7th Edition):
DeRosa, Sean Edward. “Impact of natural gas and natural gas liquids on chemical manufacturing in the United States.” 2016. Web. 24 Jan 2021.
Vancouver:
DeRosa SE. Impact of natural gas and natural gas liquids on chemical manufacturing in the United States. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/39644.
Council of Science Editors:
DeRosa SE. Impact of natural gas and natural gas liquids on chemical manufacturing in the United States. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/39644
University of Texas – Austin
10.
Ganesh, Hari Sai.
Modeling, control, and optimization of an industrial austenitization furnace.
Degree: PhD, Chemical Engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/1047
► Steel production and processing is both energy-intensive (2% of overall energy consumption) and one of the biggest contributors to CO₂ emissions. Its use is projected…
(more)
▼ Steel production and processing is both energy-intensive (2% of overall energy consumption) and one of the biggest contributors to CO₂ emissions. Its use is projected to increase by 1.5 times that of present levels (around 1.6 billion metric tonnes per year) by 2050 to meet the needs of a growing population. The main goal of this research is to minimize the energy consumption of a steel quench hardening (or heat treating) process, currently in operation at an industrial partner, by mathematical modeling, optimization, advanced control, and heat integration.
The quench hardening processes consists of heating pre-finished metal parts to a certain temperature in a continuously operating furnace (austenitization), followed by rapid cooling (quenching) in water, brine or oil to induce desired metallurgical properties like hardness, toughness, shear strength, tensile strength, etc. The novelty of this work lies in the two scale modeling approach considered to solve the furnace energy consumption minimization problem. We improve a previously developed two-dimensional (2D) physicsbased model of the heat treating furnace that computes the energy usage of the furnace and the part temperature distribution as a function of time and position within the furnace under temperature feedback control. We predict the effect of process variables on microstructural evolution of the parts using an empirical relation reported in the literature and their consequent effects on the metallurgical properties of the quenched product. The physics-based model combined with the empirical model is used to simulate the furnace operation for a batch of parts processed sequentially under heuristic temperature set points with a simple linear control strategy suggested by the operators of the plant. We then minimize the energy consumption of the furnace without compromising the product quality by real-time optimization (RTO), model predictive control (MPC), and heat integration using radiant recuperators. Energy savings of 3.7%, 15.93%, and 20.88% were obtained under model predictive control, heat integration, and optimized set points respectively compared to reference heuristic operation case without heat integration and MPC.
Advisors/Committee Members: Baldea, Michael (advisor), Edgar, Thomas F. (advisor), Ezekoye, Ofodike A. (committee member), Rochelle, Gary T. (committee member).
Subjects/Keywords: Austenitization; Furnace; Modeling; Optimization; Control
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APA (6th Edition):
Ganesh, H. S. (2019). Modeling, control, and optimization of an industrial austenitization furnace. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1047
Chicago Manual of Style (16th Edition):
Ganesh, Hari Sai. “Modeling, control, and optimization of an industrial austenitization furnace.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://dx.doi.org/10.26153/tsw/1047.
MLA Handbook (7th Edition):
Ganesh, Hari Sai. “Modeling, control, and optimization of an industrial austenitization furnace.” 2019. Web. 24 Jan 2021.
Vancouver:
Ganesh HS. Modeling, control, and optimization of an industrial austenitization furnace. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Jan 24].
Available from: http://dx.doi.org/10.26153/tsw/1047.
Council of Science Editors:
Ganesh HS. Modeling, control, and optimization of an industrial austenitization furnace. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1047
University of Texas – Austin
11.
Kumar, Ankur, Ph. D.
Model based operation of industrial steam methane reformers using large scale sensor data.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/46443
► Large quantities of hydrogen are consumed in refineries and for production of important chemicals such as ammonia and methanol. Declining crude-oil quality and increased fertilizer…
(more)
▼ Large quantities of hydrogen are consumed in refineries and for production of important chemicals such as ammonia and methanol. Declining crude-oil quality and increased fertilizer demands, among others, have led to further increase in hydrogen demand. A significant portion (~80%) of industrial hydrogen consumption is met via natural-gas steam methane reforming. This process takes place in a large scale, high-temperature, and highly energy-intensive unit called a steam methane reformer (SMR), where endothermic reforming reactions are carried out in hundreds of catalyst-filled tubes placed in a gas-fired furnace. A typical modern hydrogen production plant consumes a substantial amount (~10
5 GJ) of natural gas per day. The overall productivity (energy consumed per unit H2 produced) of the plant is strongly dependent on how efficiently the SMR is operated, which further depends on the spatial temperature distribution inside the furnace, where a more uniform distribution paves the way for reduced plant-wide energy use. Controlling the temperature distribution is, however, a challenging task due to the distributed nature of the system and the difficulty of obtaining distributed temperature measurements (the latter associated with the extreme operating conditions and the complex geometry of the furnace). In this thesis, results concerning the monitoring of temperature distribution in an industrial SMR furnace using a large array of infrared camera sensors, which produce a significant stream of data regarding the furnace temperature distribution, are presented. Specifically, strategies for homogenization of reformer tube-wall temperature distribution, also called furnace balancing, using reduced-order and physics-based models are developed. First, for a proof-of-concept study, a computational fluid dynamics (CFD) model of a small scale SMR system is developed as a substitute for a real plant. A proper orthogonal decomposition-based reduced-order linear model is used to modulate the fuel distribution among the burners. It is shown that a reduced-order empirical model with much lower computational requirements, when trained with sufficiently rich data, can be a viable substitute to the detailed modeling of the complex thermal and flow interactions in the furnace. Next, the data-driven modeling approach is extended to a real full-scale industrial SMR furnace. Shortcomings in popular empirical modeling approaches such as partial least squares (PLS) and ordinary least squares (OLS) are highlighted and a novel egg-crate SMR (EC-SMR) model is proposed. The model is calibrated using temperature measurements from the infrared cameras. Experimental results confirm that the proposed framework has excellent performance providing a 44% improvement in temperature distribution non-uniformity. While computationally intensive CFD models are not suitable for use in furnace efficiency optimization calculations, empirical models (data-driven reduced-order models) have limited accuracy when wide changes in operating conditions are…
Advisors/Committee Members: Edgar, Thomas F. (advisor), Baldea, Michael (advisor), Bonnecaze, Roger T (committee member), Ezekoye, Ofodike A (committee member), Beaman, Joseph J (committee member).
Subjects/Keywords: Steam methane reformer; Distributed parameter control; Plantwide optimization; Hydrogen plant optimization; Smart manufacturing
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APA (6th Edition):
Kumar, Ankur, P. D. (2016). Model based operation of industrial steam methane reformers using large scale sensor data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46443
Chicago Manual of Style (16th Edition):
Kumar, Ankur, Ph D. “Model based operation of industrial steam methane reformers using large scale sensor data.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/46443.
MLA Handbook (7th Edition):
Kumar, Ankur, Ph D. “Model based operation of industrial steam methane reformers using large scale sensor data.” 2016. Web. 24 Jan 2021.
Vancouver:
Kumar, Ankur PD. Model based operation of industrial steam methane reformers using large scale sensor data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/46443.
Council of Science Editors:
Kumar, Ankur PD. Model based operation of industrial steam methane reformers using large scale sensor data. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46443
University of Texas – Austin
12.
Pattison, Richard C.
Equation-oriented modeling, simulation, and optimization of integrated and intensified process and energy systems.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/46459
► Process intensification, defined as unconventional design and/or operation of processes that results in substantial performance improvements, represents a promising route toward reducing capital and operating…
(more)
▼ Process intensification, defined as unconventional design and/or operation of processes that results in substantial performance improvements, represents a promising route toward reducing capital and operating expenses in the chemical/petrochemical process industry, while simultaneously achieving improved safety and environmental performance. In this dissertation, intensification is approached from three different angles: reactor design and control, process flowsheet design and optimization, and production scheduling and control. In the first part of the dissertation, three novel concepts for improving the controllability of intensified microchannel reactors are introduced. The first concept is a latent energy storage-based temperature controller, where a phase change material is confined within the walls of an autothermal reactor to improve local temperature control. The second concept is a segmented catalyst layer which modulates the rate of heat generation and consumption along the length of an autothermal reactor. Finally, the third concept is a thermally actuated valve, which uses small-scale bimetallic strips to modulate flow in a microchannel reactor in response to temperature changes. The second part of the dissertation introduces a novel framework for equation-oriented flowsheet modeling, simulation and optimization. The framework consists of a pseudo-transient reformulation of the steady-state material and energy balance equations of process unit operations as differential-algebraic equation (DAE) systems that are statically equivalent to the original model. I show that these pseudo-transient models improve the convergence properties of equation-oriented process flowsheet simulations by expanding the convergence basin in comparison to conventional steady state equation-oriented simulators. A library of pseudo-transient unit operation models is developed, and several case studies are presented. Models for more complex unit operations such as a pseudo-transient multistream heat exchanger and a dividing-wall distillation column are later introduced, and can easily be included in the flowsheet optimization framework. In the final part of the dissertation, a paradigm for calculating the optimal production schedule in a fast changing market situation is introduced. This is accomplished by including a model of the dynamics of a process and its control system into production scheduling calculations. The scheduling-relevant dynamic models are constructed to be of lower order than a detailed dynamic process model, while capturing the closed-loop behavior of a set of scheduling-relevant variables. Additionally, a method is given for carrying out these production scheduling calculations online and in "closed scheduling loop,"' i.e., recalculating scheduling decisions upon the advent of scheduling-relevant process or market events. An air separation unit operating in a demand response scenario is used as a representative case study.
Advisors/Committee Members: Baldea, Michael (advisor), Edgar, Thomas F. (committee member), Rochelle, Gary T (committee member), Bonnecaze, Roger T (committee member), Biros, George (committee member).
Subjects/Keywords: Process modeling; Process control; Flowsheet optimization; Integration of scheduling and control; Equation-oriented modeling; Process intensification; Process integration
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APA (6th Edition):
Pattison, R. C. (2016). Equation-oriented modeling, simulation, and optimization of integrated and intensified process and energy systems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46459
Chicago Manual of Style (16th Edition):
Pattison, Richard C. “Equation-oriented modeling, simulation, and optimization of integrated and intensified process and energy systems.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/46459.
MLA Handbook (7th Edition):
Pattison, Richard C. “Equation-oriented modeling, simulation, and optimization of integrated and intensified process and energy systems.” 2016. Web. 24 Jan 2021.
Vancouver:
Pattison RC. Equation-oriented modeling, simulation, and optimization of integrated and intensified process and energy systems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/46459.
Council of Science Editors:
Pattison RC. Equation-oriented modeling, simulation, and optimization of integrated and intensified process and energy systems. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46459
University of Texas – Austin
13.
Ondeck, Abigail Devin.
The economic feasibility of combined heat and power as a utility producer for the residential sector.
Degree: PhD, Chemical Engineering, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/60378
► Combined heat and power (CHP) plants are a very promising prospect to reducing CO₂ emissions and increasing efficiency in the power generation sector, especially when…
(more)
▼ Combined heat and power (CHP) plants are a very promising prospect to reducing CO₂ emissions and increasing efficiency in the power generation sector, especially when combined with residential solar photovoltaic (PV) power generation. By utilizing natural gas, a cleaner fuel than coal, CHP plants can reduce CO₂ emissions, while exploiting the waste heat from electricity production to generate a useful thermal energy, increasing the overall efficiency of the plant. While incorporating residential solar PV power generation has important environmental benefits, it can - if not properly managed - lead to an over-generation situation with very high power plant ramp rates. Most current power plants are unlikely to be able to withstand such rapid changes in generation rates. If PV generation is incorporated into the design and operation of the CHP plant, both thermal and electrical energy storage systems can be included, opening the door to more strategies for controlling photovoltaic generation and increased PV power generation. The ability to combine thermal and electrical energy generation in an efficient manner, on a medium to large scale, suggests that CHP plants with rooftop PV panels and energy storage are an appealing choice as an integrated utility supplier for the neighborhood of the future. Yet, there are currently no CHP plants that serve exclusively residential neighborhoods in the United States. Thus, the objective of this research was to determine the most economical design and operation of a CHP plant with integrated residential solar PV power generation to meet all the energy demands of a residential neighborhood. After determining that a CHP plant could meet all the electricity, heating, and cooling demands of a residential neighborhood, a multi-scale economical optimization formulation to simultaneously determine the design and operation of a CHP plant with PV generation was constructed. The optimal CHP plant produced extra energy, so the optimization formulation was updated to include both thermal and electrical energy storage. Utilizing the results from these optimizations, the monetary values of PV generation and energy storage were evaluated, giving a guide for future economic targets for these technologies.
Advisors/Committee Members: Edgar, Thomas F. (advisor), Baldea, Michael (advisor), Baldick, Ross (committee member), Novoselac, Atila (committee member), Truskett, Thomas (committee member).
Subjects/Keywords: Combined heat and power; Energy; Renewables; Residential district utilities
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Export
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APA (6th Edition):
Ondeck, A. D. (2017). The economic feasibility of combined heat and power as a utility producer for the residential sector. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/60378
Chicago Manual of Style (16th Edition):
Ondeck, Abigail Devin. “The economic feasibility of combined heat and power as a utility producer for the residential sector.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/60378.
MLA Handbook (7th Edition):
Ondeck, Abigail Devin. “The economic feasibility of combined heat and power as a utility producer for the residential sector.” 2017. Web. 24 Jan 2021.
Vancouver:
Ondeck AD. The economic feasibility of combined heat and power as a utility producer for the residential sector. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/60378.
Council of Science Editors:
Ondeck AD. The economic feasibility of combined heat and power as a utility producer for the residential sector. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/60378
University of Texas – Austin
14.
Park, Jungup.
Data-driven modeling and optimization of sequential batch-continuous process.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/39461
► Driven by the need to lower capital expenditures and operating costs, as well as by competitive pressure to increase product quality and consistency, modern chemical…
(more)
▼ Driven by the need to lower capital expenditures and operating costs, as well as by competitive pressure to increase product quality and consistency, modern chemical processes have become increasingly complex. These trends are manifest, on the one hand, in complex equipment configurations and, on the other hand, in a broad array of sensors (and control systems), which generate large quantities of operating data. Of particular interest is the combination of two traditional routes of chemical processing: batch and continuous. Batch to continuous processes (B2C), which constitute the topic of this dissertation, comprise of a batch section, which is responsible for preparing the materials that are then processed in the continuous section. In addition to merging the modeling, control and optimization approaches related to the batch and continuous operating paradigms – which are radically different in many aspects – challenges related to analyzing the operation of such processes arise from the multi-phase flow. In particular, we will be considering the case where a particulate solid is suspended in a liquid ``carrier'', in the batch stage, and the two-phase mixture is conveyed through the continuous stage. Our explicit goal is to provide a complete operating solution for such processes, starting with the development of meaningful and computationally efficient mathematical models, continuing with a control and fault detection solution, and finally, a production scheduling concept. Owing to process complexity, we reject out of hand the use of first-principles models, which are inevitably high dimensional and computationally expensive, and focus on data-driven approaches instead. Raw data obtained from chemical industry are subject to noise, equipment malfunction and communication failures and, as such, data recorded in process historian databases may contain outliers and measurement noise. Without proper pretreatment, the accuracy and performance of a model derived from such data may be inadequate. In the next chapter of this dissertation, we address this issue, and evaluate several data outlier removal techniques and filtering methods using actual production data from an industrial B2C system. We also address a specific challenge of B2C systems, that is, synchronizing the timing of the batch data need with the data collected from the continuous section of the process. Variable-wise unfolded data (a typical approach for batch processes) exhibit measurement gaps between the batches; however, this type of behavior cannot be found in the subsequent continuous section. These data gaps have an impact on data analysis and, in order to address this issue, we provide a method for filling in the missing values. The batch characteristic values are assigned in the gaps to match the data length with the continuous process, a procedure that preserves meaningful process correlations. Data-driven modeling techniques such as principal component analysis (PCA) and partial least squares (PLS) regression are well-established for modeling batch…
Advisors/Committee Members: Edgar, Thomas F. (advisor), Baldea, Michael (advisor), Djurdjanovic, Dragan (committee member), Rochelle, Gary T (committee member), Truskett, Thomas M (committee member).
Subjects/Keywords: Sequential batch-continuous process; Data-driven modeling; Time scale bridging model; Scheduling; Control; Real-time optimization
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APA ·
Chicago ·
MLA ·
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APA (6th Edition):
Park, J. (2016). Data-driven modeling and optimization of sequential batch-continuous process. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39461
Chicago Manual of Style (16th Edition):
Park, Jungup. “Data-driven modeling and optimization of sequential batch-continuous process.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/39461.
MLA Handbook (7th Edition):
Park, Jungup. “Data-driven modeling and optimization of sequential batch-continuous process.” 2016. Web. 24 Jan 2021.
Vancouver:
Park J. Data-driven modeling and optimization of sequential batch-continuous process. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/39461.
Council of Science Editors:
Park J. Data-driven modeling and optimization of sequential batch-continuous process. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/39461
15.
-1509-3807.
Application of optical coherence tomography for improved in-situ flaw detection in nylon 12 selective laser sintering.
Degree: PhD, Chemical Engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/2183
► Despite significant advances made since the inception of selective laser sintering (SLS), many of the same problems identified by early researchers including high part porosity,…
(more)
▼ Despite significant advances made since the inception of selective laser sintering (SLS), many of the same problems identified by early researchers including high part porosity, inadequate surface finish, and part strength uncertainty persist today. Because of these challenges, quality validation and improved process control continue to be identified as critical areas of improvement in industry roadmaps. To address these issues, an optical coherence tomography (OCT) sensor is investigated for feasibility of use in in-situ flaw detection in SLS. Benchtop OCT imaging of nylon in solid, liquid, and resolidified phases revealed subsurface imaging through liquid and resolidified nylon material was possible.
Subsequent benchtop imaging showed that multiple-scattering was the cause of an imaging artifact which contributed to the limited imaging depth in nylon powder. Additionally, nylon powder was continuously imaged before, during, and after melting and resolidification. The resulting images showed scattering was consistent with the presence of crystalline spherulites, suggesting the spherulites are a strong source of scattering in the nylon 12.
An OCT sensor was subsequently mounted on a production-sized research SLS machine. Design and implementation information is detailed including artifact correction and noise subtraction strategies. The OCT sensor is then used to detect various common defects in the SLS process. Imaging single layer individual scanlines revealed deeper melt depth due to overheating from galvo deceleration near the end of the scan lines. Additionally, surface curl was able to be quantified and visualized for a build. Finally, an SLS build was performed at higher powder bed temperatures. OCT images collected from the build were compared with X-ray computed tomography (CT) images, and many of the pores in the OCT images are shown to agree well with those detected in the CT images. One pore in the dataset was much larger than the others in the part. This caused the author to hypothesize that a different mode was responsible for creating these pores which a subsequent build confirmed. A summary of contributions and future work is also listed.
Advisors/Committee Members: Beaman, Joseph J. (advisor), Edgar, Thomas F. (advisor), Milner, Thomas (committee member), Bonnecaze, Roger T (committee member), Lynd, Nathaniel (committee member).
Subjects/Keywords: Selective laser sintering; Optical coherence tomography; Additive manufacturing; Non destructive evaluation; Sensing
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APA ·
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Export
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APA (6th Edition):
-1509-3807. (2019). Application of optical coherence tomography for improved in-situ flaw detection in nylon 12 selective laser sintering. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2183
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-1509-3807. “Application of optical coherence tomography for improved in-situ flaw detection in nylon 12 selective laser sintering.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://dx.doi.org/10.26153/tsw/2183.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-1509-3807. “Application of optical coherence tomography for improved in-situ flaw detection in nylon 12 selective laser sintering.” 2019. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-1509-3807. Application of optical coherence tomography for improved in-situ flaw detection in nylon 12 selective laser sintering. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Jan 24].
Available from: http://dx.doi.org/10.26153/tsw/2183.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-1509-3807. Application of optical coherence tomography for improved in-situ flaw detection in nylon 12 selective laser sintering. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2183
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
16.
-7739-8836.
Data-driven methods for improved decision-making in the chemical process industries.
Degree: PhD, Chemical Engineering, 2020, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/9108
► Recent decades have prompted chemical manufacturers to consider new operating paradigms. Globalization and other market trends have reduced profit margins and emphasized the need for…
(more)
▼ Recent decades have prompted chemical manufacturers to consider new operating paradigms. Globalization and other market trends have reduced profit margins and emphasized the need for processes to operate in a more flexible and agile manner, e.g., to rapidly shift productions targets in response to real-time economic data, or to benefit from participation in short-term electricity markets. The twin prongs of (dynamic) process modeling and mathematical optimization have been key in meeting these challenges. One example is the widely adopted model-predictive control, an optimization-based feedback control framework which uses a dynamic model to determine an optimal sequence of control inputs. More broadly, there is a growing trend toward integrating modeling / optimization problems across the decisional hierarchy, ranging from design and long-term planning to the scheduling and real-time operation of process units. In this dissertation, I propose data-driven solutions that address some of these problems. The first part of this dissertation is concerned with the problem of model quality maintenance for model predictive controllers. I propose a statistical method for locating and estimating plant-model mismatch in these systems, formulated as an optimization problem which minimizes the discrepancy between theoretical and empirical statistics associated with the process variables. The method is capable of detecting and estimating the magnitude of plant-model mismatch in industrially relevant controllers, i.e., using state-space dynamic models and including state estimation. Furthermore, the procedure can be applied to data collected from normal process operation, without requiring costly system re-identification tests. Case studies demonstrate very good performance of the proposed method. The second part of this dissertation is focused on the problem of integrating process scheduling with (nonlinear) dynamics of the control system and the process itself. Such efforts are motivated by the increasing overlap in the time scales of the respective layers in the decisional hierarchy: as scheduling decisions are made more frequently, e.g., as modulation of throughput for participation in demand response; and/or as plant-wide dynamics become slower, e.g., with greater energy/material integration. These trends require integrated solution methods in order to obtain optimal operating policies. First, I propose a framework for explicitly representing the behavior of dynamic systems under model-predictive control within scheduling optimization problems. My approach converts this large-scale bi-level problem into a single-level "mathematical problem with complementarity constraints,'' in which the optimality conditions of the lower-level MPC problem are embedded directly in the upper-level scheduling problem. Reformulations of the resulting nonlinear optimization problem are proposed to improve computational performance. Two case studies demonstrate that the integrated problem achieves better performance relatively to alternative (i.e.,…
Advisors/Committee Members: Baldea, Michael (advisor), Edgar, Thomas F (committee member), Bakolas, Efsthathios (committee member), Bonnecaze, Roger T (committee member), Djurdjanovic, Dragan (committee member).
Subjects/Keywords: Process systems engineering; Optimal scheduling and control
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APA (6th Edition):
-7739-8836. (2020). Data-driven methods for improved decision-making in the chemical process industries. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/9108
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-7739-8836. “Data-driven methods for improved decision-making in the chemical process industries.” 2020. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://dx.doi.org/10.26153/tsw/9108.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-7739-8836. “Data-driven methods for improved decision-making in the chemical process industries.” 2020. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-7739-8836. Data-driven methods for improved decision-making in the chemical process industries. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2020. [cited 2021 Jan 24].
Available from: http://dx.doi.org/10.26153/tsw/9108.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-7739-8836. Data-driven methods for improved decision-making in the chemical process industries. [Doctoral Dissertation]. University of Texas – Austin; 2020. Available from: http://dx.doi.org/10.26153/tsw/9108
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
17.
Wang, Ray Chen.
Geometric fault detection using 3D Kiviat plots and their applications.
Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin
URL: http://hdl.handle.net/2152/47439
► The surge in large-scale data being collected through various social and economic systems comes along with the ever-increasing need to understand and gain insight from…
(more)
▼ The surge in large-scale data being collected through various social and economic systems comes along with the ever-increasing need to understand and gain insight from the data being collected. This has spurred on the development and advent of big data analytics in many different areas such as healthcare, e-commerce, and group-sharing applications. This applies also to the process industry as well, as the development of more complex processes, which in turn require increased monitoring, mean that a larger amount of data are being collected than previously seen. This data is not only high in volume (measurements taken with a high sampling frequency), but also high in dimensionality (many sensors set up throughout the process). Process monitoring requires the continuous observation of such high dimensional and high volume data, but current visualization techniques do not lend themselves to do doing so. Furthermore, parallel to process monitoring is the desire for fault detection capability – to detect faults as soon as they occur or predict them before they occur. For that reason it is ideal if there is a visualization technique that also contributes to fault detection efforts, so that both process monitoring and fault detection is satisfied. To that end, in this dissertation the development of three-dimensional (3D) Kiviat diagrams and its use in fault detection is explored in great detail. In Kiviat diagrams, axes are laid out radially around a center point, in contrast to axes being perpendicular to one another in traditional score plots, or in parallel to one another as seen in parallel coordinates. This theoretically allows for an infinite number of axes, and therefore high dimensional data, to be plotted on one figure at once. Due to the time-explicit nature of process data, the addition of a third axis normal to the Kiviat diagram is proposed as well. In the Kiviat diagram representation, each sample forms a polygon on the plot. This is taken advantage of for fault detection purposes by condensing each polygon into its centroid. By doing so the state of the process at every point in time can be represented by its centroid – this allows for multivariate fault detection to be performed. Using these centroids, a variety of fault detection mechanisms are proposed specific to the types of processes the data is obtained from. The mechanisms are developed for 3 process types commonly seen in industry – continuous processes, batch processes, and periodic processes. For each process type the fault detection mechanism is detailed and case studies are laid out, demonstrating the application of the method.
Advisors/Committee Members: Baldick, Ross (advisor), Baldea, Michael (advisor), Arapostathis, Ari (committee member), Edgar, Thomas F (committee member), Ghosh, Joydeep (committee member).
Subjects/Keywords: Fault detection; Data visualization; 3D Kiviat plots; Fault detection capability; Process monitoring; Kiviat diagrams; High dimensional data; Process data
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APA (6th Edition):
Wang, R. C. (2017). Geometric fault detection using 3D Kiviat plots and their applications. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47439
Chicago Manual of Style (16th Edition):
Wang, Ray Chen. “Geometric fault detection using 3D Kiviat plots and their applications.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/47439.
MLA Handbook (7th Edition):
Wang, Ray Chen. “Geometric fault detection using 3D Kiviat plots and their applications.” 2017. Web. 24 Jan 2021.
Vancouver:
Wang RC. Geometric fault detection using 3D Kiviat plots and their applications. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/47439.
Council of Science Editors:
Wang RC. Geometric fault detection using 3D Kiviat plots and their applications. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47439
University of Texas – Austin
18.
Rhodes, Joshua Daniel.
Optimal residential energy consumption, prediction, and analysis.
Degree: PhD, Civil Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/33342
► In the United States, buildings are responsible for 40.36 Quads (40.36 x 10¹⁵ BTU) of total primary energy consumption per year, 22.15 of which are…
(more)
▼ In the United States, buildings are responsible for 40.36 Quads (40.36 x 10¹⁵ BTU) of total primary energy consumption per year, 22.15 of which are used in residential buildings (reference year 2010). Also, the United States residential sector is responsible for about 20% of United States carbon emissions or about 4% of the world's total. While there are over 130 million residential units in the United States, only 0.1% of R&D is spent in the residential sector. This means the residential sector represents an underinvested opportunity for energy savings. Tackling that problem, this dissertation presents work that is focused on assessing, analyzing, and optimizing how residential buildings use and generate energy. This work presents an analysis of a unique dataset of 4971 energy audits performed on homes in
Austin,
Texas in 2009 - 2010. The analysis quantifies the prevalence of typical air-conditioner design and installation issues such as low efficiency, oversizing, duct leakage, and low measured capacity, then estimates the impacts that resolving these issues would have on peak power demand and cooling energy consumption. It is estimated that air-conditioner use in single-family residences currently accounts for 17 - 18% of peak demand in
Austin, and that improving equipment efficiency alone could save up to 205 MW, or 8%, of peak demand. It was also found that 31% of systems in this study were oversized, leading to up to 41 MW of excess peak demand. Replacing oversized systems with correctly sized higher efficiency units has the potential for further savings of up to 81 MW. Also, the mean system could achieve 18% and 20% in cooling energy savings by sealing duct leaks and servicing air-conditioning units to achieve 100% of nominal capacity, respectively. A different dataset of measured whole-home electricity consumption from 103 homes in
Austin, TX was analyzed to 1) determine the shape of seasonally-resolved residential demand profiles, 2) determine the optimal number of normalized representative residential electricity use profiles within each season, and 3) draw correlations to the different profiles based on survey data from the occupants of the 103 homes. Within each season, homes with similar hourly electricity use patterns were clustered into groups using the k-means clustering algorithm. The number of groups within each season was determined by comparing 30 different optimal clustering criteria. Then probit regression was performed to determine if homeowner survey responses could serve as explanatory variables for the clustering results. This analysis found that
Austin homes typically fall into one of two seasonal groups. Because these groups differ in temporal energy use and the wholesale electricity price is temporal, homes in one group use more expensive electricity than others. The probit regression results indicated that variables such as whether or not someone worked from home, the number of hours of television watched per week, and level of education have significant correlation with average profile…
Advisors/Committee Members: Webber, Michael E., 1971- (advisor), Blackhurst, Michael F (committee member), Edgar, Thomas F (committee member), King, Carey (committee member), Novoselac, Atila (committee member).
Subjects/Keywords: Residential energy use; Peak energy use; HVAC; Temporal energy use
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APA (6th Edition):
Rhodes, J. D. (2014). Optimal residential energy consumption, prediction, and analysis. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33342
Chicago Manual of Style (16th Edition):
Rhodes, Joshua Daniel. “Optimal residential energy consumption, prediction, and analysis.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/33342.
MLA Handbook (7th Edition):
Rhodes, Joshua Daniel. “Optimal residential energy consumption, prediction, and analysis.” 2014. Web. 24 Jan 2021.
Vancouver:
Rhodes JD. Optimal residential energy consumption, prediction, and analysis. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/33342.
Council of Science Editors:
Rhodes JD. Optimal residential energy consumption, prediction, and analysis. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/33342
University of Texas – Austin
19.
-9912-2897.
Embedding dynamics and control considerations in operational optimization of process and energy systems.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/39556
► Embedding dynamics and control considerations within operational optimization decisions can result in improved performance of processes and energy systems. These efforts are motivated by modern…
(more)
▼ Embedding dynamics and control considerations within operational optimization decisions can result in improved performance of processes and energy systems. These efforts are motivated by modern sustainability initiatives, in particular demand response and demand management strategies for improving the efficiency of the electric grid. In these scenarios residential, commercial, and industrial electricity consumers are provided with a financial incentive to shift their demand such that the total load on the grid can be satisfied using efficient generation technologies and renewable energy sources. The financial incentive is typically a time-dependent price structure, where rates reflect the demand level and stress on the grid. Reacting to such fast-changing energy markets requires that process and energy systems be highly flexible, which is a significant departure from traditional steady state operation under fixed market conditions. In this context, flexibility means the ability to make frequent changes to the system operation (e.g., production setpoints, constraint levels, etc.) while still maintaining stability and satisfying operating constraints at all times. This necessitates the development of advanced control and decision making strategies which are aware of system dynamics. Accounting for dynamics by incorporating detailed, first-principles models of a system into optimization-based controllers or scheduling calculations would provide ample dynamic information. However, the resulting dynamic optimization formulations would be plagued by a large problem size, numerical difficulties associated with stiff equations and multiple time scales, and the presence of integer decisions. In this dissertation, we address these challenges through hierarchical controller designs and novel scheduling (and rescheduling) formulations including low-order models of relevant system dynamics, which are identified through an appropriate model reduction or system identification procedure. Case studies involving the built environment and chemical processes are used to demonstrate the proposed methods.
Advisors/Committee Members: Baldea, Michael (advisor), Edgar, Thomas F (committee member), Truskett, Thomas M (committee member), Bonnecaze, Roger T (committee member), Novpselac, Atila (committee member).
Subjects/Keywords: Optimization; Control
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APA (6th Edition):
-9912-2897. (2016). Embedding dynamics and control considerations in operational optimization of process and energy systems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39556
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-9912-2897. “Embedding dynamics and control considerations in operational optimization of process and energy systems.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/39556.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-9912-2897. “Embedding dynamics and control considerations in operational optimization of process and energy systems.” 2016. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-9912-2897. Embedding dynamics and control considerations in operational optimization of process and energy systems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/39556.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-9912-2897. Embedding dynamics and control considerations in operational optimization of process and energy systems. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/39556
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
20.
Duribe, Victor Chijioke.
Capacitance resistance modeling for improved characterization in waterflooding and thermal recovery projects.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/45711
► Rates are typically one of the most measured in an oil recovery project. The abundance of these types of data is explained partly by their…
(more)
▼ Rates are typically one of the most measured in an oil recovery project. The abundance of these types of data is explained partly by their relative ease of collection. Additionally, their collection and reporting is often required for logistical as well as financial purposes. Numerous researchers have shown the potency of using these data for characterization and management of oil reservoirs under primary or secondary recovery. Reduced-order models typically use these measurements as input to characterize reservoirs. The capacitance resistance model (CRM) is one such reduced order modeling method. This model uses well rates (and bottomhole pressure data, if available) to characterize a reservoir in a cheap and fast way. In characterizing an oil reservoir, the CRM and its linear counterpart (the Integrated Capacitance Resistance Model or ICRM) use historical data available at the wells to infer connectivity and flow paths between these wells through a set of model parameters. This use of readily available data, enabled by the speed of these models, creates a powerful tool that can be used as an alternative or as a complement to more expensive and time consuming traditional reservoir management tools. The CRM was initially developed for secondary recovery (i.e., water-flooding) but has been shown to work very well for primary recovery and many enhanced oil recovery (EOR) processes. The increasing industry acceptance of this modeling method is because of the work researchers who have contributed in expanding the capabilities of this modeling approach. However, key questions such as the impact of noise of CRM and ICRM performance remain. Additionally, a rigorous way of designing injection rates (a key input into the CRM model) such that parameter estimation is optimal has not been addressed. Finally, ideas about the applicability of the CRM modeling method to thermal EOR processes has not been explored. This research aims to address these questions. By addressing these questions, this work aims to contribute towards deepening current under-standing of the CRM modeling method and to opening new avenues for its application and research.
Advisors/Committee Members: Edgar, Thomas F. (advisor), Lake, Larry W. (advisor), Sanchez, Isaac C (committee member), Baldea, Michael (committee member), Lasdon, Leon S (committee member).
Subjects/Keywords: Capacitance resistance model; CRM; ICRM; Water flooding; Hot water floods
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APA (6th Edition):
Duribe, V. C. (2016). Capacitance resistance modeling for improved characterization in waterflooding and thermal recovery projects. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/45711
Chicago Manual of Style (16th Edition):
Duribe, Victor Chijioke. “Capacitance resistance modeling for improved characterization in waterflooding and thermal recovery projects.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/45711.
MLA Handbook (7th Edition):
Duribe, Victor Chijioke. “Capacitance resistance modeling for improved characterization in waterflooding and thermal recovery projects.” 2016. Web. 24 Jan 2021.
Vancouver:
Duribe VC. Capacitance resistance modeling for improved characterization in waterflooding and thermal recovery projects. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/45711.
Council of Science Editors:
Duribe VC. Capacitance resistance modeling for improved characterization in waterflooding and thermal recovery projects. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/45711
University of Texas – Austin
21.
-9524-1741.
Addressing uncertainty and modeling error in the design and control of process systems : methods and applications.
Degree: PhD, Chemical engineering, 2016, University of Texas – Austin
URL: http://hdl.handle.net/2152/41993
► A process system faces the challenge of uncertainty throughout its lifetime. At the design stage, uncertainty originates from inaccurate knowledge of design parameters and unmeasured…
(more)
▼ A process system faces the challenge of uncertainty throughout its lifetime. At the design stage, uncertainty originates from inaccurate knowledge of design parameters and unmeasured or unmeasurable ambient disturbances. Oftentimes, designers choose to increase system size to account for uncertainty and fluctuations; however, this approach has an economic limit, past which the capital expenditure outweighs the potential operational benefits. In the operational stage, uncertainty is manifest, amongst others, in fluctuations in operating conditions, market demand and raw material availability. Another type of uncertainty in (modern) process operations is related to the quality of process models that are used for making control and operational decisions. Of particular importance is the quality of the dynamic models that are used in real-time optimal control computations. The chemical industry has been the pioneer (and is currently the leader) of model predictive control (MPC) implementations, whereby the control moves are computed, over a receding time horizon, by solving an optimal control problem at each time step. While uniquely able to deal with large-scale, non-square constrained systems, MPC is vitally dependent on the predictive abilities of the built-in model. Changes in plant conditions are a a source of uncertainty in this case as-well, leading to a discrepancy (mismatch) between the model predictions and the true plant behavior.
In this dissertation, I address the problems of design under uncertainty and plant-model mismatch. For the former, identification-based optimization (IBO) framework is proposed as a new, computationally efficient framework for optimizing the design of dynamic systems under uncertainty problem. The framework uses properly designed pseudo-random multilevel signals (PRMS) to represent time-varying uncertain variables. This allows us to formulate the design under uncertainty problem as a dynamic optimization problem. A solution algorithm is proposed using a sequential approach. Several application examples are discussed, demonstrating the superior computational performance of the IBO approach. Furthermore, an extension of the method that explicitly considers the tradeoff between conservativeness and dynamic performance is introduced.
The latter, plant-model mismatch problem, is addressed using a novel autocovariance-based approach. Under appropriate assumptions, an explicit relation is established between the autocovariance of the process output and the plant-model mismatch terms, represented either in a step response model or a transfer function model. It is demonstrated that an asymptotically correct set of estimates of the values of plant-model mismatch for each model parameters is the global minimizer of the discrepancy between the autocovariance predicted using the relation and the autocovariance calculated from a data set collected from closed-loop operating data. Extensions of this approach handle cases where the active set of the MPC is changing over time and there are setpoint…
Advisors/Committee Members: Baldea, Michael (advisor), Edgar, Thomas F. (committee member), Rochelle, Gary T. (committee member), Truskett, Thomas M. (committee member), Biros, George (committee member).
Subjects/Keywords: Uncertainty; MPC; Modeling error; Control of process systems; Applications; Methods
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APA (6th Edition):
-9524-1741. (2016). Addressing uncertainty and modeling error in the design and control of process systems : methods and applications. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/41993
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-9524-1741. “Addressing uncertainty and modeling error in the design and control of process systems : methods and applications.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/41993.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-9524-1741. “Addressing uncertainty and modeling error in the design and control of process systems : methods and applications.” 2016. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-9524-1741. Addressing uncertainty and modeling error in the design and control of process systems : methods and applications. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/41993.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-9524-1741. Addressing uncertainty and modeling error in the design and control of process systems : methods and applications. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/41993
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
22.
-6151-0678.
Plug-in electric vehicle deployment and integration with the electric grid.
Degree: PhD, Electrical and Computer Engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32852
► Key battery, semiconductor, and software technologies have sufficiently progressed over the past few decades to enable viable plug-in electric vehicle (PEV) alternatives to conventional vehicles.…
(more)
▼ Key battery, semiconductor, and software technologies have sufficiently progressed over the past few decades to enable viable plug-in electric vehicle (PEV) alternatives to conventional vehicles. Alternatives to petroleum-based fuels for transportation are sought to address concerns over energy security, foreign oil derived U.S. trade deficits, oil related geopolitical entanglements, and emissions. The various types of PEVs have substantially different characteristics. The types and key attributes of PEVs, charging standards, and charging locations are described. The likely scenario for PEV-Grid interactions over the next decade is synthesized from the analysis of the technologies available to and circumstances of vehicle manufacturers, utilities, and supplier firms. PEV adoption considerations are evolving. Many lessons have been learned from the first generation of PEVs that were introduced starting in late 2010. Technology, market, and policy drivers of emerging trends in the diffusion of PEVs are explored more in-depth. PEVs as electric loads are unique in that they are large, flexible, and intelligent. These attributes can not only provide utilities a new source of revenue, but also improve grid stability and economics. Actions, technologies, and policies that utilities can deploy to increase adoption are discussed. Actions are explored to make the overall PEV ownership experience superior to a conventional vehicle. This dissertation also describes research of the capability for PEVs in Vehicle to Home (V2H) scenarios, where the vehicle acts as a residential battery storage system and/or a backup generator in a residential microgrid configuration during a grid outage. Residential energy data collected from a smart grid testbed is used with a custom PEV model to assess the performance (in terms of duration and power output) of a BEV or PHEV used for backup power. Our earlier results quantify the extent to which photovoltaic (PV) generation and the characteristics of a PEV (battery size, gasoline availability) affect the backup duration during an electric grid outage. Strategies to further increase backup duration and non-continuous self-sustaining off-grid alternatives were found in our early V2H research. Varied amounts of load curtailment and PHEV engine-generator control improvements are modeled in subsequent research.
Advisors/Committee Members: Baldick, Ross (advisor), Santoso, Surya (committee member), Dodabalapur, Ananth (committee member), Edgar, Thomas F (committee member), Webber, Michael (committee member), Kwasinski, Alexis (committee member).
Subjects/Keywords: Plug-in electric vehicle; PEV; EV; BEV; PHEV; V2H; G2V
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APA (6th Edition):
-6151-0678. (2015). Plug-in electric vehicle deployment and integration with the electric grid. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32852
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-6151-0678. “Plug-in electric vehicle deployment and integration with the electric grid.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/32852.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-6151-0678. “Plug-in electric vehicle deployment and integration with the electric grid.” 2015. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-6151-0678. Plug-in electric vehicle deployment and integration with the electric grid. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/32852.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-6151-0678. Plug-in electric vehicle deployment and integration with the electric grid. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32852
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
23.
-3146-1283.
Modeling, estimation, and control of proton exchange membrane-based electrochemical systems.
Degree: PhD, Mechanical Engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32873
► To reduce emissions and meet the rapidly growing global energy demand, affordable and efficient methods of electrical energy storage and generation are needed to exploit…
(more)
▼ To reduce emissions and meet the rapidly growing global energy demand, affordable and efficient methods of electrical energy storage and generation are needed to exploit renewable energy sources more effectively. Proton exchange membrane (PEM) based electrochemical systems, such as vanadium redox flow batteries (VRFB) and PEM fuel cells, are playing an increasingly important role because they have a fast response rate, high efficiency, and small environmental impact. However, widespread commercial viability of these technologies in the future heavily depends on further improvements in their performance and reliability. Accordingly, this dissertation focuses on developing new methodologies to predict and control the behavior of these PEM-based electrochemical systems. In the first part of this work, a control-oriented physics-based model of a VRFB system is developed. This model can predict the transient response of the cell voltage under different operating conditions and inputs such as current, flow rate, and temperature. The significance of this study is having the ability to predict the short and long term effects of membrane crossover on the system performance. One major challenge of operating VRFB systems is that monitoring the state-of-charge (SOC) in real-time using traditional measurement techniques is expensive and impractical. To address this problem, an SOC estimator is developed based on a constrained extended Kalman filter that can be used for real-time optimization and control because it requires only simple voltage measurements and a low-order model. Simulation results demonstrate the ability to predict the vanadium concentrations of a VRFB system without knowledge of the crossover dynamics. A major obstacle preventing the widespread commercialization of VRFBs is excessive capital costs. This issue is addressed by developing a methodology to optimally size a VRFB system using the minimum amount of materials required for the intended power range. For PEM fuel cells, proper water and thermal management is critical to optimizing performance and longevity. However, this can be a challenging task due to strong system interactions between multiple input and output variables. In the final part of this work, these system interactions are studied in detail and a suitable controller is designed to regulate the stack voltage, stack temperature, and relative humidity during load transients.
Advisors/Committee Members: Chen, Dongmei, Ph. D. (advisor), Longoria, Raul G (committee member), Deshpande, Ashish D (committee member), Fahrenthold, Eric P (committee member), Edgar, Thomas F (committee member).
Subjects/Keywords: Modeling; Estimation; Control; Flow battery; PEM fuel cell
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APA (6th Edition):
-3146-1283. (2015). Modeling, estimation, and control of proton exchange membrane-based electrochemical systems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32873
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-3146-1283. “Modeling, estimation, and control of proton exchange membrane-based electrochemical systems.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/32873.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-3146-1283. “Modeling, estimation, and control of proton exchange membrane-based electrochemical systems.” 2015. Web. 24 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-3146-1283. Modeling, estimation, and control of proton exchange membrane-based electrochemical systems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/32873.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-3146-1283. Modeling, estimation, and control of proton exchange membrane-based electrochemical systems. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32873
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
University of Texas – Austin
24.
Xu, Ph. D., Shu.
Data cleaning and knowledge discovery in process data.
Degree: PhD, Chemical engineering, 2015, University of Texas – Austin
URL: http://hdl.handle.net/2152/32920
► This dissertation presents several methods for overcoming the Big Data challenges, with an emphasis on data cleaning and knowledge discovery in process data. Data cleaning…
(more)
▼ This dissertation presents several methods for overcoming the Big Data challenges, with an emphasis on data cleaning and knowledge discovery in process data. Data cleaning and knowledge discovery is chosen as a main research area here due to its importance from both theoretical and practical points of view.
Theoretical background and recent developments of data cleaning methods are reviewed from four aspects: missing data imputation, outlier detection, noise removal and time delay estimation. Moreover, the impact of contaminated data on model performance and corresponding improvement obtained by data cleaning methods are analyzed through both simulated and industrial case studies. The results provide a starting point for further advanced methodology development.
It is hard to find a universally applicable method for data cleaning since every data set may have its own distinctive features. Thus, we have to customize available methods so that the quality of the data set is guaranteed. An integrated data cleaning scheme is proposed, which incorporates model building and performance evaluation, to provide guidance in tuning the parameters of data cleaning methods and prevent over-cleaning. A case study based on industrial data has been used to verify the feasibility and effectiveness of the proposed new method, during which a partial least squares (PLS) model was built and three univariate data cleaning procedures is tested.
A time series Kalman filter (TSKF) is proposed that successfully handles outlier detection in dynamic systems, where normal process changes often mask the existence of outliers. The TSKF method combines a time series model fitting procedure with a modified Kalman filter to deal with additive outlier (AO) and innovational outlier (IO) detection problems in dynamic process data set. A comparative analysis of TSKF and available methods is performed on simulated and real chemical plant data.
Root cause diagnosis of plant-wide oscillations, as a concrete example of data cleaning and knowledge discovery in the process data, is provided. Plant-wide oscillations can negatively influence the overall control performance of the process and the detection results are often affected by noise at different frequency ranges. To address such a problem, an information transfer method combining spectral envelope algorithm with spectral transfer entropy is proposed to detect and diagnose such oscillations within a specific frequency range, mitigating the effects from measurement noise. The feasibility and effectiveness of the proposed method are verified and compared with available methods through both simulated and industrial case studies.
Advisors/Committee Members: Edgar, Thomas F. (advisor), Wojsznis, Willy (committee member), Djurdjanovic, Dragan (committee member), Rochelle, Gary T. (committee member), Baldea, Michael (committee member), Daniels, Michael J. (committee member).
Subjects/Keywords: Data cleaning; Knowledge discovery
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APA ·
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APA (6th Edition):
Xu, Ph. D., S. (2015). Data cleaning and knowledge discovery in process data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32920
Chicago Manual of Style (16th Edition):
Xu, Ph. D., Shu. “Data cleaning and knowledge discovery in process data.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/32920.
MLA Handbook (7th Edition):
Xu, Ph. D., Shu. “Data cleaning and knowledge discovery in process data.” 2015. Web. 24 Jan 2021.
Vancouver:
Xu, Ph. D. S. Data cleaning and knowledge discovery in process data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/32920.
Council of Science Editors:
Xu, Ph. D. S. Data cleaning and knowledge discovery in process data. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32920
University of Texas – Austin
25.
Nagoo, Anand Subhash.
Pipe fractional flow theory : principles and applications.
Degree: PhD, Chemical Engineering, 2014, University of Texas – Austin
URL: http://hdl.handle.net/2152/65197
► The contribution of this research is a simple, analytical mathematical modeling framework that connects multiphase pipe flow phenomena and satisfactorily reproduces key multiphase pipe flow…
(more)
▼ The contribution of this research is a simple, analytical mathematical modeling framework that connects multiphase pipe flow phenomena and satisfactorily reproduces key multiphase pipe flow experimental findings and field observations, from older classic data to modern ones. The proposed unified formulation presents, for the first time, a reliably accurate analytical solution for averaged (1D) multiphase pipe flow over a wide range of applications. The two new fundamental insights provided by this research are that: (a) macroscopic single-phase pipe flow fluid mechanics concepts can be generalized to multiphase pipe flow, and (b): viewing and analyzing multiphase pipe flow in general terms of averaged relative flow (or fractional flow) can lead to a unified understanding of its resultant (global) behavior. The first insight stems from our finding that the universal relationship that exists between pressure and velocity in single-phase flow can also be found equivalently between pressure and relative velocity in multiphase flow. This eliminates the need for a-priori flow pattern determination in calculating multiphase flow pressure gradients. The second insight signifies that, in general, averaged multiphase flow problems can be sufficiently modeled by knowing only the averaged volume fractions. This proves that flow patterns are merely the visual, spatial manifestations of the in-situ velocity and volume fraction distributions (the quantities that govern the transport processes of the flow), which are neatly captured in the averaged sense as different fractional flow paths in our proposed fractional flow graphs. Due to their simplicity, these new insights provide for a deeper understanding of flow phenomena and a broader capability to produce quantitative answers in response to what-if questions. Since these insights do not draw from any precedent in the prior literature, a science-oriented, comprehensive validation of our core analytical principles was performed. Model validation was performed against a diverse range of vapor-liquid, liquid-liquid, fluid-solid and vapor-liquid-liquid applications (over 74,000 experimental measurements from over 110 different labs and over 6,000 field measurements). Additionally, our analytical theory was benchmarked against other modeling methods and current industry codes with identical (unbiased), named published data. The validation and benchmarking results affirm the central finding of this research – that simple, suitably-averaged analytical models can yield an improved understanding and significantly better accuracy than that obtained with extremely complex, tunable models. It is proven that the numerous, continuously interacting (local) flow microphysics effects in a multiphase flow can be (implicitly) accounted for by just a few properly validated (global) closure models that capture their net (resultant) behavior. In essence, it is the claim of this research that there is an underlying simplicity and connectedness in this subject if looking at the resultant macroscopic…
Advisors/Committee Members: Sharma, Mukul M. (advisor), Bonnecaze, Roger T (committee member), Edgar, Thomas F (committee member), Rochelle, Gary T (committee member), Lake, Larry W (committee member).
Subjects/Keywords: Multiphase flow; Pipe flow; Closed conduits; Multiphase pipe flow; Analytical modeling; Averaged multiphase flow; Fractional flow; Pipe fractional flow; Pipe fractional flow theory; Mixture model; Nagoo-Sharma equations; Fluid mechanics; Single-phase pipe flow; Flow pressure gradients; Flow patterns; Volume fraction distributions; Fractional flow graphs
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APA (6th Edition):
Nagoo, A. S. (2014). Pipe fractional flow theory : principles and applications. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65197
Chicago Manual of Style (16th Edition):
Nagoo, Anand Subhash. “Pipe fractional flow theory : principles and applications.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/65197.
MLA Handbook (7th Edition):
Nagoo, Anand Subhash. “Pipe fractional flow theory : principles and applications.” 2014. Web. 24 Jan 2021.
Vancouver:
Nagoo AS. Pipe fractional flow theory : principles and applications. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/65197.
Council of Science Editors:
Nagoo AS. Pipe fractional flow theory : principles and applications. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/65197
26.
Zhang, Yang, 1980-.
Improved methods in statistical and first principles modeling for batch process control and monitoring.
Degree: PhD, Chemical Engineering, 2008, University of Texas – Austin
URL: http://hdl.handle.net/2152/17920
► This dissertation presents several methods for improving statistical and first principles modeling capabilities, with an emphasis on nonlinear, unsteady state batch processes. Batch process online…
(more)
▼ This dissertation presents several methods for improving statistical and first principles modeling capabilities, with an emphasis on nonlinear, unsteady state batch processes. Batch process online monitoring is chosen as a main research area here due to its importance from both theoretical and practical points of view. Theoretical background and recent developments of PCA/PLS-based online monitoring methodologies are reviewed, along with fault detection metrics, and algorithm variations for different applications. The available commercial softwares are also evaluated based on the corresponding application area. A detailed Multiway PCA based batch online monitoring procedure is used as the starting point for further improvements. The issue of dynamic batch profile synchronization is addressed. By converting synchronization into a dynamic optimization problem, Dynamic Time Warping (DTW) and Derivative DTW (DDTW) show the best performance by far. To deal with the singularity point and numerical derivative estimation problems of DTW and DDTW in the presence of noise, a robust DDTW algorithm is proposed by combining Savitzky-Golay filter and DDTW algorithm together. A comparative analysis of robust DDTW and available methods is performed on simulated and real chemical plant data. As traditional Multiway PCA-based (MPCA) methods consider batch monitoring in a static fashion (fail to consider time dependency between/within process variables with respect to time), an EWMA filtered Hybrid-wise unfolding MPCA (E-HMPCA) is proposed that considers batch dynamics in the model and reduce the number of Type I and II errors in online monitoring. Chemical and biochemical batch examples are used to compare the E-HMPCA algorithm with traditional methods. First principles modeling is known to be time consuming for development. In order to increase modeling efficiency, dynamic Design of Experiments (DOE) is introduced for Dynamic Algebraic Equation (DAE) system parameter estimation. A new criterion is proposed by combining PCA and parameter sensitivity analysis (P-optimal criterion). The new criterion under certain assumptions reduce to several available criteria and is suitable for designing experiments to improve estimation of specific parameter sets. Furthermore, the criterion systematically decomposes a complex system into small pieces according to PCA. Two engineering examples (one batch, one continuous) are used to illustrate the idea and algorithm.
Advisors/Committee Members: Edgar, Thomas F. (advisor).
Subjects/Keywords: Electronic data processing – Batch processing – Statistical methods; Electronic data processing – Batch processing – Mathematical models; Chemical engineering – Mathematical models; Algorithms
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APA (6th Edition):
Zhang, Yang, 1. (2008). Improved methods in statistical and first principles modeling for batch process control and monitoring. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/17920
Chicago Manual of Style (16th Edition):
Zhang, Yang, 1980-. “Improved methods in statistical and first principles modeling for batch process control and monitoring.” 2008. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/17920.
MLA Handbook (7th Edition):
Zhang, Yang, 1980-. “Improved methods in statistical and first principles modeling for batch process control and monitoring.” 2008. Web. 24 Jan 2021.
Vancouver:
Zhang, Yang 1. Improved methods in statistical and first principles modeling for batch process control and monitoring. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2008. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/17920.
Council of Science Editors:
Zhang, Yang 1. Improved methods in statistical and first principles modeling for batch process control and monitoring. [Doctoral Dissertation]. University of Texas – Austin; 2008. Available from: http://hdl.handle.net/2152/17920
27.
Kapoor, Kriti.
Modeling and optimization for energy efficient large scale cooling operation.
Degree: PhD, Chemical Engineering, 2013, University of Texas – Austin
URL: http://hdl.handle.net/2152/23160
► Optimal chiller loading (OCL) is described as a means to improve the energy efficiency of a chiller plant operation. It is formulated as a multi-period…
(more)
▼ Optimal chiller loading (OCL) is described as a means to improve the energy efficiency of a chiller plant operation. It is formulated as a multi-period constrained mixed integer non-linear optimization problem to optimize the total cooling load distribution through accurate chiller models. OCL is solved as a set of quadratic programs using sequential programming algorithm (SQP) in MATLAB. Based on application of the methodology to chiller systems at UT
Austin and a semiconductor manufacturing facility, OCL can result in an annual energy savings of about 8%. However, the savings may reduce considerably in case of additional physical constraints on overall plant operation. With the addition of thermal energy storage (TES) to the system, OCL can reduce the daily cooling costs in the case of time varying electricity prices by 13.45% on an average.
The energy efficiency of a chiller plant as a function of its chiller arrangement is studied by using fitted chiller models. If all other variables are kept same, chillers operating in parallel consume up to 9.62% less power as compared to when they are operated in series. Otherwise, chillers may operate up to 12.26% more efficiently in series depending on their chilled water outlet temperature values. The answer to the optimal chiller arrangement can be straightforward in some cases or can be a complex optimization problem in others.
Advisors/Committee Members: Edgar, Thomas F. (advisor).
Subjects/Keywords: Energy efficiency; Chiller plant; Cooling; Modeling; Optimization; Semiconductor; University campus; Thermal storage
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APA (6th Edition):
Kapoor, K. (2013). Modeling and optimization for energy efficient large scale cooling operation. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/23160
Chicago Manual of Style (16th Edition):
Kapoor, Kriti. “Modeling and optimization for energy efficient large scale cooling operation.” 2013. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/23160.
MLA Handbook (7th Edition):
Kapoor, Kriti. “Modeling and optimization for energy efficient large scale cooling operation.” 2013. Web. 24 Jan 2021.
Vancouver:
Kapoor K. Modeling and optimization for energy efficient large scale cooling operation. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2013. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/23160.
Council of Science Editors:
Kapoor K. Modeling and optimization for energy efficient large scale cooling operation. [Doctoral Dissertation]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/23160
28.
Sriprasad, Akshay.
Economic forecasting and optimization in a smart grid built environment.
Degree: MSin Engineering, Chemical Engineering, 2013, University of Texas – Austin
URL: http://hdl.handle.net/2152/22436
► This Master’s Report outlines graduate research work completed by Akshay Sriprasad, who is supervised by Professor Tom Edgar, in the area of modeling and systems…
(more)
▼ This Master’s Report outlines graduate research work completed by Akshay Sriprasad, who is supervised by Professor Tom
Edgar, in the area of modeling and systems optimization for the smart grid. The scope this report includes the development and validation of strategies to elicit demand response, defined as reduction of peak demand, at the residential level, in conjunction with collaborative research efforts from the Pecan Street Research Institute, a smart grid research consortium based in
Austin, TX. The first project outlined is an artificial neural network-‐based demand forecasting model, initially developed for UT’s campus cooling system and adapted for residential homes. Utilizing this forecasting model, a number of demand response-‐focused optimization studies are carried out, including optimization of community energy storage for peak shifting, and electric vehicle charging optimization to harness inexpensive night-‐time
Texas wind energy. Community energy storage and electric vehicles are chosen as ideal dynamic charging media due to increased proliferation and focus of Pecan Street Research Institute on critical emerging technologies. As these two technologies involve significant capital investment, an alternative mobile application-‐based demand response strategy is outlined to complete a comprehensive portfolio of demand response strategies to suit a variety of budgets and capabilities.
Advisors/Committee Members: Edgar, Thomas F. (advisor).
Subjects/Keywords: Energy; Smart grid; Optimization; Modeling; Demand response
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APA (6th Edition):
Sriprasad, A. (2013). Economic forecasting and optimization in a smart grid built environment. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22436
Chicago Manual of Style (16th Edition):
Sriprasad, Akshay. “Economic forecasting and optimization in a smart grid built environment.” 2013. Masters Thesis, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/22436.
MLA Handbook (7th Edition):
Sriprasad, Akshay. “Economic forecasting and optimization in a smart grid built environment.” 2013. Web. 24 Jan 2021.
Vancouver:
Sriprasad A. Economic forecasting and optimization in a smart grid built environment. [Internet] [Masters thesis]. University of Texas – Austin; 2013. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/22436.
Council of Science Editors:
Sriprasad A. Economic forecasting and optimization in a smart grid built environment. [Masters Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22436
29.
Hedengren, John David.
Real-time estimation and control of large-scale nonlinear DAE systems.
Degree: PhD, Chemical Engineering, 2005, University of Texas – Austin
URL: http://hdl.handle.net/2152/1563
Subjects/Keywords: Differential-algebraic equations; Control theory; Nonlinear theories
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Export
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APA (6th Edition):
Hedengren, J. D. (2005). Real-time estimation and control of large-scale nonlinear DAE systems. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/1563
Chicago Manual of Style (16th Edition):
Hedengren, John David. “Real-time estimation and control of large-scale nonlinear DAE systems.” 2005. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/1563.
MLA Handbook (7th Edition):
Hedengren, John David. “Real-time estimation and control of large-scale nonlinear DAE systems.” 2005. Web. 24 Jan 2021.
Vancouver:
Hedengren JD. Real-time estimation and control of large-scale nonlinear DAE systems. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2005. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/1563.
Council of Science Editors:
Hedengren JD. Real-time estimation and control of large-scale nonlinear DAE systems. [Doctoral Dissertation]. University of Texas – Austin; 2005. Available from: http://hdl.handle.net/2152/1563
30.
Lee, Hyung Joo, 1979-.
Advanced process control and optimal sampling in semiconductor manufacturing.
Degree: PhD, Chemical Engineering, 2008, University of Texas – Austin
URL: http://hdl.handle.net/2152/17929
► Semiconductor manufacturing is characterized by a dynamic, varying environment and the technology to produce integrated circuits is always shifting in response to the demand for…
(more)
▼ Semiconductor manufacturing is characterized by a dynamic, varying environment and the technology to produce integrated circuits is always shifting in response to the demand for faster and new products, and the time between the development of a new profitable method of manufacturing and its transfer to tangible production is very short. The semiconductor industry has adopted the use of advanced process control (APC), namely a set of automated methodologies to reach desired process goals in operating individual process steps. That is because the ultimate motivation for APC is improved device yield and a typical semiconductor manufacturing process can have several hundred unit processes, any of which could be a yield limiter if a given unit procedure is out of control. APC uses information about the materials to be processed, metrology data, and the desired output results to choose which model and control plan to employ. The current focus of APC for semiconductor manufacturers is run-to-run control. Many metrology applications have become key enablers for the conventionally labeled “value-added” processing steps in lithography and etch and are now integral parts of these processes. The economic advantage of effective metrology applications increases with the difficulty of the manufacturing process. Frequent measurement facilitates products reaching its target but it increases the cost and cycle time. If lots of measurements are skipped, the product quality does not be guaranteed due to process error from uncompensated drift and step disturbance. Thus, it is necessary to optimize the sampling plan in order to quickly identify the sources of prediction errors and decrease the metrology cost and cycle time. The goal of this research intend to understand the relationship between metrology and advanced process control (APC) in semiconductor manufacturing and develop an enhanced sampling strategy in order to maximize the value of metrology and control for critical wafer features.
Advisors/Committee Members: Edgar, Thomas F. (advisor).
Subjects/Keywords: Semiconductor wafers – Quality control; Semiconductor wafers – Measurement; Semiconductor industry – Production control; Process control
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APA (6th Edition):
Lee, Hyung Joo, 1. (2008). Advanced process control and optimal sampling in semiconductor manufacturing. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/17929
Chicago Manual of Style (16th Edition):
Lee, Hyung Joo, 1979-. “Advanced process control and optimal sampling in semiconductor manufacturing.” 2008. Doctoral Dissertation, University of Texas – Austin. Accessed January 24, 2021.
http://hdl.handle.net/2152/17929.
MLA Handbook (7th Edition):
Lee, Hyung Joo, 1979-. “Advanced process control and optimal sampling in semiconductor manufacturing.” 2008. Web. 24 Jan 2021.
Vancouver:
Lee, Hyung Joo 1. Advanced process control and optimal sampling in semiconductor manufacturing. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2008. [cited 2021 Jan 24].
Available from: http://hdl.handle.net/2152/17929.
Council of Science Editors:
Lee, Hyung Joo 1. Advanced process control and optimal sampling in semiconductor manufacturing. [Doctoral Dissertation]. University of Texas – Austin; 2008. Available from: http://hdl.handle.net/2152/17929
◁ [1] [2] [3] ▶
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Open Access Theses and Dissertations
