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Developing a Multiscale Modeling Approach Using Lumped Kinetics for Pyrolysis of Plastic Waste
No full textPlastic waste poses a global environmental problem and one day the State of Qatar needs to address it in line with its 2030 vision. The pyrolysis of plastic waste is a thermochemical conversion process that has recently gained considerable attention for its potential to convert plastic waste while generating valuable chemicals. Despite extensive research on plastic waste pyrolysis, there is a notable gap in the literature regarding the exploration of connections between different scales���experimental, kinetic, reactor, and process scales. This thesis aims to address this gap by establishing a multiscale approach, bridging the transition from laboratory-scale experiments to process models for plastic waste pyrolysis by utilizing lumped kinetic models, demonstrated for the cases of polyolefins.
A methodology is developed to link the scales, followed by the creation of a sequence chart representing all the necessary steps to complete each stage. This approach is explored through three case studies that vary either in lumped kinetics, plastic feedstock, or component selection. For each case, a lump kinetic model is integrated into 1D pseudo-homogeneous tubular reactor model, that is developed using MATLAB. Parametric studies were conducted for residence time (0-2 hours), wall temperature (650-950 K), and overall heat transfer coefficients (300, 500, and 1000 W/m����K) within the reactor. With the insights gained from these parametric studies, selected scenarios based on the minimum reactor energy and maximum plastic consumption were integrated into Aspen Plus to construct process models for determining mass and energy requirements. Additionally, the fourth case was subjected to techno-economic analysis, where the total annualized cost, profit, and circularity indicators were calculated and compared with other thermochemical plastic waste routes.
Through this research, the gaps associated with multi-scale modeling aspects in literature are aimed to be addressed, and a framework is sought to be established that seamlessly connects information from each scale. By achieving this goal, a holistic understanding of the entire plastic waste pyrolysis chain is aimed to be provided to decision-makers, empowering them to be informed about implementing plastic waste pyrolysis as an effective and sustainable solution for waste management. The results are within the range of other plastic waste management processes, showcasing the practical implementation and gap-bridging capabilities of the approach
Data Modeling, Computing, and Generation: New Techniques by and for AI
No full textThis dissertation investigates approaches in data handling within the domain of Artificial Intelligence (AI), covering data modeling, computing, and generation. It explores four primary tasks, each addressing distinct challenges and presenting novel solutions in their respective fields.
In the realm of data modeling, the Side Information Boosted Symbolic Regression (SIBSR) and Symbolic Modeling techniques are introduced. SIBSR incorporates side information into the symbolic regression process, enhancing the search for accurate mathematical relationships in complex datasets. Symbolic Modeling extends this approach to multi-dataset scenarios, particularly in financial asset pricing, providing adaptable and interpretable models that capture the dynamics of financial markets.
For data computing, the focus shifts to neuromorphic systems with the analysis of new Analog Error-Correcting Codes (ECCs) and the design of neural network-based decoders. These advancements address the challenges of reliability and accuracy in analog data processing, marking a progression of error-correcting from digital to analog and benefits in neuromorphic computing environments.
In data generation, Reinforcement Prompting, a novel methodology that leverages Large Language Models (LLMs) for the generation of synthetic data, is proposed. This approach mitigates issues of data privacy and scarcity of labeled datasets, especially in the finance domain. This method demonstrates that models trained on the generated synthetic data maintain performance integrity comparable to those trained on real financial data.
The dissertation presents a comprehensive exploration of these methods, substantiated by experimental evaluations and theoretical analysis. The research contributes to the advancement of AI in data handling, offering new perspectives and tools in data modeling, computing, and generation. The findings underscore the transformative potential of AI in understanding, processing, and generating data more effectively and ethically across various domains
Nitrogen-Containing Organic Bases for Molecular Electronic and Biological Investigations
No full textThis dissertation shows the efforts towards tailor-made nitrogen-containing organic base molecules for modular electronic studies as well as biological investigations. The first part features the development of two different kinds of aniline-derived conjugated molecules and their applications as molecular models for single-molecule junction studies. The second part of this dissertation describes the development of a series of polyamine-based detergents and their applications in native mass spectrometry studies of membrane proteins.
First, a general introduction of nitrogen-containing organic bases is presented in Chapter I. Example molecules and their applications including pharmaceuticals, metalorganic frameworks (MOFs) and conducting polymers, specifically polyaniline (PANI), are discussed.
In Chapter II, the design and synthesis of a ladder-type polyaniline-inspired single-molecule switch are presented. With exceptional electrochemical stability rendered by the ladder-type constitution, the molecule can be converted between three distinct molecular states characterized by varying levels of protonation and oxidation. In collaboration with the Schroeder Group at UIUC, we demonstrate its superior conductance and multi-state switching capabilities through electrochemical scanning tunneling microscope break-junction (STM-BJ) experiments. Our results suggest that ladder-type molecules are promising candidates for advanced single-molecule electronics. This work also sheds light on the mechanism of electronic conductivity of redox-active conductive polymers.
Chapter III describes the motivations of a model study involving oligo(para-phenylene)s within a graphene single-molecule junction for mechanistic studies of RCM in the synthesis of ladder polymers or oligomers. The design and synthetic efforts towards the target molecules are the main foci of this chapter.
Next, in Chapter IV, synthesis and purification towards spermine-derived detergents are described. These detergents are employed as additives/co-detergents in native mass spectrometry (MS) studies of various membrane proteins. Their effectiveness in achieving reduced average protein charge states and preserving membrane proteins in more intact states has been demonstrated. Our research in this area laid the groundwork for the molecular design principles of charge-reducing detergents with enhanced protein solubility and improved compatibility with commercial detergents. These principles are crucial for conducting native mass spectrometry studies of membrane protein complexes.
Lastly, Chapter V concludes the entire dissertation by providing an overview of the findings presented in the preceding chapters. Additionally, future research perspectives that promise to shed further light on both fields are discussed
Developing the Texas A&M Smart and Connected Homes Testbed
No full textThe Texas A&M Smart and Connected Homes Testbed has been developed to support residential HVAC research. The flexible testbed enables the windows and walls to be replaced, the floorplan to be reconfigured, provides two separate duct networks, and incorporates on-site renewable energy. Heavy instrumentation is done at the testbed to capture information on local weather conditions, building envelope performance, occupant comfort, HVAC equipment performance, and the power consumption of all household end-uses. A smart thermostat is also incorporated to provide HVAC control capabilities. Occupants are simulated inside the home to mimic actual operation and internal loads. A Modelica model has been created for the building envelope and split system HVAC at the testbed. Using data from the experimental testbed, the model is tuned to ensure accurate implementation. Researchers can use these models to bridge the gap between simulation-based studies and their real-world application
Essays on the Causal Effects of Retailer Strategic Actions on Sales, Omnichannel Shopping, and Mobile App Engagement
No full textRetailers engage in strategic actions such as store closure and the introduction of new features in mobile apps. Each action has the potential to change shoppers��� omnichannel shopping behavior and engagement. Empirical analysis of the causal effects of these strategic actions is challenging because field experiments are often expensive and infeasible. In the two essays of my dissertation, I focus on the strategic actions of a large U.S. retailer of video games and consumer electronics and analyze the causal effects of these actions. I use the difference-in-differences (DID) framework, controlling for potential endogeneity, to causally estimate the effects of the strategic actions. I apply machine learning algorithms to explore treatment effect heterogeneity and analyze unstructured app clickstream data.
In Essay 1, I study the impact of store closure on the retail chain���s aggregate sales, customers��� omnichannel shopping, and mobile app usage. The results show that store closures led to a significant loss of $209,317 in net monthly sales per county, surpassing the average sales of the closed stores. Both offline and online sales dropped after the closure of a store. These results highlight that retailers should re-examine their closure plans and account for the negative spillover effect.
Essay 2 examines the causal effect of in-app payment introduction on omnichannel shopping and mobile app usage behavior, uncovers individual-level treatment effect heterogeneity, and investigates the underlying mechanisms. I find that adopting in-app payment significantly boosts overall purchases. As a result, overall spending net of returns is 25.3% (33.6%) higher for Apple Pay (PayPal) adopters than nonadopters. Mechanisms driving these results include increased spending both offline and online, reduced friction, lower mobile cart abandonment rate, increased Buy-Online-and-Pickup-In-Store (BOPIS) orders, heightened app usage near stores, greater store visits due to increased product returns and trade-ins, and more impulse purchases
Thermal Lethality Validation for Human Pathogenic Salmonella enterica on Chicken Feathers and Blood During Simulated Low-Temperature Rendering
No full textPoultry carcass offal rendering is the process in which poultry inedible and carcass waste materials are converted into high-quality protein meals such as poultry, feather, and blood meal, and other commercial byproducts through physical and chemical transformation using sustained heat application, moisture extraction, and fat separation. Nevertheless, the presence of pathogens in animal feed that have been linked to ingredients obtained from rendered products has raised concerns over the process���s capability to reduce food safety hazards to acceptable levels that might be transmitted to animal feeds. This study was conducted to validate the inactivation, and determine the death kinetics, of a thermotolerant human pathogenic Salmonella enterica in chicken feathers and blood during simulated low-temperature dry rendering conditions. Chicken feathers and blood were inoculated with Salmonella enterica serovar Senftenberg 775W and heated to 60, 70, or 80 ��C for 60, 20, and 5 min, respectively. Complete block design experiments were conducted and three samples for each product were processed at each time point and replicated three times (N=3). After thermal treatment, samples were serially diluted and selectively enumerated after incubating for 24 h at 37��C. Experimental data were log-transformed and the Geeraerd non-linear inactivation model was used for curve and data fitting with the Microsoft Excel Add-In software toolkit GInaFiT. The data analysis showed D-values and observed shoulder periods decreased with increase in processing temperature for both feathers and blood. D-values were 2.23��0.045, 0.66��0.135, and 0.29��0.0225 min for chicken feathers and 2.22��0.2, 0.46��0.0175, and 0.27��0.05 min for chicken blood at 60,70, and 80 ��C respectively. Secondary model analysis showed that the maximum inactivation rate was positively correlated with the processing temperature. Model predicted Kmax values calculated per minute were 1.05��0.07, 3.63��0.20, and 8.37��0.67 for chicken feathers, and 1.06��0.10, 5.06��0.20, and 8.53��1.10 for chicken blood at 60, 70, and 80 oC respectively. Study findings validated ability of low-temperature rendering conditions to significantly reduce Salmonella in poultry rendered product to below detectable values. They also provide rendering industry a supporting tool for food safety validation and food safety regulatory standards compliance
Characterizing Effects That Influence Measured Range Hood Capture Efficiency
No full textKitchen range hoods remove harmful contaminants released by cooking, and they are essential for maintaining healthy indoor air quality. Standardized range hood capture efficiency (RHCE) experiments and tests were performed, and the resulting data were used to calculate RHCE, which is a parameter that provides a measure of how contaminants are removed. The goal of the studies reported herein is to improve the repeatability and reproducibility of RHCE tests performed in accordance with ASTM E3087.18. Changes to the standard aligning with the results of these reported studies will contribute to a reduction in error between measurements made at different testing laboratories, as well as a reduction in test variability. Achieving these goals is an essential step in ensuring further widespread adoption of the testing standard and RHCE as a metric, which in turn will lead to improved indoor air quality and human health, along with less energy consumed and greenhouse gas released.
A detailed experimental study was performed to analyze the effect of tracer gas injection rate, emitter assembly surface temperature, and chamber volume on measured RHCE. One study herein found that when the chamber volume was reduced from 36.6 m3 to 21.0m3 then a sample produced average results 3.6%CE lower at its higher operating speed and 7.8%CE lower at its lower operating speed. In contrast, in another study herein, a more extreme volume reduction led to a decrease in RHCE for another sample���s high-speed setting and an increase on its low-speed setting, suggesting that while chamber volume change has a definite effect on measured RHCE, this effect is not always consistent between samples or operating conditions.
Two analytical models were derived to predict chamber and exhaust concentrations during an RHCE test. The second model, which is the more useful model, assumes that there are two bodies of chamber air with limited interaction. It was validated by comparing its predicted results against experimentally measured ones. While the model predicted chamber CO2 concentration within 1.8% for a high-RHCE case, its accuracy was just 14.8% for a low-RHCE case. Further analysis found the model to have a consistent decrease in accuracy with decreasing nominal RHCE. The models can be used to predict performance and trends if one keeps in mind the limitations of the model with regards to its accuracy in specific flow ranges.
Another experimental study was performed herein to characterize the distribution of tracer gas throughout the test chamber. Results of this study confirmed that the concentration of tracer gas decreases as distance from the emitters increases, but that there is also a local zone of relatively low tracer gas concentration along the centerline of the room.
A reference range hood frame without a blower was constructed and tested for comparison against conventional range hoods. This test series aimed to determine whether the fan blower and motor selection impacts low-CE results. Using the exhaust as a prime mover, the reference box achieved capture efficiencies of 96.5% at 250 CFM, 93.3% at 160 CFM, and 81.3% at 100 CFM, outperforming other range hoods while showing lower test variance, with the result that fan and motor installation have a negative effect on RHCE. While design refinement is needed to better satisfy the initial goal of replicating low-RHCE results, the box���s low variance between tests suggests only minor modification of the design should be needed to do so.
A chamber inlet modification was performed and validated to ensure it did not severely impact the quality of measured RHCE. Three units were tested before and after installing the inlet modification. The results show that the inlet modification slightly reduced variance without noticeably affecting mean RHCE in four of five tested cases, but in one case it led to an increase in mean RHCE from 75.6%CE to 87.2%CE accompanied by an increase in variance from 0.94%CE to 3.06%CE. The results of this study found that the inlet change does not noticeably interfere with RHCE measurements
The Hepatic Biotransformation Capability and Occurrence of Emerging Contaminants in American Alligators (Alligator Mississippiensis)
No full textRecently, emerging contaminants (ECs) have been drawing more attention due to health concerns in exposed organisms. Pharmaceuticals and per- and polyfluorinated substances (PFAS) are representative ECs that are ubiquitous and persistent in aquatic environments. American alligators (Alligator mississippiensis) are often considered a sentinel species in coastal aquatic ecosystems along the Gulf of Mexico as they are susceptible to the bioaccumulation of contaminants due to their high trophic position and longevity. However, the biotransformation capability of alligators for ECs and EC exposure impact in wild alligators are yet to be understood. Therefore, this study aimed to address these knowledge gaps.
For the evaluation of biotransformation capability, a novel in situ liver perfusion system was developed using juvenile alligators. The operativity of perfused livers was tested with normoxic and hypoxic treatments. Under normoxia, the aspartate transferase (AST) and lactate/pyruvate ratio in effluent perfusate remained stable for 6 hours whereas hypoxia significantly increased the lactate/pyruvate ratio after 2 hours. The elevation of lactate suggests the induction of anaerobic metabolism indicating the viability of the organ. With the establishment of operable perfused livers, alligator biotransformation capability for carbamazepine (CBZ) and nicotine (NCT) was investigated by measuring the formation of their primary metabolites. Additionally, the in vitro S9 assay was performed to compare its metabolic potential with perfused livers. For CBZ, perfused livers exhibited only 30% intrinsic formation clearance (CLf,int) relative to the S9 assay. The NCT metabolism was only observed in perfused livers. Compared to the corresponding rat models (S9 or perfused livers), alligators��� CLf,int was 20-60% for CBZ and 50% for NCT of rats. Lastly, wild alligator plasma was analyzed for select ECs and biochemistry parameters. The detected ECs included amphetamine, atenolol, ketoprofen, naproxen, nicotine, and perfluorooctane sulfonic acid. Statistical analyses showed positive correlations between the plasma EC levels and biochemistry indicating potential physiological stress associated with organ injury and endocrine disruption. While this study provides invaluable insight into the metabolic capability and vulnerability to EC exposure in alligators, the methods developed in this study can also serve as an effective toolbox for future studies to protect this ecologically important species
"Trust to Us, the Union Men of the South": The 1866 Southern Loyalist Convention and the Fight for Reconstruction
No full textIn 1866, unconditional Unionists of the South, who had remained loyal to the United States during its civil war, convened in Philadelphia to express their displeasure with President Andrew Johnson. In their view, Johnson���s willful inaction enabled traitors rather than Unionists to continue dominating Southern politics in the aftermath of the war. If the Union defeated the Confederacy but its supporters remained in power, Unionists��� staunch devotion to the Union was rendered pointless. The 1866 Southern Loyalist Convention was the culmination of years of political struggles. Scholarly literature on Southern Unionists is extensive, yet historians have overlooked the significance of this convention. This thesis argues that the convention utilized a narrative of suffering that Southern Unionists employed consistently throughout the Civil War and Reconstruction. In various pamphlets published early in the crisis, Southern Unionists voiced their disapproval of secession and had articulated their perspectives on national affairs. By concentrating those disparate voices in a single location, the Southern Loyalist Convention amplified long standing whispers of Unionist dissatisfaction. Collectively, these broadsheets pointed to deep divisions among Unionists, generally involving disagreements about African American enfranchisement. Despite the internal divisions that ultimately prevented them from forming a powerful Unionist coalition, Southern Unionists were consistent in a few key ways. They insisted upon their undying support for the Union, demanded the implementation of their agenda, and constructed a powerful narrative of their own suffering. Their mutual misery, in fact, became the foundation upon which Unionists built relationships with each other at this convention. This thesis considers how the 1866 Convention contributed to a post-war narrative that historians have labeled the ���Won Cause��� memory of the war
Optimizing Infectious Disease Control: Strategies for Social Separation and Vaccine Allocation with Equity Consideration
No full textAs global threats from infectious diseases intensify, as highlighted by the COVID-19 pandemic, the urgency to enhance control measures becomes evident. Strategies such as social separation and vaccine allocation are pivotal in disease management. However, despite extensive research, many models still fall short. They rely on oversimplified assumptions such as population homogeneity and a single-wave pattern of infection spread. Current research still lacks comprehensive approaches in addressing the effective and equitable implementation of these strategies in more complex, realistic scenarios.
To address these gaps, we explore various models for social separation and vaccine distribution, considering individual-specific factors and the multi-wave nature of pandemics observed in reality. Focusing on efficiency and equity in disease mitigation, we uncover key components of an optimal infectious disease control strategy. These insights help us to formulate effective algorithms, understanding the trade-offs between efficacy, costs, and fairness. Our work also leads to the development of an efficient, fair clustering algorithm that not only performs well in our disease mitigation context but also excels across various datasets.
Case studies underscore the advantages of strategies tailored to specific individual information and dynamic behavior. Such approaches are more effective, cost-efficient, and equitable than traditional disease control measures. The fair clustering algorithm we developed further demonstrates its advantages over benchmark algorithms