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- Genre: Academic theses
Description
Adsorption equilibrium is an important metric used to assess adsorbent performance for gas mixture separation processes. Gas adsorption processes such as carbon capture are becoming more urgent as climate change and global warming accelerate. To speed up and reduce the cost of research on adsorbent materials and adsorption processes, I developed an open-source Python code that generates mixed gas adsorption equilibrium data using pure gas adsorption isotherms based on the ideal adsorbed solution theory (IAST). The major efforts of this M.S. research were placed on adding additional components to the mixture models since most other publications focused on binary gas mixtures. Generated mixed-gas equilibrium data were compared to experimentally collected data in order to validate the multicomponent IAST model and to determine the accuracy of the computer codes developed in this work. Additional mixed-gas equilibrium data were then generated and analyzed for trends in the data for humid flue gas conditions, natural gas processing conditions, and hydrogen gas purification conditions. For humid flue gas conditions, neither the analyzed Mg-MOF-74 nor the Zeolite 13X were shown to be suitable for use. For natural gas processing conditions, the Zeolite 13X was determined to be a much better candidate for use than the MIL-101. For hydrogen gas purification conditions, the Zeolite 5A was determined to be a better adsorbent for use than CD-AC due to the Zeolite 5A’s much lower adsorption of H2.
ContributorsCiha, Trevor (Author) / Deng, Shuguang (Thesis advisor) / Machas, Michael (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2022
Idiographic Models of Walking Behavior for Personalized mHealth Interventions: Some Novel Approaches
Description
This thesis presents the development of idiographic models (i.e., single subject or N = 1) of walking behavior as a means of facilitating the design of control systems to optimize mobile health (mHealth) interventions for sedentary adults. Model-on-Demand (MoD), an adaptive modeling technique, is demonstrated as an ideal method for modeling nonlinear systems with noise on a simulated continuously stirred tank reactor (CSTR). Comparing MoD to AutoRegressive with eXogenous input (ARX) estimation, MoD outperforms ARX in terms of addressing both nonlinearity and noise in the CSTR system. With the CSTR system as an initial proof of concept, MoD is then used to model individual walking behavior using intervention data from participants of HeartSteps, a walking intervention that studies the effect of within-day suggestions. Given the number of possible measured features from which to design the MoD models, as well as the number of model parameters that influence the model’s performance, optimizing MoD models through exhaustive search is infeasible.
Consequently, a discrete implementation of simultaneous perturbation stochastic approximation (DSPSA) is shown to be an efficient algorithm to find optimal models of walking behavior. Combining MoD with DSPSA, models of walking behavior were developed using participant data from Just Walk, a day-to-day walking intervention; MoD outperformed ARX models on both estimation and validation data. DSPSA was also applied to ARX modeling, highlighting the use of DSPSA to not only search over model parameters and features but also data partitioning, as DSPSA was used to evaluate models under various combinations of estimation and validation data from a single participant’s walking data. Results of this thesis point to ARX with DSPSA as a routine means for dynamic model estimation in large-scale behavioral intervention settings.
ContributorsKha, Rachael T (Author) / Rivera, Daniel E (Thesis advisor) / Deng, Shuguang (Committee member) / Muhich, Christopher (Committee member) / Arizona State University (Publisher)
Created2022
Description
Polypropylene, a non-biodegradable plastic with a higher c-c bond disassociation energy than other conventional polymers like Polyethylene (PE), is used to manufacture these three-layered masks. The amount of plastic pollution in the environment has grown tremendously, nearing million tons in a short period of time. As a result, the purpose of this study is to reduce the environmental damage caused by facemasks. This M.S. thesis offers a concise overview of various thermochemical methods employed to depolymerize plastic waste materials. It emphasizes environmentally conscious and sustainable practices, specifically focusing on solvothermal processing. This innovative approach aims to convert discarded face masks into valuable resources, including hydrocarbons suitable for jet fuel and other useful products.
The thesis provides an in-depth exploration of experimental investigations into solvothermal liquefaction techniques. Operating under specific conditions, namely, a temperature of 350°C and a reaction duration of 90 minutes, the results were notably impressive. These results included an exceptional conversion rate of 99.8%, an oil yield of 39.3%, and higher heating values (HHV) of 46.81 MJ/kg for the generated oil samples. It's worth noting that the HHV of the oil samples obtained through the solvothermal liquefaction (STL) method, at 46.82 MJ/kg, surpasses the HHV of gasoline, which stands at 43.4 MJ/kg.
The significant role of the solvent in the depolymerization process involves the dissolution and dispersion of the feedstock through solvation. This reduces the required thermal cracking temperature by enhancing mass and thermal energy transfer. While solvolysis reactions between the solvent and feedstock are limited in thermal liquefaction, the primary depolymerization process follows thermal cracking. This involves the random scission of polypropylene (PP) molecules during heat treatment, with minimal polymerization, cyclization, and radical recombination reactions occurring through free radical mechanisms.
Overall, this work demonstrates the feasibility of a highly promising technique for the effective chemical upcycling of polypropylene-based plastics into valuable resources, particularly in the context of jet fuel hydrocarbons, showcasing the comprehensive analytical methods employed to characterize the products of this innovative process.
ContributorsAkula, kapil Chandra (Author) / Deng, Shuguang (Thesis advisor) / Fini, Elham (Committee member) / Salifu, Emmanuel (Committee member) / Arizona State University (Publisher)
Created2023
Description
The objective of this research is to create a python program that can describe the adsorption breakthrough performance of direct air capture of CO2 by zeolite and other adsorbents. The purpose of creating this open-source code is because many commercial simulation software for adsorption process simulation can be extremely expensive and typically are yearly subscriptions which can be a costly expenditure for academic research labs and chemical engineers working on adsorption processes development and design. The simulation models are generated by solving the governing mass and energy transfer equations and validating the models with experimental data. The typical inputs for the adsorption process simulation include adsorption equilibrium of both CO2 and N2 on selected adsorbents, mass transfer coefficients information, adsorbent bed length and void fraction, and other physical and chemical properties of the adsorbent being tested. The outputs of the simulation package are the dimensionless CO2 concentration profile as a function of dimensionless time, which is usually used for evaluating the adsorbent performance for CO2 capture. The models created were compared to the commercial package gPROMs and they performed extremely well. The main variation between the models created and gPROMs was that the models tended to underpredict the breakpoint of experimental data and gPROMs tended to overpredict. This M.S. research is part of the major research efforts for developing an open-source adsorption process simulation package for carbon capture and conversion in Prof. Deng’s group at ASU. The ultimate goal of this research program is to reduce carbon emissions and develop a sustainable solution for a future carbon-free economy.
ContributorsBonelli, Xavier Berlage (Author) / Deng, Shuguang (Thesis advisor) / Andino, Jean (Committee member) / Seo, Don (Committee member) / Arizona State University (Publisher)
Created2022
Description
The objective of this research was to develop Aluminophosphate-five (AlPO4-5, AFI) zeolite adsorbents for efficient oxygen removal from a process stream to support an on-going Department of Energy (DOE) project on solar energy storage. A molecular simulation study predicted that substituted AlPO4-5 zeolite can adsorb O2 through a weak chemical bond at ambient temperature. Substituted AlPO4-5 zeolite was successfully synthesized via hydrothermal crystallization by following carefully designed procedures to tailor the zeolite for efficient O2 adsorption. Synthesized AlPO4-5 in this work included Sn/AlPO-5, Mo/AlPO-5, Pd/AlPO-5, Si/AlPO-5, Mn/AlPO-5, Ce/AlPO-5, Fe/AlPO-5, CuCe/AlPO-5, and MnSnSi/AlPO-5. While not all zeolite samples synthesized were fully characterized, selected zeolite samples were characterized by powder x-ray diffraction (XRD) for crystal structure confirmation and phase identification, and nitrogen adsorption for their pore textural properties. The Brunauer-Emmett-Teller (BET) specific surface area and pore size distribution were between 172 m2 /g - 306 m2 /g and 6Å - 9Å, respectively, for most of the zeolites synthesized. Samples of great interest to this project such as Sn/AlPO-5, Mo/AlPO-5 and MnSnSi/AlPO-5 were also characterized using x-ray photoelectron spectroscopy (XPS) and energy-dispersive x-ray spectroscopy (EDS) for elemental analysis, scanning electron microscopy (SEM) for morphology and particle size estimation, and electron paramagnetic resonance (EPR) for nature of adsorbed oxygen. Oxygen and nitrogen adsorption experiments were carried out in a 3-Flex adsorption apparatus (Micrometrics) at various temperatures (primarily at 25℃) to determine the adsorption properties of these zeolite samples as potential adsorbents for oxygen/nitrogen separation. Experiments showed that some of the zeolite samples adsorb little-to-no oxygen and nitrogen at 25℃, while other zeolites such as Sn/AlPO-5, Mo/AlPO-5, and MnSnSi/AlPO-5 adsorb decent but inconsistent amounts of oxygen with the highest observed values of about 0.47 mmol/ g, 0.56 mmol/g, and 0.84 mmol/ g respectively. The inconsistency in adsorption is currently attributed to non-uniform doping of the zeolites, and these findings validate that some substituted AlPO4-5 zeolites are promising adsorbents. However, more investigations are needed to verify the causes of this inconsistency to develop a successful AlPO4-5 zeolite-based adsorbent for oxygen/nitrogen separation.
ContributorsBuyinza, Allan Smith (Author) / Deng, Shuguang (Thesis advisor) / Varman, Arul M (Committee member) / Jin, Kailong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Physical inactivity is a major contributor to chronic illnesses and mortality globally. However, most interventions to address it rely on static, aggregate models that overlook idiographic (i.e., individual-level) dynamics, limiting intervention effectiveness. Leveraging mobile technology and control systems engineering principles, this dissertation provides a novel, comprehensive framework for personalized behavioral interventions that have been tested experimentally under the Control Optimization Trial (COT) paradigm. Through careful design of experiments, elaborate signal processing and model estimation, and judicious formulation of behavior intervention optimization as a control system problem, this dissertation develops tools to overcome challenges faced in the large-scale dissemination of mobile health (mHealth) interventions. A novel Three-Degrees-of-Freedom Kalman Filter-based Hybrid Model Predictive Control (3DoF-KF HMPC) controller is formulated for physical activity interventions and evaluated in a clinical trial, demonstrating its effectiveness. Furthermore, this dissertation expands on understanding the underlying dynamics influencing behavior change. Engineering principles are applied to develop a conceptual approach to generate dynamic hypotheses and translate these into first-principle dynamic models. The generated models are used in concert with system identification principles to enhance the design of experiments that yield dynamically informative data sets for behavioral medicine applications. Additionally, sophisticated search, filtering, and model estimation algorithms are applied to optimize and personalize model structures and estimate dynamic models that account for nonlinearities and “Just-in-Time” (JIT; moments of need, receptivity, and opportunity) context in behavior change systems. In addition, the pervasive issue of data missingness in interventions is addressed by integrating system identification principles with a Bayesian inference model-based technique for data imputation. The findings in this dissertation extend beyond physical activity, offering insights for promoting healthy behaviors in other applications, such as smoking cessation and weight management. The integration of control systems engineering in behavioral medicine research, as demonstrated in this dissertation, offers broad impacts by advancing the field's understanding of behavior change dynamics, enhancing accessibility to personalized behavioral health interventions, and improving patient outcomes. This research has the potential to radically improve behavioral interventions, increase affordability and accessibility, inspire interdisciplinary collaboration, and provide behavioral scientists with tools capable of addressing societal challenges in mHealth and preventive medicine.
ContributorsEl Mistiri, Mohamed (Author) / Rivera, Daniel E. (Thesis advisor) / Deng, Shuguang (Committee member) / Muhich, Christopher (Committee member) / Pavlic, Theodore P. (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2024
Description
Adsorption is fundamentally known to be a non-isothermal process; in which temperature increase is largely significant, causing fairly appreciable impacts on the processkinetics. For porous adsorbent particles like metal organic frameworks (MOFs), silica
gel, and zeolite, the resultant relative heat generated is partly distributed within the
particle, and the rest is transferred to the surrounding ambient fluid (air).
For large step changes in adsorbed phase concentration and fast adsorption rates,
especially, the isothermality of adsorption (as in some studies) is an inadequate assumption and inspires rather erroneous diffusivities of porous adsorbents. Isothermal models, in consequence, are insufficient for studying adsorption in porous adsorbents. Non-isothermal models can satisfactorily and exhaustively describe adsorption
in porous adsorbents. However, in many of the analyses done using the models, the
thermal conductivity of the adsorbent is assumed to be infinite; thus, particle temperature is taken to be fairly uniform during the process—a trend not observed for
carbon dioxide (CO2) adsorption on MOFs.
A new and detailed analysis of CO2 adsorption in a single microporous MOF-5
particle, assuming a finite effective thermal conductivity along with comprehensive
parametric studies for the models, is presented herein. A significant average temperature increase of 5K was calculated using the new model, compared to the 0.7K
obtained using the Stremming model. A corresponding increase in diffusivity from
8.17 x 10-13 to 1.72 x 10-11 m2/s was observed, indicating the limitations of both
isothermal models and models that assume constant diffusivity.
ContributorsNkuutu, John (Author) / Lin, Jerry (Thesis advisor) / Emady, Heather (Committee member) / Deng, Shuguang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Anthropogenic processes have increased the concentration of toxic Se, As and N in water. Oxo-anions of these species are poisonous to aquatic and terrestrial life. Current remediation techniques have low selectivity towards their removal. Understanding the chemistry and physics which control oxo-anion adsorption on metal oxide and the catalytic nitrate reduction to inform improved remediation technologies can be done using Density functional theory (DFT) calculations. The adsorption of selenate, selenite, and arsenate was investigated on the alumina and hematite to inform sorbent design strategies. Adsorption energies were calculated as a function of surface structure, composition, binding motif, and pH within a hybrid implicit-explicit solvation strategy. Correlations between surface property descriptors including water network structure, cationic species identity, and facet and the adsorption energies of the ions show that the surface water network controls the adsorption energy more than any other, including the cationic species of the metal-oxide. Additionally, to achieve selectivity for selenate over sulphate, differences in their electronic structure must be exploited, for example by the reduction of selenate to selenite by Ti3+ cations.
Thermochemical or electrochemical reduction pathways to convert NO3- to N2 or NH3, which are benign or value-added products, respectively are examined over single-atom electrocatalysts (SAC) in Cu. The activity and selectivity for nitrate reduction are compared with the competitive hydrogen evolution reaction (HER). Cu suppresses HER but produces toxic NO2- because of a high activation barrier for cleaving the second N-O bond. SACs provide secondary sites for reaction and break traditional linear scaling relationships. Ru-SACs selectively produce NH3 because N-O bond scission is facile, and the resulting N remains isolated on SAC sites; reacting with H+ from solvating H2O to form ammonia. Conversely, Pd-SAC forms N2 because the reduced N* atoms migrate to the Cu surface, which has a low H availability, allowing N atoms to combine to N2. This relation between N* binding preference and reduction product is demonstrated across an array of SAC elements.
Hence, the solvation effects on the surface critically alter the activity of adsorption and catalysis and the removal of toxic pollutants can be improved by altering the surface water network.
ContributorsGupta, Srishti (Author) / Muhich, Christopher L (Thesis advisor) / Singh, Arunima (Committee member) / Emady, Heather (Committee member) / Westerhoff, Paul (Committee member) / Deng, Shuguang (Committee member) / Arizona State University (Publisher)
Created2023
Description
This study presents an evaluation of the predicted flow behavior and the minimum outlet diameter in a computationally simulated hopper. The flow pattern in hoppers was simulated to test three size fractions, three moisture levels of microcrystalline cellulose (MCC), and two hopper wall angles in Multiphase Flow with Interphase eXchanges (MFiX). Predictions from MFiX were then compared to current literature. As expected, the smaller size fractions with lower water content were closer to ideal funnel flow than their larger counterparts. The predicted minimum outlet diameter in simulations showed good agreement with close to ideal flowability. These findings illustrate the connection between lab flowability experiments and computational simulations. Lastly, three fluidized bed simulations were also created in MFiX with zeolite 13X to analyze the pressure and velocity within the bed. The application of flowability simulations can improve the transport of solids in processing equipment used during the production of powders.
ContributorsBuchanan, Lidija (Author) / Emady, Heather (Thesis advisor) / Muhich, Christopher (Committee member) / Deng, Shuguang (Committee member) / Arizona State University (Publisher)
Created2023
Description
Cyclical chemical looping involves the thermal reduction of metal oxide to release O2 at high temperatures, followed by its oxidation using O-containing molecules like O2, H2O, or CO2. This process is a promising method for solar thermochemical water splitting (STCH), oxygen separation, and thermochemical energy storage (TCES). The efficiency and economic viability of this process hinge on the thermodynamics of metal oxide reduction. This dissertation presents innovative methods to enhance the performance of these processes, with a specific focus on STCH and TCES by advancing thermodynamic characterization and screening of potential metal oxides, thereby reducing H2 costs.A novel CALPHAD approach, the CrossFit Compound Energy Formalism (CEF), integrates theoretical (density functional theory) and experimental (thermogravimetric) data to develop thermodynamic models for desired materials. The CrossFit-CEF was applied to BaxSr1-xFeO3-δ identifying matching thermodynamics and off-stoichiometric values to literature (~100-180 kJ/mol O2 reduction enthalpies across the BaxSr1-xFeO3-δ compositional range). Comparisons with the traditional van ‘t Hoff thermodynamic extraction technique reveal that the CrossFit-CEF method significantly outperforms conventional methods. For instance, the CEF method was employed to extract thermodynamic data for CaFexMn1-xO3-δ and identify optimal TCES CaFexMn1-xO3-δ compositions. The CrossFit-CEF method found the same thermodynamic trends on less than half the data utilized in a van ‘t Hoff approach and determined that the optimal CaFexMn1-xO3-δ composition had the minimal Fe concentration synthesized (x=0.625), achieving ~60 kJ/mol TCES.
Bayesian Inference was employed was employed to expedite data collection. When combined with the CrossFit-CEF method, Bayesian Inference assesses the likelihood that the current model accurately describes the data, providing confidence intervals for the model. This approach reduces the amount of data needed for accurate thermodynamic modeling by 50%.
Finally, the CrossFit-CEF and Bayesian methods are integrated into a system-level STCH model to optimize and accelerate materials design for specific plant operating conditions. Overall, this dissertation introduces methods that yield more accurate thermodynamic models with reduced data requirements. The time saved in data collection enables screening of more materials, expediting material identification and optimization. The materials identified through these techniques are expected to enhance chemical looping cycles, leading to increased H2 production efficiency and reduced costs.
ContributorsWilson, Steven A (Author) / Muhich, Christopher L (Thesis advisor) / Rivera, Daniel E (Committee member) / Stechel, Ellen B (Committee member) / Lin, Jerry (Committee member) / Deng, Shuguang (Committee member) / Arizona State University (Publisher)
Created2024