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- All Subjects: engineering
- All Subjects: wastewater
- Creators: Liu, Yongming
- Member of: Theses and Dissertations
- Resource Type: Text
- Status: Published
Description
Microbial fuel cells (MFCs) promote the sustainable conversion of organic matter in black water to electrical current, enabling the production of hydrogen peroxide (H2O2) while making waste water treatment energy neutral or positive. H2O2 is useful in remote locations such as U.S. military forward operating bases (FOBs) for on-site tertiary water treatment or as a medical disinfectant, among many other uses. Various carbon-based catalysts and binders for use at the cathode of a an MFC for H2O2 production are explored using linear sweep voltammetry (LSV) and rotating ring-disk electrode (RRDE) techniques. The oxygen reduction reaction (ORR) at the cathode has slow kinetics at conditions present in the MFC, making it important to find a catalyst type and loading which promote a 2e- (rather than 4e-) reaction to maximize H2O2 formation. Using LSV methods, I compared the cathodic overpotentials associated with graphite and Vulcan carbon catalysts as well as Nafion and AS-4 binders. Vulcan carbon catalyst with Nafion binder produced the lowest overpotentials of any binder/catalyst combinations. Additionally, I determined that pH control may be required at the cathode due to large potential losses caused by hydroxide (OH-) concentration gradients. Furthermore, RRDE tests indicate that Vulcan carbon catalyst with a Nafion binder has a higher H2O2 production efficiency at lower catalyst loadings, but the trade-off is a greater potential loss due to higher activation energy. Therefore, an intermediate catalyst loading of 0.5 mg/cm2 Vulcan carbon with Nafion binder is recommended for the final MFC design. The chosen catalyst, binder, and loading will maximize H2O2 production, optimize MFC performance, and minimize the need for additional energy input into the system.
ContributorsStadie, Mikaela Johanna (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
In our modern world the source of for many chemicals is to acquire and refine oil. This process is becoming an expensive to the environment and to human health. Alternative processes for acquiring the final product have been developed but still need work. One product that is valuable is butanol. The normal process for butanol production is very intensive but there is a method to produce butanol from bacteria. This process is better because it is more environmentally safe than using oil. One problem however is that when the bacteria produce too much butanol it reaches the toxicity limit and stops the production of butanol. In order to keep butanol from reaching the toxicity limit an adsorbent is used to remove the butanol without harming the bacteria. The adsorbent is a mesoporous carbon powder that allows the butanol to be adsorbed on it. This thesis explores different designs for a magnetic separation process to extract the carbon powder from the culture.
ContributorsChabra, Rohin (Author) / Nielsen, David (Thesis director) / Torres, Cesar (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05
Description
Optimizing cathodes for microbial fuel cells is important to maximize energy harvested from wastewater. Cathodes were made by modifying a recipe from previous literature and testing the current of the cathode using linear sweep voltammetry. The cathodes contained an Fe-N-C catalyst combined with a Polytetrafluoroethylene binder. Optimizing the power resulting from the microbial fuel cells will help MFCs be an alternative energy source to fossil fuels. The new cathodes did improve in current production from −16 𝐴/𝑚 to −37 𝐴/𝑚 at -0.4 V. When fitted using a Butler-Volmer model, the cathode linear-sweep voltammograms did not follow the expected exponential trend. These results show a need for more research on the cathodes and the Butler-Volmer model, and they also show that the cathode is ready for further and longer application in a microbial fuel cell.
ContributorsRussell, Andrea Christine (Author) / Torres, Cesar (Thesis director) / Young, Michelle (Committee member) / School of Sustainable Engineering & Built Envirnmt (Contributor) / School of Sustainability (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Alternative ion exchange membranes for implementation in a peroxide production microbial electrochemical cel (PP-MEC) are explored through membrane stability tests with NaCl electrolyte and stabilizer EDTA at varying operational pHs. PP-MEC performance parameters \u2014 H2O2 concentration, current density, coulombic efficiency and power input required \u2014 are optimized over a 7 month continuous operation period based on their response to changes in HRT, EDTA concentration, air flow rate and electrolyte. I found that EDTA was compatible for use with the membranes. I also determined that AMI membranes were preferable to CMI and FAA because it was consistently stable and maintained its structural integrity. Still, I suggest testing more membranes because the AMI degraded in continuous operation. The PP-MEC produced up to 0.38 wt% H2O2, enough to perform water treatment through the Fenton process and significantly greater than the 0.13 wt% batch PP-MEC tests by previous researchers. It ran at > 0.20 W-hr/g H2O2 power input, ~ three orders of magnitude less than what is required for the anthraquinone process. I recommend high HRT and EDTA concentration while running the PP- MEC to increase H2O2 concentration, but low HRT and low EDTA concentration to decrease power input required. I recommend NaCl electrolyte but suggest testing new electrolytes that may control pH without degrading H2O2. I determined that air flow rate has no effect on PP-MEC operation. These recommendations should optimize PP-MEC operation based on its application.
ContributorsChowdhury, Nadratun Naeem (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Microbial fuel cells (MFCs) facilitate the conversion of organic matter to electrical current to make the total energy in black water treatment neutral or positive and produce hydrogen peroxide to assist the reuse of gray water. This research focuses on wastewater treatment at the U.S. military forward operating bases (FOBs). FOBs experience significant challenges with their wastewater treatment due to their isolation and dangers in transporting waste water and fresh water to and from the bases. Even though it is theoretically favorable to produce power in a MFC while treating black water, producing H2O2 is more useful and practical because it is a powerful cleaning agent that can reduce odor, disinfect, and aid in the treatment of gray water. Various acid forms of buffers were tested in the anode and cathode chamber to determine if the pH would lower in the cathode chamber while maintaining H2O2 efficiency, as well as to determine ion diffusion from the anode to the cathode via the membrane. For the catholyte experiments, phosphate and bicarbonate were tested as buffers while sodium chloride was the control. These experiments determined that the two buffers did not lower the pH. It was seen that the phosphate buffer reduced the H2O2 efficiency significantly while still staying at a high pH, while the bicarbonate buffer had the same efficiency as the NaCl control. For the anolyte experiments, it was shown that there was no diffusion of the buffers or MFC media across the membrane that would cause a decrease in the H2O2 production efficiency.
ContributorsThompson, Julia (Author) / Torres, Cesar (Thesis director) / Popat, Sudeep (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This research examines the current challenges of using Lamb wave interrogation methods to localize fatigue crack damage in a complex metallic structural component subjected to unknown temperatures. The goal of this work is to improve damage localization results for a structural component interrogated at an unknown temperature, by developing a probabilistic and reference-free framework for estimating Lamb wave velocities and the damage location. The methodology for damage localization at unknown temperatures includes the following key elements: i) a model that can describe the change in Lamb wave velocities with temperature; ii) the extension of an advanced time-frequency based signal processing technique for enhanced time-of-flight feature extraction from a dispersive signal; iii) the development of a Bayesian damage localization framework incorporating data association and sensor fusion. The technique requires no additional transducers to be installed on a structure, and allows for the estimation of both the temperature and the wave velocity in the component. Additionally, the framework of the algorithm allows it to function completely in an unsupervised manner by probabilistically accounting for all measurement origin uncertainty. The novel algorithm was experimentally validated using an aluminum lug joint with a growing fatigue crack. The lug joint was interrogated using piezoelectric transducers at multiple fatigue crack lengths, and at temperatures between 20°C and 80°C. The results showed that the algorithm could accurately predict the temperature and wave speed of the lug joint. The localization results for the fatigue damage were found to correlate well with the true locations at long crack lengths, but loss of accuracy was observed in localizing small cracks due to time-of-flight measurement errors. To validate the algorithm across a wider range of temperatures the electromechanically coupled LISA/SIM model was used to simulate the effects of temperatures. The numerical results showed that this approach would be capable of experimentally estimating the temperature and velocity in the lug joint for temperatures from -60°C to 150°C. The velocity estimation algorithm was found to significantly increase the accuracy of localization at temperatures above 120°C when error due to incorrect velocity selection begins to outweigh the error due to time-of-flight measurements.
ContributorsHensberry, Kevin (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Structural/system health monitoring (SHM) and prognostic health management (PHM) are vital techniques to ensure engineering system reliability and safety during the service. As multi-functionality and enhanced performance are in demand, modern engineering systems including aerospace, mechanical, and civil applications have become more complex. The constituent and architectural complexity, and multisource sensing sources in modern engineering systems may limit the monitoring capabilities of conventional approaches and require more advanced SHM/PHM techniques. Therefore, a hybrid methodology that incorporates information fusion, nondestructive evaluation (NDE), machine learning (ML), and statistical analysis is needed for more effective damage diagnosis/prognosis and system safety management.This dissertation presents an automated aviation health management technique to enable proactive safety management for both aircraft and national airspace system (NAS). A real-time, data-driven aircraft safety monitoring technique using ML models and statistical models is developed to enable an early-stage upset detection capability, which can improve pilot’s situational awareness and provide a sufficient safety margin. The detection accuracy and computational efficiency of the developed monitoring techniques is validated using commercial unlabeled flight data recorder (FDR) and reported accident FDR dataset. A stochastic post-upset prediction framework is developed using a high-fidelity flight dynamics model to predict the post-impacts in both aircraft and air traffic system. Stall upset scenarios that are most likely occurred during loss of control in-flight (LOC-I) operation are investigated, and stochastic flight envelopes and risk region are predicted to quantify their severities.
In addition, a robust, automatic damage diagnosis technique using ultrasonic Lamb waves and ML models is developed to effectively detect and classify fatigue damage modes in composite structures. The dispersion and propagation characteristics of the Lamb waves in a composite plate are investigated. A deep autoencoder-based diagnosis technique is proposed to detect fatigue damage using anomaly detection approach and automatically extract damage sensitive features from the waves. The patterns in the features are then further analyzed using outlier detection approach to classify the fatigue damage modes. The developed diagnosis technique is validated through an in-situ fatigue tests with periodic active sensing.
The developed techniques in this research are expected to be integrated with the existing safety strategies to enhance decision making process for improving engineering system safety without affecting the system’s functions.
ContributorsLee, Hyunseong (Author) / Chattopadhyay, Aditi (Thesis advisor) / Liu, Yongming (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Fard, Masoud Yekani (Committee member) / Tang, Pingbo (Committee member) / Campbell, Angela (Committee member) / Arizona State University (Publisher)
Created2021
Description
Energy can be harvested from wastewater using microbial fuel cells (MFC). In order to increase power generation, MFCs can be scaled-up. The MFCs are designed with two air cathodes and two anode electrodes. The limiting electrode for power generation is the cathode and in order to maximize power, the cathodes were made out of a C-N-Fe catalyst and a polytetrafluoroethylene binder which had a higher current production at -3.2 mA/cm2 than previous carbon felt cathodes at -0.15 mA/cm2 at a potential of -0.29 V. Commercial microbial fuel cells from Aquacycl were tested for their power production while operating with simulated blackwater achieved an average of 5.67 mW per cell. The small MFC with the C-N-Fe catalyst and one cathode was able to generate 8.7 mW. Imitating the Aquacycl cells, the new MFC was a scaled-up version of the small MFC where the cathode surface area increased from 81 cm2 to 200 cm2. While the MFC was operating with simulated blackwater, the peak power produced was 14.8 mW, more than the smaller MFC, but only increasing in the scaled-up MFC by 1.7 when the surface area of the cathode increased by 2.46. Further long-term application can be done, as well as operating multiple MFCs in series to generate more power and improve the design.
ContributorsRussell, Andrea (Author) / Torres, Cesar (Thesis advisor) / Garcia Segura, Sergio (Committee member) / Fraser, Matthew (Committee member) / Arizona State University (Publisher)
Created2022
Description
National Airspace Systems (NAS) are complex cyber-physical systems that require swift air traffic management (ATM) to ensure flight safety and efficiency. With the surging demand for air travel and the increasing intricacy of aviation systems, the need for advanced technologies to support air traffic management and air traffic control (ATC) service has become more crucial than ever. Data-driven models or artificial intelligence (AI) have been conceptually investigated by various parties and shown immense potential, especially when provided with a vast volume of real-world data. These data include traffic information, weather contours, operational reports, terrain information, flight procedures, and aviation regulations. Data-driven models learn from historical experiences and observations and provide expeditious recommendations and decision support for various operation tasks, directly contributing to the digital transformation in aviation. This dissertation reports several research studies covering different aspects of air traffic management and ATC service utilizing data-driven modeling, which are validated using real-world big data (flight tracks, flight events, convective weather, workload probes). These studies encompass a range of topics, including trajectory recommendations, weather studies, landing operations, and aviation human factors. Specifically, the topics explored are (i) trajectory recommendations under weather conditions, which examine the impact of convective weather on last on-file flight plans and provide calibrated trajectories based on convective weather; (ii) multi-aircraft trajectory predictions, which study the intention of multiple mid-air aircraft in the near-terminal airspace and provide trajectory predictions; (iii) flight scheduling operations, which involve probabilistic machine learning-enhanced optimization algorithms for robust and efficient aircraft landing sequencing; (iv) aviation human factors, which predict air traffic controller workload level from flight traffic data with conformalized graph neural network. The uncertainties associated with these studies are given special attention and addressed through Bayesian/probabilistic machine learning. Finally, discussions on high-level AI-enabled ATM research directions are provided, hoping to extend the proposed studies in the future. This dissertation demonstrates that data-driven modeling has great potential for aviation digital twins, revolutionizing the aviation decision-making process and enhancing the safety and efficiency of ATM. Moreover, these research directions are not merely add-ons to existing aviation practices but also contribute to the future of transportation, particularly in the development of autonomous systems.
ContributorsPang, Yutian (Author) / Liu, Yongming (Thesis advisor) / Yan, Hao (Committee member) / Zhuang, Houlong (Committee member) / Marvi, Hamid (Committee member) / Ren, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
The design of energy absorbing structures is driven by application specific requirements like the amount of energy to be absorbed, maximum transmitted stress that is permissible, stroke length, and available enclosing space. Cellular structures like foams are commonly leveraged in nature for energy absorption and have also found use in engineering applications. With the possibility of manufacturing complex cellular shapes using additive manufacturing technologies, there is an opportunity to explore new topologies that improve energy absorption performance. This thesis aims to systematically understand the relationships between four key elements: (i) unit cell topology, (ii) material composition, (iii) relative density, and (iv) fields; and energy absorption behavior, and then leverage this understanding to develop, implement and validate a methodology to design the ideal cellular structure energy absorber. After a review of the literature in the domain of additively manufactured cellular materials for energy absorption, results from quasi-static compression of six cellular structures (hexagonal honeycomb, auxetic and Voronoi lattice, and diamond, Gyroid, and Schwarz-P) manufactured out of AlSi10Mg and Nylon-12. These cellular structures were compared to each other in the context of four design-relevant metrics to understand the influence of cell design on the deformation and failure behavior. Three new and revised metrics for energy absorption were proposed to enable more meaningful comparisons and subsequent design selection. Triply Periodic Minimal Surface (TPMS) structures were found to have the most promising overall performance and formed the basis for the numerical investigation of the effect of fields on the energy absorption performance of TPMS structures. A continuum shell-based methodology was developed to analyze the large deformation behavior of field-driven variable thickness TPMS structures and validated against experimental data. A range of analytical and stochastic fields were then evaluated that modified the TPMS structure, some of which were found to be effective in enhancing energy absorption behavior in the structures while retaining the same relative density. Combining findings from studies on the role of cell geometry, composition, relative density, and fields, this thesis concludes with the development of a design framework that can enable the formulation of cellular material energy absorbers with idealized behavior.
ContributorsShinde, Mandar (Author) / Bhate, Dhruv (Thesis advisor) / Peralta, Pedro (Committee member) / Liu, Yongming (Committee member) / Jiao, Yang (Committee member) / Kwon, Beomjin (Committee member) / Arizona State University (Publisher)
Created2023