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- All Subjects: engineering
- All Subjects: water treatment
- Creators: Liu, Yongming
Description
Filtration for microfluidic sample-collection devices is desirable for sample selection, concentration, preprocessing, and downstream manipulation, but microfabricating the required sub-micrometer filtration structure is an elaborate process. This thesis presents a simple method to fabricate polydimethylsiloxane (PDMS) devices with an integrated membrane filter that will sample, lyse, and extract the DNA from microorganisms in aqueous environments. An off-the-shelf membrane filter disc was embedded in a PDMS layer and sequentially bound with other PDMS channel layers. No leakage was observed during filtration. This device was validated by concentrating a large amount of cyanobacterium Synechocystis in simulated sample water with consistent performance across devices. After accumulating sufficient biomass on the filter, a sequential electrochemical lysing process was performed by applying 5VDC across the filter. This device was further evaluated by delivering several samples of differing concentrations of cyanobacterium Synechocystis then quantifying the DNA using real-time PCR. Lastly, an environmental sample was run through the device and the amount of photosynthetic microorganisms present in the water was determined. The major breakthroughs in this design are low energy demand, cheap materials, simple design, straightforward fabrication, and robust performance, together enabling wide-utility of similar chip-based devices for field-deployable operations in environmental micro-biotechnology.
ContributorsLecluse, Aurelie (Author) / Meldrum, Deirdre (Thesis advisor) / Chao, Joseph (Thesis advisor) / Westerhoff, Paul (Committee member) / Arizona State University (Publisher)
Created2011
Description
Much of Nepal lacks access to clean drinking water, and many water sources are contaminated with arsenic at concentrations above both World Health Organization and local Nepalese guidelines. While many water treatment technologies exist, it is necessary to identify those that are easily implementable in developing areas. One simple treatment that has gained popularity is biochar—a porous, carbon-based substance produced through pyrolysis of biomass in an oxygen-free environment. Arizona State University’s Engineering Projects in Community Service (EPICS) has partnered with communities in Nepal in an attempt to increase biochar production in the area, as it has several valuable applications including water treatment. Biochar’s arsenic adsorption capability will be investigated in this project with the goal of using the biochar that Nepalese communities produce to remove water contaminants. It has been found in scientific literature that biochar is effective in removing heavy metal contaminants from water with the addition of iron through surface activation. Thus, the specific goal of this research was to compare the arsenic adsorption disparity between raw biochar and iron-impregnated biochar. It was hypothesized that after numerous bed volumes pass through a water treatment column, iron from the source water will accumulate on the surface of raw biochar, mimicking the intentionally iron-impregnated biochar and further increasing contaminant uptake. It is thus an additional goal of this project to compare biochar loaded with iron through an iron-spiked water column and biochar impregnated with iron through surface oxidation. For this investigation, the biochar was crushed and sieved to a size between 90 and 100 micrometers. Two samples were prepared: raw biochar and oxidized biochar. The oxidized biochar was impregnated with iron through surface oxidation with potassium permanganate and iron loading. Then, X-ray fluorescence was used to compare the composition of the oxidized biochar with its raw counterpart, indicating approximately 0.5% iron in the raw and 1% iron in the oxidized biochar. The biochar samples were then added to batches of arsenic-spiked water at iron to arsenic concentration ratios of 20 mg/L:1 mg/L and 50 mg/L:1 mg/L to determine adsorption efficiency. Inductively coupled plasma mass spectrometry (ICP-MS) analysis indicated an 86% removal of arsenic using a 50:1 ratio of iron to arsenic (1.25 g biochar required in 40 mL solution), and 75% removal with a 20:1 ratio (0.5 g biochar required in 40 mL solution). Additional samples were then inserted into a column process apparatus for further adsorption analysis. Again, ICP-MS analysis was performed and the results showed that while both raw and treated biochars were capable of adsorbing arsenic, they were exhausted after less than 70 bed volumes (234 mL), with raw biochar lasting 60 bed volumes (201 mL) and oxidized about 70 bed volumes (234 mL). Further research should be conducted to investigate more affordable and less laboratory-intensive processes to prepare biochar for water treatment.
ContributorsLaird, Ashlyn (Author) / Schoepf, Jared (Thesis director) / Westerhoff, Paul (Committee member) / Chemical Engineering Program (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Analysis and modeling of residual compounds in process streams from U.S. wastewater treatment plants
Description
The presence of compounds such as pharmaceuticals and personal care products (PPCPs) in the environment is a cause for concern as they exhibit secondary effects on non-target organisms and are also indicative of incomplete removal by wastewater treatment plants (WWTPs) during water reclamation. Analytical methods and predictive models can help inform on the rates at which these contaminants enter the environment via biosolids use or wastewater effluent release to estimate the risk of adverse effects. The goals of this research project were to integrate the results obtained from the two different methods of risk assessment, (a) in silico modeling and (b) experimental analysis. Using a previously published empirical model, influent and effluent concentration ranges were predicted for 10 sterols and validated with peer-reviewed literature. The in silico risk assessment analysis performed for sterols and hormones in biosolids concluded that hormones possess high leaching potentials and that particularly 17-α-ethinyl estradiol (EE2) can pose significant threat to fathead minnows (P. promelas) via leaching from terrestrial depositions of biosolids. Six mega-composite biosolids samples representative of 94 WWTPs were analyzed for a suite of 120 PPCPs using the extended U.S. EPA Method 1694 protocol. Results indicated the presence of 26 previously unmonitored PPCPs in the samples with estimated annual release rates of 5-15 tons yr-1 via land application of biosolids. A mesocosm sampling analysis that was included in the study concluded that four compounds amitriptyline, paroxetine, propranolol and sertraline warrant further monitoring due to their high release rates from land applied biosolids and their calculated extended half-lives in soils. There is a growing interest in the scientific community towards the development of new analytical protocols for analyzing solid matrices such as biosolids for the presence of PPCPs and other established and emerging contaminants of concern. The two studies presented here are timely and an important addition to the increasing base of scientific articles regarding environmental release of PPCPs and exposure risks associated with biosolids land application. This research study emphasizes the need for coupling experimental results with predictive analytical modeling output in order to more fully assess the risks posed by compounds detected in biosolids.
ContributorsPrakash Chari, Bipin (Author) / Halden, Rolf U. (Thesis advisor) / Westerhoff, Paul (Committee member) / Fox, Peter (Committee member) / Arizona State University (Publisher)
Created2012
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
Existing water quality sensors in surface, environmental, and drinking water systems are not well suited for long-term, scalable use as they require calibration, replacement of reagents, and are subject to biofouling which degrades measurement accuracy. Microbial Potentiometric Sensors (MPSs) offer an alternative approach to water quality monitoring by monitoring a biofilm-mediated potentiometric response to diverse water quality parameters. MPS biofilms grow naturally on graphite electrodes in diverse aqueous systems, are regenerative, and their potentiometric response correlates with numerous water quality parameters. As such, the overarching hypothesis of this dissertation is that MPS signal can be used to assess water quality trends and that its signal is driven by biofilm vitality. To test this hypothesis, machine learning, statistical regression, and the use of more complex, impedimetric measurement techniques were explored to characterize water quality trends in diverse water systems. This was accomplished by completing three dissertation objectives: 1.) Assess whether Machine Learning/Artificial Intelligence (ML/AI) tools can be used to disaggregate various surface water quality parameter values from Open Circuit Potential (OCP) signals produced by MPSs; 2.) Determine whether residual free chlorine concentration in drinking water could be determined by monitoring MPSs; and 3.) Determine whether OCP and/or Electrochemical Impedance Spectroscopy (EIS)-derived impedance data from an MPS can be used to determine water quality trends while confirming its biological origins. The findings confirm the hypothesis by demonstrating that ML/AI can be used to disaggregate MPS signal and determine numerous water quality parameters, offering unique opportunities for real-time monitoring of aqueous environments. Additionally, MPSs are particularly useful in measuring free chlorine concentrations in drinking water distribution systems which offers opportunities for scalable, in-situ, continuous monitoring of chlorine throughout a distribution network. Finally, the findings demonstrate that coupling MPSs’ OCP signal with more advanced measurement techniques such as EIS can improve understanding of drinking water quality trends, however current open source, affordable technologies capable of conducting EIS are prone to high measurement noise and are not currently accurate enough to be used in drinking water systems.
ContributorsSaboe, Daniel (Author) / Hristovski, Kiril (Thesis advisor) / Olson, Larry (Committee member) / Perreault, Francois (Committee member) / Arizona State University (Publisher)
Created2022
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
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
Description
Electrochemical technologies emerge as a feasible solution to monitor and treat pollutants. Although electrochemical technologies have garnered widespread attention, their commercial applications are still constrained by the use of expensive electrocatalysts, and the bulky and rigid plate design of electrodes that restricts electrochemical reactor design to systems with poor electrode surface/ volume treated ratios. By making electrodes flexible, more compact designs that maximize electrode surface per volume treated might become a reality. This dissertation encompasses the successful fabrication of flexible nanocomposite electrodes for electrocatalysis and electroanalysis applications.First, nano boron-doped diamond electrodes (BDD) were prepared as an inexpensive alternative to commercial boron-doped diamond electrodes. Comparative detailed surface and electrochemical characterization was conducted. Empirical study showed that replacing commercial BDD electrodes with nano-BDD electrodes can result in a cost reduction of roughly 1000x while maintaining the same electrochemical performance.
Next, self-standing electrodes were fabricated through the electropolymerization of conducing polymer, polypyrrole. Surface characterizations, such as SEM, FTIR and XPS proved the successful fabrication of these self-standing electrodes. High mechanical stability and bending flexibility demonstrated the ability to use these electrodes in different designs, such as roll-to-roll membranes. Electrochemical nitrite reduction was employed to demonstrate the viability of using self-standing nanocomposite electrodes for electrocatalytic applications reducing hazardous nitrogen oxyanions (i.e., nitrite) towards innocuous species such as nitrogen gas. A high faradaic efficiency of 78% was achieved, with high selectivity of 91% towards nitrogen gas.
To further enhance the conductivity and charge transfer properties of self-standing polypyrrole electrodes, three different nanoparticles, including copper (Cu), gold (Au), and platinum (Pt), were incorporated in the polypyrrole matrix. Effect of nanoparticle wt% and interaction between metal nanoparticles and polypyrrole matrix was investigated for electroanalytical applications, specifically dopamine sensing. Flexible nanocomposite electrodes showed outstanding performance as electrochemical sensors with PPy-Cu 120s exhibiting a low limit of detection (LOD) of 1.19 µM and PPy-Au 120s exhibiting a high linear range of 5 µM - 300 µM.
This dissertation outlines a method of fabricating self-standing electrodes and provides a pathway of using self-standing electrodes based on polypyrrole and polypyrrole-metal nanocomposites for various applications in wastewater treatment and electroanalytical sensing.
ContributorsBansal, Rishabh (Author) / Garcia-Segura, Sergio (Thesis advisor) / Westerhoff, Paul (Committee member) / Perreault, Francois (Committee member) / Chan, Candace (Committee member) / Arizona State University (Publisher)
Created2023
Description
Nitrate contamination to groundwater and surface water is a serious problem in areas with high agricultural production due to over application of fertilizers. There is a need for alternative technologies to reduce nutrient runoff without compromising yield. Carbon nanoparticles have adsorptive properties and have shown to improve germination and yield of a variety of crops. Graphite nanoparticles (CNP) were studied under a variety of different fertilizer conditions to grow lettuce for the three seasons of summer, fall, and winter. The aim of this thesis was to quantify the effect of CNPs on nitrate leaching and lettuce growth. This was accomplished by measuring the lettuce leaf yield, formulating a nutrient balance using the leachate, plant tissue, and soil data, and changing the hydraulic conductivity of the soil to assess the effect on nutrient mobility. summer and fall experiments used Arizona soil with different amounts of nitrogen, phosphorus, and potassium (NPK) fertilizer being applied to the soil with and without CNPs. The winter experiments used three different soil blends of Arizona soil, Arizona soil blended with 30% sand, and Arizona soil blended with 70% sand with a constant fertilizer treatment of 30% NPK with and without CNPs. The results showed that the 70% NPK with CNP treatment was best at reducing the amount of nitrate leached while having little to no compromise in yield. The winter experiments showed that the effectiveness of CNPs in reducing nitrate leaching and enhancing yield, improved with the higher the hydraulic conductivity of the soil.
ContributorsPandorf, Madelyn (Author) / Westerhoff, Paul K (Thesis advisor) / Boyer, Treavor (Committee member) / Perreault, Francois (Committee member) / Arizona State University (Publisher)
Created2018