Matching Items (196)
Description
Water contamination with nitrate (NO3−) (from fertilizers) and perchlorate (ClO4−) (from rocket fuel and explosives) is a widespread environmental problem. I employed the Membrane Biofilm Reactor (MBfR), a novel bioremediation technology, to treat NO3− and ClO4− in the presence of naturally occurring sulfate (SO42−). In the MBfR, bacteria reduce oxidized pollutants that act as electron acceptors, and they grow as a biofilm on the outer surface of gas-transfer membranes that deliver the electron donor (hydrogen gas, (H2). The overarching objective of my research was to achieve a comprehensive understanding of ecological interactions among key microbial members in the MBfR when treating polluted water with NO3− and ClO4− in the presence of SO42−. First, I characterized competition and co-existence between denitrifying bacteria (DB) and sulfate-reducing bacteria (SRB) when the loading of either the electron donor or electron acceptor was varied. Then, I assessed the microbial community structure of biofilms mostly populated by DB and SRB, linking structure with function based on the electron-donor bioavailability and electron-acceptor loading. Next, I introduced ClO4− as a second oxidized contaminant and discovered that SRB harm the performance of perchlorate-reducing bacteria (PRB) when the aim is complete ClO4− destruction from a highly contaminated groundwater. SRB competed too successfully for H2 and space in the biofilm, forcing the PRB to unfavorable zones in the biofilm. To better control SRB, I tested a two-stage MBfR for total ClO4− removal from a groundwater highly contaminated with ClO4−. I document successful remediation of ClO4− after controlling SO4 2− reduction by restricting electron-donor availability and increasing the acceptor loading to the second stage reactor. Finally, I evaluated the performance of a two-stage pilot MBfR treating water polluted with NO3− and ClO4−, and I provided a holistic understanding of the microbial community structure and diversity. In summary, the microbial community structure in the MBfR contributes to and can be used to explain/predict successful or failed water bioremediation. Based on this understanding, I developed means to manage the microbial community to achieve desired water-decontamination results. This research shows the benefits of looking "inside the box" for "improving the box".
ContributorsOntiveros-Valencia, Aura (Author) / Rittmann, Bruce E. (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Torres, Cesar I. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose significant challenges as brain tissue is both functionally and structurally dynamic: a) time varying mechanical properties of the brain tissue-microelectrode interface due to the hyperelastic, viscoelastic nature of brain tissue b) non-stationarities in the neural signal caused by mechanical and physiological events in the interface and c) the lack of visual feedback of microelectrode position in brain tissue. A closed loop control algorithm is proposed here for autonomous navigation of microelectrodes in brain tissue while optimizing the signal-to-noise ratio of multi-unit neural recordings. The algorithm incorporates a quantitative understanding of constitutive mechanical properties of soft viscoelastic tissue like the brain and is guided by models that predict stresses developed in brain tissue during movement of the microelectrode. An optimal movement strategy is developed that achieves precise positioning of microelectrodes in the brain by minimizing the stresses developed in the surrounding tissue during navigation and maximizing the speed of movement. Results of testing the closed-loop control paradigm in short-term rodent experiments validated that it was possible to achieve a consistently high quality SNR throughout the duration of the experiment. At the systems level, new generation of MEMS actuators for movable microelectrode array are characterized and the MEMS device operation parameters are optimized for improved performance and reliability. Further, recommendations for packaging to minimize the form factor of the implant; design of device mounting and implantation techniques of MEMS microelectrode array to enhance the longevity of the implant are also included in a top-down approach to achieve a reliable brain interface.
ContributorsAnand, Sindhu (Author) / Muthuswamy, Jitendran (Thesis advisor) / Tillery, Stephen H (Committee member) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Mobile robots are used in a broad range of application areas; e.g. search and rescue, reconnaissance, exploration, etc. Given the increasing need for high performance mobile robots, the area has received attention by researchers. In this thesis, critical control and control-relevant design issues for differential drive mobile robots is addressed. Two major themes that have been explored are the use of kinematic models for control design and the use of decentralized proportional plus integral (PI) control. While these topics have received much attention, there still remain critical questions which have not been rigorously addressed. In this thesis, answers to the following critical questions are provided: When is 1. a kinematic model sufficient for control design? 2. coupled dynamics essential? 3. a decentralized PI inner loop velocity controller sufficient? 4. centralized multiple-input multiple-output (MIMO) control essential? and how can one design the robot to relax the requirements implied in 1 and 2? In this thesis, the following is shown: 1. The nonlinear kinematic model will suffice for control design when the inner velocity (dynamic) loop is much faster (10X) than the slower outer positioning loop. 2. A dynamic model is essential when the inner velocity (dynamic) loop is less than two times faster than the slower outer positioning loop. 3. A decentralized inner loop PI velocity controller will be sufficient for accomplish- ing high performance control when the required velocity bandwidth is small, rel- ative to the peak dynamic coupling frequency. A rule-of-thumb which depends on the robot aspect ratio is given. 4. A centralized MIMO velocity controller is needed when the required bandwidth is large, relative to the peak dynamic coupling frequency. Here, the analysis in the thesis is sparse making the topic an area for future analytical work. Despite this, it is clearly shown that a centralized MIMO inner loop controller can offer increased performance vis- ́a-vis a decentralized PI controller. 5. Finally, it is shown how the dynamic coupling depends on the robot aspect ratio and how the coupling can be significantly reduced. As such, this can be used to ease the requirements imposed by 2 and 4 above.
ContributorsAnvari, Iman (Author) / Rodriguez, Armando A (Thesis advisor) / Si, Jenni (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
This study focuses on implementing probabilistic nature of material properties (Kevlar® 49) to the existing deterministic finite element analysis (FEA) of fabric based engine containment system through Monte Carlo simulations (MCS) and implementation of probabilistic analysis in engineering designs through Reliability Based Design Optimization (RBDO). First, the emphasis is on experimental data analysis focusing on probabilistic distribution models which characterize the randomness associated with the experimental data. The material properties of Kevlar® 49 are modeled using experimental data analysis and implemented along with an existing spiral modeling scheme (SMS) and user defined constitutive model (UMAT) for fabric based engine containment simulations in LS-DYNA. MCS of the model are performed to observe the failure pattern and exit velocities of the models. Then the solutions are compared with NASA experimental tests and deterministic results. MCS with probabilistic material data give a good prospective on results rather than a single deterministic simulation results. The next part of research is to implement the probabilistic material properties in engineering designs. The main aim of structural design is to obtain optimal solutions. In any case, in a deterministic optimization problem even though the structures are cost effective, it becomes highly unreliable if the uncertainty that may be associated with the system (material properties, loading etc.) is not represented or considered in the solution process. Reliable and optimal solution can be obtained by performing reliability optimization along with the deterministic optimization, which is RBDO. In RBDO problem formulation, in addition to structural performance constraints, reliability constraints are also considered. This part of research starts with introduction to reliability analysis such as first order reliability analysis, second order reliability analysis followed by simulation technique that are performed to obtain probability of failure and reliability of structures. Next, decoupled RBDO procedure is proposed with a new reliability analysis formulation with sensitivity analysis, which is performed to remove the highly reliable constraints in the RBDO, thereby reducing the computational time and function evaluations. Followed by implementation of the reliability analysis concepts and RBDO in finite element 2D truss problems and a planar beam problem are presented and discussed.
ContributorsDeivanayagam, Arumugam (Author) / Rajan, Subramaniam D. (Thesis advisor) / Mobasher, Barzin (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. However, this process still requires that seizures are visually detected and marked by experienced and trained electroencephalographers. The motivation for the development of an automated seizure detection algorithm in this research was to assist physicians in such a laborious, time consuming and expensive task. Seizures in the EEG vary in duration (seconds to minutes), morphology and severity (clinical to subclinical, occurrence rate) within the same patient and across patients. The task of seizure detection is also made difficult due to the presence of movement and other recording artifacts. An early approach towards the development of automated seizure detection algorithms utilizing both EEG changes and clinical manifestations resulted to a sensitivity of 70-80% and 1 false detection per hour. Approaches based on artificial neural networks have improved the detection performance at the cost of algorithm's training. Measures of nonlinear dynamics, such as Lyapunov exponents, have been applied successfully to seizure prediction. Within the framework of this MS research, a seizure detection algorithm based on measures of linear and nonlinear dynamics, i.e., the adaptive short-term maximum Lyapunov exponent (ASTLmax) and the adaptive Teager energy (ATE) was developed and tested. The algorithm was tested on long-term (0.5-11.7 days) continuous EEG recordings from five patients (3 with intracranial and 2 with scalp EEG) and a total of 56 seizures, producing a mean sensitivity of 93% and mean specificity of 0.048 false positives per hour. The developed seizure detection algorithm is data-adaptive, training-free and patient-independent. It is expected that this algorithm will assist physicians in reducing the time spent on detecting seizures, lead to faster and more accurate diagnosis, better evaluation of treatment, and possibly to better treatments if it is incorporated on-line and real-time with advanced neuromodulation therapies for epilepsy.
ContributorsVenkataraman, Vinay (Author) / Jassemidis, Leonidas (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2012
Description
As engineered nanomaterials (NMs) become used in industry and commerce their loading to sewage will increase. However, the fate of widely used NMs in wastewater treatment plants (WWTPs) remains poorly understood. In this research, sequencing batch reactors (SBRs) were operated with hydraulic (HRT) and sludge (SRT) retention times representative of full-scale biological WWTPs for several weeks. NM loadings at the higher range of expected environmental concentrations were selected. To achieve the pseudo-equilibrium state concentration of NMs in biomass, SBR experiments needed to operate for more than three times the SRT value, approximately 18 days. Under the conditions tested, NMs had negligible effects on ability of the wastewater bacteria to biodegrade organic material, as measured by chemical oxygen demand (COD). NM mass balance closure was achieved by measuring NMs in liquid effluent and waste biosolids. All NMs were well removed at the typical biomass concentration (1~2 gSS/L). However, carboxy-terminated polymer coated silver nanoparticles (fn-Ag) were removed less effectively (88% removal) than hydroxylated fullerenes (fullerols; >90% removal), nano TiO2 (>95% removal) or aqueous fullerenes (nC60; >95% removal). Although most NMs did not settle out of the feed solution without bacteria present, approximately 65% of the titanium dioxide was removed even in the absence of biomass simply due to self-aggregation and settling. Experiments conducted over 4 months with daily loadings of nC60 showed that nC60 removal from solution depends on the biomass concentration. Under conditions representative of most suspended growth biological WWTPs (e.g., activated sludge), most of the NMs will accumulate in biosolids rather than in liquid effluent discharged to surface waters. Significant fractions of fn-Ag were associated with colloidal material which suggests that efficient particle separation processes (sedimentation or filtration) could further improve removal of NM from effluent. As most NMs appear to accumulate in biosolids, future research should examine the fate of NMs during disposal of WWTP biosolids, which may occur through composting or anaerobic digestion and/or land application, incineration, or landfill disposal.
ContributorsWang, Yifei (Author) / Westerhoff, Paul (Thesis advisor) / Krajmalnik-Brown, Rosa (Committee member) / Rittmann, Bruce (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2012
Description
Concrete design has recently seen a shift in focus from prescriptive specifications to performance based specifications with increasing demands for sustainable products. Fiber reinforced composites (FRC) provides unique properties to a material that is very weak under tensile loads. The addition of fibers to a concrete mix provides additional ductility and reduces the propagation of cracks in the concrete structure. It is the fibers that bridge the crack and dissipate the incurred strain energy in the form of a fiber-pullout mechanism. The addition of fibers plays an important role in tunnel lining systems and in reducing shrinkage cracking in high performance concretes. The interest in most design situations is the load where cracking first takes place. Typically the post crack response will exhibit either a load bearing increase as deflection continues, or a load bearing decrease as deflection continues. These behaviors are referred to as strain hardening and strain softening respectively. A strain softening or hardening response is used to model the behavior of different types of fiber reinforced concrete and simulate the experimental flexural response. Closed form equations for moment-curvature response of rectangular beams under four and three point loading in conjunction with crack localization rules are utilized. As a result, the stress distribution that considers a shifting neutral axis can be simulated which provides a more accurate representation of the residual strength of the fiber cement composites. The use of typical residual strength parameters by standards organizations ASTM, JCI and RILEM are examined to be incorrect in their linear elastic assumption of FRC behavior. Finite element models were implemented to study the effects and simulate the load defection response of fiber reinforced shotcrete round discrete panels (RDP's) tested in accordance with ASTM C-1550. The back-calculated material properties from the flexural tests were used as a basis for the FEM material models. Further development of FEM beams were also used to provide additional comparisons in residual strengths of early age samples. A correlation between the RDP and flexural beam test was generated based a relationship between normalized toughness with respect to the newly generated crack surfaces. A set of design equations are proposed using a residual strength correction factor generated by the model and produce the design moment based on specified concrete slab geometry.
ContributorsBarsby, Christopher (Author) / Mobasher, Barzin (Thesis advisor) / Rajan, Subramaniam D. (Committee member) / Neithalath, Narayanan (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this work, the vapor transport and aerobic bio-attenuation of compounds from a multi-component petroleum vapor mixture were studied for six idealized lithologies in 1.8-m tall laboratory soil columns. Columns representing different geological settings were prepared using 20-40 mesh sand (medium-grained) and 16-minus mesh crushed granite (fine-grained). The contaminant vapor source was a liquid composed of twelve petroleum hydrocarbons common in weathered gasoline. It was placed in a chamber at the bottom of each column and the vapors diffused upward through the soil to the top where they were swept away with humidified gas. The experiment was conducted in three phases: i) nitrogen sweep gas; ii) air sweep gas; iii) vapor source concentrations decreased by ten times from the original concentrations and under air sweep gas. Oxygen, carbon dioxide and hydrocarbon concentrations were monitored over time. The data allowed determination of times to reach steady conditions, effluent mass emissions and concentration profiles. Times to reach near-steady conditions were consistent with theory and chemical-specific properties. First-order degradation rates were highest for straight-chain alkanes and aromatic hydrocarbons. Normalized effluent mass emissions were lower for lower source concentration and aerobic conditions. At the end of the study, soil core samples were taken every 6 in. Soil moisture content analyses showed that water had redistributed in the soil during the experiment. The soil at the bottom of the columns generally had higher moisture contents than initial values, and soil at the top had lower moisture contents. Profiles of the number of colony forming units of hydrocarbon-utilizing bacteria/g-soil indicated that the highest concentrations of degraders were located at the vertical intervals where maximum degradation activity was suggested by CO2 profiles. Finally, the near-steady conditions of each phase of the study were simulated using a three-dimensional transient numerical model. The model was fit to the Phase I data by adjusting soil properties, and then fit to Phase III data to obtain compound-specific first-order biodegradation rate constants ranging from 0.0 to 5.7x103 d-1.
ContributorsEscobar Melendez, Elsy (Author) / Johnson, Paul C. (Thesis advisor) / Andino, Jean (Committee member) / Forzani, Erica (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Kavazanjian, Edward (Committee member) / Arizona State University (Publisher)
Created2012
Description
Ultra-concealable multi-threat body armor used by law-enforcement is a multi-purpose armor that protects against attacks from knife, spikes, and small caliber rounds. The design of this type of armor involves fiber-resin composite materials that are flexible, light, are not unduly affected by environmental conditions, and perform as required. The National Institute of Justice (NIJ) characterizes this type of armor as low-level protection armor. NIJ also specifies the geometry of the knife and spike as well as the strike energy levels required for this level of protection. The biggest challenges are to design a thin, lightweight and ultra-concealable armor that can be worn under street clothes. In this study, several fundamental tasks involved in the design of such armor are addressed. First, the roles of design of experiments and regression analysis in experimental testing and finite element analysis are presented. Second, off-the-shelf materials available from international material manufacturers are characterized via laboratory experiments. Third, the calibration process required for a constitutive model is explained through the use of experimental data and computer software. Various material models in LS-DYNA for use in the finite element model are discussed. Numerical results are generated via finite element simulations and are compared against experimental data thus establishing the foundation for optimizing the design.
ContributorsVokshi, Erblina (Author) / Rajan, Subramaniam D. (Thesis advisor) / Neithalath, Narayanan (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Contamination by chlorinated ethenes is widespread in groundwater aquifers, sediment, and soils worldwide. The overarching objectives of my research were to understand how the bacterial genus Dehalococcoides function optimally to carry out reductive dechlorination of chlorinated ethenes in a mixed microbial community and then apply this knowledge to manage dechlorinating communities in the hydrogen-based membrane biofilm reactor (MBfR). The MBfR is used for the biological reduction of oxidized contaminants in water using hydrogen supplied as the electron donor by diffusion through gas-transfer fibers. First, I characterized a new anaerobic dechlorinating community developed in our laboratory, named DehaloR^2, in terms of chlorinated ethene turnover rates and assessed its microbial community composition. I then carried out an experiment to correlate performance and community structure for trichloroethene (TCE)-fed microbial consortia. Fill-and-draw reactors inoculated with DehaloR^2 demonstrated a direct correlation between microbial community function and structure as the TCE-pulsing rate was increased. An electron-balance analysis predicted the community structure based on measured concentrations of products and constant net yields for each microorganism. The predictions corresponded to trends in the community structure based on pyrosequencing and quantitative PCR up to the highest TCE pulsing rate, where deviations to the trend resulted from stress by the chlorinated ethenes. Next, I optimized a method for simultaneous detection of chlorinated ethenes and ethene at or below the Environmental Protection Agency maximum contaminant levels for groundwater using solid phase microextraction in a gas chromatograph with a flame ionization detector. This method is ideal for monitoring biological reductive dechlorination in groundwater, where ethene is the ultimate end product. The major advantage of this method is that it uses a small sample volume of 1 mL, making it ideally suited for bench-scale feasibility studies, such as the MBfR. Last, I developed a reliable start-up and operation strategy for TCE reduction in the MBfR. Successful operation relied on controlling the pH-increase effects of methanogenesis and homoacetogenesis, along with creating hydrogen limitation during start-up to allow dechlorinators to compete against other microorgansims. Methanogens were additionally minimized during continuous flow operation by a limitation in bicarbonate resulting from strong homoacetogenic activity.
ContributorsZiv-El, Michal (Author) / Rittmann, Bruce E. (Thesis advisor) / Krajmalnik-Brown, Rosa (Thesis advisor) / Halden, Rolf U. (Committee member) / Arizona State University (Publisher)
Created2012