Browsing by Subject "Stabilization"
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Item Arsenic removal and stabilization by synthesized pyrite(2009-05-15) Song, Jin KunArsenic is ubiquitous whether it is naturally occurring or produced by humans. It is found at sites on the National Priority List and at sites operated by DOE, where it is the second most commonly found contaminant. More wastes containing arsenic will be produced due to the lowering of the Maximum Contaminant Level (MCL) for arsenic in drinking water which will result in more treatment facilities for arsenic removal that will generate residuals. Furthermore, arsenic can be released from such wastes under the reduced conditions that are found in landfills. Pyrite (FeS2) is believed to be a compound that has a high affinity for arsenic and is stable under anoxic conditions. The first task of this research was to develop a method for making pyrite crystals of defined size with minimal reaction time and at high yield. Effects on the synthesis of pyrite particles of pH, the ratio of Fe/S, temperature and reaction time were investigated in batch reactor systems. Pyrite was synthesized within 24 hours at pH values ranging from pH 3.6 through pH 5.6, and at a ratio of Fe/S of 0.5. X-ray diffraction and scanning electron microscopy were used to size and characterize the pyrite particles. Experimental and analytical procedures developed for this work, included a hydride generation atomic absorption spectrometry method for measuring arsenic species (As(III), As(V)). The synthesized pyrite was applied to remove arsenic and its maximum capacity for arsenic removal was measured in batch adsorption experiments to be 3.23 ?mol/g for As(III) and 113 ?mol/g for As(V). Information obtained on the characteristics of chemical species before and after the reaction with arsenic showed that iron and sulfur were oxidized. Last, how strongly arsenic was bound to pyrite was investigated and it was determined that release of arsenic from As(III)-pyrite is not affected by pH, but release from As(V)-pyrite is affected by pH with minimum release in the range pH 5 to pH 8.Item Attitude dynamics stabilization with unknown delay in feedback control implementation(2009-12) Chunodkar, Apurva Arvind; Akella, Maruthi Ram, 1972-; Lightsey, Edgar G.This work addresses the problem of stabilizing attitude dynamics with an unknown delay in feedback. Two cases are considered: 1) constant time-delay 2) time-varying time-delay. This is to our best knowledge the first result that provides asymptotically stable closed-loop control design for the attitude dynamics problem with an unknown delay in feedback. Strict upper bounds on the unknown delay are assumed to be known. The time-varying delay is assumed to be made of the constant unknown delay with a time-varying perturbation. Upper bounds on the magnitude and rate of the time-varying part of the delay are assumed to be known. A novel modification to the concept of the complete type Lyapunov-Krasovskii (L-K) functional plays a crucial role in this analysis towards ensuring stability robustness to time-delay in the control design. The governing attitude dynamic equations are partitioned to form a nominal system with a perturbation term. Frequency domain analysis is employed in order to construct necessary and sufficient stability conditions for the nominal system. Consequently, a complete type L-K functional is constructed for stability analysis that includes the perturbation term. As an intermediate step, an analytical solution for the underlying Lyapunov matrix is obtained. Departing from previous approaches, where controller parameter values are arbitrarily chosen to satisfy the sufficient conditions obtained from robustness analysis, a systematic numerical optimization process is employed here to choose control parameters so that the region of attraction is maximized. The estimate of the region of attraction is directly related to the initial angular velocity norm and the closed-loop system is shown to be stable for a large set of initial attitude orientations.Item Comparative Deterministic and Probabilistic Modeling in Geotechnics: Applications to Stabilization of Organic Soils, Determination of Unknown Foundations for Bridge Scour, and One-Dimensional Diffusion Processes(2013-08-08) Yousefpour, NeginThis study presents different aspects on the use of deterministic methods including Artificial Neural Networks (ANNs), and linear and nonlinear regression, as well as probabilistic methods including Bayesian inference and Monte Carlo methods to develop reliable solutions for challenging problems in geotechnics. This study addresses the theoretical and computational advantages and limitations of these methods in application to: 1) prediction of the stiffness and strength of stabilized organic soils, 2) determination of unknown foundations for bridges vulnerable to scour, and 3) uncertainty quantification for one-dimensional diffusion processes. ANNs were successfully implemented in this study to develop nonlinear models for the mechanical properties of stabilized organic soils. ANN models were able to learn from the training examples and then generalize the trend to make predictions for the stiffness and strength of stabilized organic soils. A stepwise parameter selection and a sensitivity analysis method were implemented to identify the most relevant factors for the prediction of the stiffness and strength. Also, the variations of the stiffness and strength with respect to each factor were investigated. A deterministic and a probabilistic approach were proposed to evaluate the characteristics of unknown foundations of bridges subjected to scour. The proposed methods were successfully implemented and validated by collecting data for bridges in the Bryan District. ANN models were developed and trained using the database of bridges to predict the foundation type and embedment depth. The probabilistic Bayesian approach generated probability distributions for the foundation and soil characteristics and was able to capture the uncertainty in the predictions. The parametric and numerical uncertainties in the one-dimensional diffusion process were evaluated under varying observation conditions. The inverse problem was solved using Bayesian inference formulated by both the analytical and numerical solutions of the ordinary differential equation of diffusion. The numerical uncertainty was evaluated by comparing the mean and standard deviation of the posterior realizations of the process corresponding to the analytical and numerical solutions of the forward problem. It was shown that higher correlation in the structure of the observations increased both parametric and numerical uncertainties, whereas increasing the number of data dramatically decreased the uncertainties in the diffusion process.Item The Gaines-Oliphint house preservation report(2006-12) McKenzie, Grace Chantal; Holleran, MichaelThis historic preservation report focuses on the Gaines-Oliphint house in Sabine County, Texas. The first part of the report establishes the geographic and historic context of the home. Next, the report concentrates on establishing significance of the Gaines- Oliphint house based on the National Register’s criteria through both an association with a significant person in history and architectural characteristics representative of a particular time and style. Finally, the report outlines a preliminary conditions assessment of the Gaines-Oliphint house followed by recommendations for stabilization, preservation and restoration of the home.Item Quadcopter stabilization with neural network(2016-12) Burman, Prateek; Julien, ChristineUAVs (Unmanned Aerial Vehicle), also known as drones, are becoming attractive in the consumer space due to their relatively low cost and their ability to operate autonomously with minimal human intervention. A user could program the drone with GPS coordinates, and the drone would comply with utmost precision. In order for the drone to operate a preprogrammed flight path, it requires a host of sensors for it to gather data and operate on that data in real time. For instance, a consumer drone typically has obstacle avoidance sensors, a GPS sensor for routing and navigation, and an IMU (Inertial Measurement Unit) for tracking position and orientation. These sensors play a crucial role in both stabilization and navigation of the drone. This report aims to investigate, analyze and understand the complexity involved in designing and implementing an autonomous quadcopter; specifically, the stabilization algorithms. In general, stabilization is achieved using some form of control algorithm. The report covers a popular approach for stabilization (PID Control) found with many open source libraries and contrasts it with an alternative machine learning approach (Neural Networks). Finally, a machine learning based algorithm is implemented and evaluated on a prototype quadcopter, and its results are presented.Item Soil stabilization using optimum quantity of calcium chloride with Class F fly ash(Texas A&M University, 2006-10-30) Choi, Hyung JunOn-going research at Texas A&M University indicated that soil stabilization using calcium chloride filter cake along with Class F fly ash generates high strength. Previous studies were conducted with samples containing calcium chloride filter cake and both Class C fly ash and Class F fly ash. Mix design was fixed at 1.3% and 1.7% calcium chloride and 5% and 10% fly ash with crushed limestone base material. Throughout previous studies, recommended mix design was 1.7% calcium chloride filter cake with 10% Class F fly ash in crushed limestone base because Class F fly ash generates early high and durable strength. This research paper focused on the strength increase initiated by greater than 1.7% pure calcium chloride used with Class F fly ash in soil to verify the effectiveness and optimum ratio of calcium chloride and Class F fly ash in soil stabilization. Mix design was programmed at pure calcium chloride concentrations at 0% to 6% and Class F fly ash at 10 to 15%. Laboratory tests showed samples containing any calcium chloride concentration from 2% to 6% and Class F fly ash content from 10% to 15% obtained high early strength however, optimum moisture content, different mix design, and mineralogy deposit analysis are recommended to evaluate the role and the effectiveness of calcium chloride in soil stabilization because of the strength decreasing tendency of the samples containing calcium chloride after 56 days.Item Sorption of Arsenic, Mercury, Selenium onto Nanostructured Adsorbent Media and Stabilization via Surface Reactions(2011-02-22) Han, Dong SukThe overall goal of this study is to evaluate the ability of novel nanostructured adsorbent media (NTAs, iron sulfides (FeS2 and FeS)) to remove arsenic, selenium and mercury from ash and scrubber pond effluents. The NTAs aim to enhance arsenic removal from solution compared to conventional adsorbents. The iron sulfides are expected to produce stable residuals for ultimate disposal after removing As, Se and Hg from solution, so that removal of these compounds from wastewaters will not result in contamination of soils and groundwaters. Methods for reliably and economically producing these materials were developed. The synthesized NTAs and iron sulfides were characterized by surface analysis techniques such as XRD, FT-IR, SEM-EDS, TEM, XPS, AFM and N2-adsorption. These analyses indicated that Ti(25)-SBA-15 has highly ordered hexagonal mesopores, MT has interparticle mesopores, pyrite (FeS2) forms crystalline, nonporous rectangular nanoparticles (<500 nm), and mackinawite (FeS) forms amorphous, nonporous nanoparticles (<100 nm). Kinetic and equilibrium tests for As(III, V) removal were conducted with NTAs over a range of pH (4, 7, 9.5). The rates of arsenic uptake were very fast and followed a bi-phasic sorption pattern, where sorption was fast for the first 10 minutes, and then slowed and was almost completed within 200 minutes. Distinct sorption maxima for As(III) removal were observed between pH 7 and pH 9.5 for MT and between pH 4 and pH 7 for Ti(25)-SBA-15. The amount of As(V) adsorbed generally decreased as pH increased. In addition, a surface complexation model (SCM) based on the diffuse layer model (DLM) was used to predict arsenic adsorption envelopes by NTAs under various environmental conditions. The SCM for As(III, V) adsorption by NTAs demonstrated the role of mono- and bidentate surface complexes in arsenic adsorption. A batch reactor system was employed in an anaerobic chamber to conduct experiments to characterize both the removal of As, Se, Hg from solution and their subsequent reactions with iron sulfides. Experiment variables for removal experiments included: contaminant valence state (As(V), As(III), Se(VI), Se(IV), Hg(II)); adsorbent/reactant type (FeS, FeS2); adsorbent/reactant concentration; pH (7, 8, 9, 10); and competing ion (SO42-) concentration (0, 1, 10 mM). Experimental variables for reaction experiments were reaction time (up to 30 days) at pH 8 and oxidation states of contaminants. In addition, the stability of iron sulfides (FeS2, FeS) combined with target compounds was investigated by measuring the ability of the target compounds to resist release to the aqueous phase after removal. These experiments showed that iron sulfides were good adsorbent/reactants for target contaminants in spite of the presence of sulfate. This was particularly true at intermediate concentrations of target compounds. The experiments also demonstrated that iron sulfides interacted with target contaminants in such a way to improve their resistance to being released back to solution as pH was changed. Therefore, this study demonstrates the ability of novel nanostructured adsorbent media to remove arsenic, selenium and mercury from ash and scrubber pond effluents and the ability of iron sulfides to produce residuals that are stable when disposed in landfills.