Matching Items (25)
Filtering by
- All Subjects: engineering
- Creators: Rogers, Bradley
Description
The object of this study was a 26 year old residential Photovoltaic (PV) monocrystalline silicon (c-Si) power plant, called Solar One, built by developer John F. Long in Phoenix, Arizona (a hot-dry field condition). The task for Arizona State University Photovoltaic Reliability Laboratory (ASU-PRL) graduate students was to evaluate the power plant through visual inspection, electrical performance, and infrared thermography. The purpose of this evaluation was to measure and understand the extent of degradation to the system along with the identification of the failure modes in this hot-dry climatic condition. This 4000 module bipolar system was originally installed with a 200 kW DC output of PV array (17 degree fixed tilt) and an AC output of 175 kVA. The system was shown to degrade approximately at a rate of 2.3% per year with no apparent potential induced degradation (PID) effect. The power plant is made of two arrays, the north array and the south array. Due to a limited time frame to execute this large project, this work was performed by two masters students (Jonathan Belmont and Kolapo Olakonu) and the test results are presented in two masters theses. This thesis presents the results obtained on the north array and the other thesis presents the results obtained on the south array. The resulting study showed that PV module design, array configuration, vandalism, installation methods and Arizona environmental conditions have had an effect on this system's longevity and reliability. Ultimately, encapsulation browning, higher series resistance (potentially due to solder bond fatigue) and non-cell interconnect ribbon breakages outside the modules were determined to be the primary causes for the power loss.
ContributorsBelmont, Jonathan (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Henderson, Mark (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2013
Description
Reductive dechlorination by members of the bacterial genus Dehalococcoides is a common and cost-effective avenue for in situ bioremediation of sites contaminated with the chlorinated solvents, trichloroethene (TCE) and perchloroethene (PCE). The overarching goal of my research was to address some of the challenges associated with bioremediation timeframes by improving the rates of reductive dechlorination and the growth of Dehalococcoides in mixed communities. Biostimulation of contaminated sites or microcosms with electron donor fails to consistently promote dechlorination of PCE/TCE beyond cis-dichloroethene (cis-DCE), even when the presence of Dehalococcoides is confirmed. Supported by data from microcosm experiments, I showed that the stalling at cis-DCE is due a H2 competition in which components of the soil or sediment serve as electron acceptors for competing microorganisms. However, once competition was minimized by providing selective enrichment techniques, I illustrated how to obtain both fast rates and high-density Dehalococcoides using three distinct enrichment cultures. Having achieved a heightened awareness of the fierce competition for electron donor, I then identified bicarbonate (HCO3-) as a potential H2 sink for reductive dechlorination. HCO3- is the natural buffer in groundwater but also the electron acceptor for hydrogenotrophic methanogens and homoacetogens, two microbial groups commonly encountered with Dehalococcoides. By testing a range of concentrations in batch experiments, I showed that methanogens are favored at low HCO3 and homoacetogens at high HCO3-. The high HCO3- concentrations increased the H2 demand which negatively affected the rates and extent of dechlorination. By applying the gained knowledge on microbial community management, I ran the first successful continuous stirred-tank reactor (CSTR) at a 3-d hydraulic retention time for cultivation of dechlorinating cultures. I demonstrated that using carefully selected conditions in a CSTR, cultivation of Dehalococcoides at short retention times is feasible, resulting in robust cultures capable of fast dechlorination. Lastly, I provide a systematic insight into the effect of high ammonia on communities involved in dechlorination of chloroethenes. This work documents the potential use of landfill leachate as a substrate for dechlorination and an increased tolerance of Dehalococcoides to high ammonia concentrations (2 g L-1 NH4+-N) without loss of the ability to dechlorinate TCE to ethene.
ContributorsDelgado, Anca Georgiana (Author) / Krajmalnik-Brown, Rosa (Thesis advisor) / Cadillo-Quiroz, Hinsby (Committee member) / Halden, Rolf U. (Committee member) / Rittmann, Bruce E. (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2013
Description
While the piezoelectric effect has been around for some time, it has only recently caught interest as a potential sustainable energy harvesting device. Piezoelectric energy harvesting has been developed for shoes and panels, but has yet to be integrated into a marketable bicycle tire. For this thesis, the development and feasibility of a piezoelectric tire was done. This includes the development of a circuit that incorporates piezoceramic elements, energy harvesting circuitry, and an energy storage device. A single phase circuit was designed using an ac-dc diode rectifier. An electrolytic capacitor was used as the energy storage device. A financial feasibility was also done to determine targets for manufacturing cost and sales price. These models take into account market trends for high performance tires, economies of scale, and the possibility of government subsidies. This research will help understand the potential for the marketability of a piezoelectric energy harvesting tire that can create electricity for remote use. This study found that there are many obstacles that must be addressed before a piezoelectric tire can be marketed to the general public. The power output of this device is miniscule compared to an alkaline battery. In order for this device to approach the power output of an alkaline battery the weight of the device would also become an issue. Additionally this device is very costly compared to the average bicycle tire. Lastly, this device is extreme fragile and easily broken. In order for this device to become marketable the issues of power output, cost, weight, and durability must all be successfully overcome.
ContributorsMalotte, Christopher (Author) / Madakannan, Arunachalanadar (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
Description
Potential induced degradation (PID) due to high system voltages is one of the major degradation mechanisms in photovoltaic (PV) modules, adversely affecting their performance due to the combined effects of the following factors: system voltage, superstrate/glass surface conductivity, encapsulant conductivity, silicon nitride anti-reflection coating property and interface property (glass/encapsulant; encapsulant/cell; encapsulant/backsheet). Previous studies carried out at ASU's Photovoltaic Reliability Laboratory (ASU-PRL) showed that only negative voltage bias (positive grounded systems) adversely affects the performance of commonly available crystalline silicon modules. In previous studies, the surface conductivity of the glass surface was obtained using either conductive carbon layer extending from the glass surface to the frame or humidity inside an environmental chamber. This thesis investigates the influence of glass surface conductivity disruption on PV modules. In this study, conductive carbon was applied only on the module's glass surface without extending to the frame and the surface conductivity was disrupted (no carbon layer) at 2cm distance from the periphery of frame inner edges. This study was carried out under dry heat at two different temperatures (60 °C and 85 °C) and three different negative bias voltages (-300V, -400V, and -600V). To replicate closeness to the field conditions, half of the selected modules were pre-stressed under damp heat for 1000 hours (DH 1000) and the remaining half under 200 hours of thermal cycling (TC 200). When the surface continuity was disrupted by maintaining a 2 cm gap from the frame to the edge of the conductive layer, as demonstrated in this study, the degradation was found to be absent or negligibly small even after 35 hours of negative bias at elevated temperatures. This preliminary study appears to indicate that the modules could become immune to PID losses if the continuity of the glass surface conductivity is disrupted at the inside boundary of the frame. The surface conductivity of the glass, due to water layer formation in a humid condition, close to the frame could be disrupted just by applying a water repelling (hydrophobic) but high transmittance surface coating (such as Teflon) or modifying the frame/glass edges with water repellent properties.
ContributorsTatapudi, Sai Ravi Vasista (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2012
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 report presents the effects and analysis of the effects of Pulsed-Gas Metal Arc Welding's (P-GMAW) on Lean Duplex stainless steel. Although the welding of Duplex and Super Duplex Stainless steels have been well documented in both the laboratory and construction industry, the use of Lean Duplex has not. The purpose for conducting this research is to ensure that the correct Ferrite-Austenite phase balance along with the correct welding procedures are used in the creation of reactor cores for new construction nuclear power generation stations. In this project the effects of Lincoln Electrics ER-2209 GMAW wire are studied. Suggestions and improvements to the welding process are then proposed in order to increase the weldability, strength, gas selection, and ferrite count. The weldability will be measured using X-Ray photography in order to determine if any inclusions, lack of fusion, or voids are found post welding, along with welder feedback. The ferritic point count method in accordance with ASTM A562-08, is employed so that the amount of ferrite and austenite can be calculated in the same manor that is currently being used in industry. These will then be correlated to the tensile strength and impact toughness in the heat-affected zone (HAZ) of the weld based on the ASTM A923 testing method.
ContributorsCarter, Roger (Author) / Rogers, Bradley (Thesis advisor) / Gintz, Jerry (Committee member) / Georgeou, Trian (Committee member) / Arizona State University (Publisher)
Created2012
Description
Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.
ContributorsDye, Michaela (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
Solar photovoltaic (PV) generation has seen significant growth in 2021, with an increase of around 22% and exceeding 1000 TWh. However, this has also led to reliability and durability issues, particularly potential induced degradation (PID), which can reduce module output by up to 30%. This study uses cell- and module-level analysis to investigate the impact of superstrate, encapsulant, and substrate on PID.The influence of different substrates and encapsulants is studied using one-cell modules, showing that substrates with poor water-blocking properties can worsen PID, and encapsulants with lower volumetric resistance can conduct easily under damp conditions, enabling PID mechanisms (results show maximum degradation of 9%). Applying an anti-soiling coating on the front glass (superstrate) reduces PID by nearly 53%. Typical superstrates have sodium which accelerates the PID process, and therefore, using such coatings can lessen the PID problem.
At the module level, the study examines the influence of weakened interface adhesion strengths in traditional Glass-Backsheet (GB) and emerging Glass-Glass (GG) (primarily bifacial modules) constructions. The findings show nearly 64% more power degradation in GG modules than in GB. Moreover, the current methods for detecting PID use new modules, which can give inaccurate information instead of DH-stressed modules for PID testing, as done in this work.
A comprehensive PID susceptibility analysis for multiple fresh bifacial constructions shows significant degradation from 20 to 50% in various constructions. The presence of glass as the substrate exacerbates the PID problem due to more ionic activity available from the two glass sides. Recovery experiments are also conducted to understand the extent of the PID issue.
Overall, this study identifies, studies, and explains the impact of superstrate, substrate, and encapsulant on the underlying PID mechanisms. Various pre- and post-stress characterization tests, including light and dark current-voltage (I-V) tests, electroluminescence (EL) imaging, infrared (IR) imaging, and UV fluorescence (UVF) imaging, are used to evaluate the findings. This study is significant as it provides insights into the PID issues in solar PV systems, which can help improve their performance and reliability.
ContributorsMahmood, Farrukh ibne (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Rogers, Bradley (Committee member) / Oh, Jaewon (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2023
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation focused on the development and application of state-of-the-art monitoring tools and analysis methods for tracking the fate of trace level contaminants in the natural and built water environments, using fipronil as a model; fipronil and its primary degradates (known collectively as fiproles) are among a group of trace level emerging environmental contaminants that are extremely potent arthropodic neurotoxins. The work further aimed to fill in data gaps regarding the presence and fate of fipronil in engineered water systems, specifically in a wastewater treatment plant (WWTP), and in an engineered wetland. A review of manual and automated “active” water sampling technologies motivated the development of two new automated samplers capable of in situ biphasic extraction of water samples across the bulk water/sediment interface of surface water systems. Combined with an optimized method for the quantification of fiproles, the newly developed In Situ Sampler for Biphasic water monitoring (IS2B) was deployed along with conventional automated water samplers, to study the fate and occurrence of fiproles in engineered water environments; continuous sampling over two days and subsequent analysis yielded average total fiprole concentrations in wetland surface water (9.9 ± 4.6 to 18.1 ± 4.6 ng/L) and wetland sediment pore water (9.1 ± 3.0 to 12.6 ± 2.1 ng/L). A mass balance of the WWTP located immediately upstream demonstrated unattenuated breakthrough of total fiproles through the WWTP with 25 ± 3 % of fipronil conversion to degradates, and only limited removal of total fiproles in the wetland (47 ± 13%). Extrapolation of local emissions (5–7 g/d) suggests nationwide annual fiprole loadings from WWTPs to U.S. surface waters on the order of about one half to three quarters of a metric tonne. The qualitative and quantitative data collected in this work have regulatory implications, and the sampling tools and analysis strategies described in this thesis have broad applicability in the assessment of risks posed by trace level environmental contaminants.
ContributorsSupowit, Samuel (Author) / Halden, Rolf U. (Thesis advisor) / Westerhoff, Paul (Committee member) / Johnson, Paul C (Committee member) / Arizona State University (Publisher)
Created2015