Techno-economic assessment of sour gas oxy-combustion water cycles for CO2 capture

Growing energy demand coupled with the threat of global warming call for investigating alternative and unconventional energy sources while reducing CO2 emissions. One of these unconventional fuels is sour gas, which consists of methane, hydrogen sulfide and carbon dioxide. Using this fuel poses many challenges because of the toxic and corrosive nature of its combustion products. A promising technology for utilizing it is oxy-fuel combustion with carbon capture and storage, including the potential of enhanced oil recovery for added economic benefits. Although methane oxy-fuel cycles have been studied in the literature, using sour gas as the fuel has not been investigated or considered. In this paper, water is used as the diluent to control the flame temperature in the combustion process, and the associated cycle type is modeled to examine its performance. As the working fluid condenses, sulfuric acid forms which causes corrosion. Therefore, either expensive acid resistant materials should be used, or a redesign of the cycle is required. These different options are explored. A cost analysis of the proposed systems is also conducted to provide preliminary estimates for the levelized cost of electricity (LCOE). The results show the acid resistance cycle with a 4.5% points increase in net efficiency over the cycle with SO[subscript x] removal. However there is nearly a 9% decrease in the cycle's LCOE for the latter case.Aspen Technology, Inc

Self-Consistent C-V Characterization of Depletion Mode Buried Channel InGaAs/InAs Quantum Well FET Incorporating Strain Effects

We investigated Capacitance-Voltage (C-V) characteristics of the Depletion Mode Buried Channel InGaAs/InAs Quantum Well FET by using Self-Consistent method incorporating Quantum Mechanical (QM) effects. Though the experimental results of C-V for enhancement type device is available in recent literature, a complete characterization of electrostatic property of depletion type Buried Channel Quantum Well FET (QWFET) structure is yet to be done. C-V characteristics of the device is studied with the variation of three important process parameters: Indium (In) composition, gate dielectric and oxide thickness. We observed that inversion capacitance and ballistic current tend to increase with the increase in Indium (In) content in InGaAs barrier layer.Comment: 5 pages, ICEDSA conference 201

Self Consistent Simulation of C-V Characterization and Ballistic Performance of Double Gate SOI Flexible-FET Incorporating QM Effects

Capacitance-Voltage (C-V) & Ballistic Current- Voltage (I-V) characteristics of Double Gate (DG) Silicon-on- Insulator (SOI) Flexible FETs having sub 35nm dimensions are obtained by self-consistent method using coupled Schrodinger- Poisson solver taking into account the quantum mechanical effects. Although, ATLAS simulations to determine current and other short channel effects in this device have been demonstrated in recent literature, C-V & Ballistic I-V characterizations by using self-consistent method are yet to be reported. C-V characteristic of this device is investigated here with the variation of bottom gate voltage. The depletion to accumulation transition point (i.e. Threshold voltage) of the C-V curve should shift in the positive direction when the bottom gate is negatively biased and our simulation results validate this phenomenon. Ballistic performance of this device has also been studied with the variation of top gate voltage.Comment: 4 pages, ICEDSA 2012 conferenc

In_xGa_{1-x}Sb MOSFET: Performance Analysis by Self Consistent CV Characterization and Direct Tunneling Gate Leakage Current

In this paper, Capacitance-Voltage (C-V) characteristics and direct tunneling (DT) gate leakage current of antimonide based surface channel MOSFET were investigated. Self-consistent method was applied by solving coupled Schr\"odinger-Poisson equation taking wave function penetration and strain effects into account. Experimental I-V and gate leakage characteristic for p-channel InxGa1-xSb MOSFETs are available in recent literature. However, a self- consistent simulation of C-V characterization and direct tunneling gate leakage current is yet to be done for both n- channel and p-channel InxGa1-xSb surface channel MOSFETs. We studied the variation of C-V characteristics and gate leakage current with some important process parameters like oxide thickness, channel composition, channel thickness and temperature for n-channel MOSFET in this work. Device performance should improve as compressive strain increases in channel. Our simulation results validate this phenomenon as ballistic current increases and gate leakage current decreases with the increase in compressive strain. We also compared the device performance by replacing InxGa1-xSb with InxGa1-xAs in channel of the structure. Simulation results show that performance is much better with this replacement.Comment: 7 pages, EIT 2012 IUPUI conferenc

A Physically Based Analytical Modeling of Threshold Voltage Control for Fully-Depleted SOI Double Gate NMOS-PMOS Flexible-FET

In this work, we propose an explicit analytical equation to show the variation of top gate threshold voltage with respect to the JFET bottom gate voltage for a Flexible Threshold Voltage Field Effect Transistor (Flexible-FET) by solving 2-D Poisson's equation with appropriate boundary conditions, incorporating Young's parabolic approximation. The proposed model illustrates excellent match with the experimental results for both n-channel and p-channel 180nm Flexible-FETs. Threshold voltage variation with several important device parameters (oxide and silicon channel thickness, doping concentration) is observed which yields qualitative matching with results obtained from SILVACO simulations.Comment: 4 pages, EIT 2012-IUPUI conferenc

Traffic Congestion Prediction using Deep Convolutional Neural Networks: A Color-coding Approach

The traffic video data has become a critical factor in confining the state of traffic congestion due to the recent advancements in computer vision. This work proposes a unique technique for traffic video classification using a color-coding scheme before training the traffic data in a Deep convolutional neural network. At first, the video data is transformed into an imagery data set; then, the vehicle detection is performed using the You Only Look Once algorithm. A color-coded scheme has been adopted to transform the imagery dataset into a binary image dataset. These binary images are fed to a Deep Convolutional Neural Network. Using the UCSD dataset, we have obtained a classification accuracy of 98.2%