The impact of reservoir properties on mixing of inert cushion and natural gas in storage reservoirs

Underground natural gas storage is a process which effectively balances a variable demand market with a nearly constant supply of energy provided by the pipeline system. Cushion gas in storage reservoirs provide the necessary pressure for the withdrawal of working gas and it makes up for the largest part of investments in underground storage reservoirs. Usually, a part of the cushion gas is replaced by an inert gas such as Nitrogen in order to reduce the investment costs. Due to this replacement there might be problems caused by the mixing of inert cushion and natural gas, which is a cause of concern as it affects the quality of the natural gas.;In this study, the inert gas mixing problem is investigated in storage reservoirs by using a reservoir simulator based on the equation of state. The degree of mixing between the two dissimilar gases was found by the amount of inert cushion that is produced along with the withdrawn gas. Reservoir parameters like Volume of inert cushion, porosity, permeability, reservoir pressure and reservoir temperature were varied to study their effect on mixing.;It has been found that reservoir parameters porosity, permeability and temperature do not impact the degree of mixing to a great extent. However, reservoir pressure has a slight impact on the mixing between these two dissimilar gases. It is evident from these simulations that the degree of mixing is purely a function of withdrawal rate and also the percentage of inert cushion in the storage reservoir to a great extent

Development of a PDMS Based Micro Total Analysis System for Rapid Biomolecule Detection

The emerging field of micro total analysis system powered by microfluidics is expected to revolutionize miniaturization and automation for point-of-care-testing systems which require quick, efficient and reproducible results. In the present study, a PDMS based micro total analysis system has been developed for rapid, multi-purpose, impedance based detection of biomolecules. The major components of the micro total analysis system include a micropump, micromixer, magnetic separator and interdigitated electrodes for impedance detection. Three designs of pneumatically actuated PDMS based micropumps were fabricated and tested. Based on the performance test results, one of the micropumps was selected for integration. The experimental results of the micropump performance were confirmed by a 2D COMSOL simulation combined with an equivalent circuit analysis of the micropump. Three designs of pneumatically actuated PDMS based active micromixers were fabricated and tested. The micromixer testing involved determination of mixing efficiency based on the streptavidin-biotin conjugation reaction between biotin comjugated fluorescent microbeads and streptavidin conjugated paramagnetic microbeads, followed by fluorescence measurements. Based on the performance test results, one of the micromixers was selected for integration. The selected micropump and micromixer were integrated into a single microfluidic system. The testing of the magnetic separation scheme involved comparison of three permanent magnets and three electromagnets of different sizes and magnetic strengths, for capturing magnetic microbeads at various flow rates. Based on the test results, one of the permanent magnets was selected. The interdigitated electrodes were fabricated on a glass substrate with gold as the electrode material. The selected micropumps, micromixer and interdigitated electrodes were integrated to achieve a fully integrated microfluidic system. The fully integrated microfluidic system was first applied towards biotin conjugated fluorescent microbeads detection based on streptavidin-biotin conjugation reaction which is followed by impedance spectrum measurements. The lower detection limit for biotin conjugated fluorescent microbeads was experimentally determined to be 1.9 x 106 microbeads. The fully integrated microfluidic system was then applied towards immuno microbead based insulin detection. The lower detection limit for insulin was determined to be 10-5M. The total detection time was 20 min. An equivalent circuit analysis was performed to explain the impedance spectrum results

Scalable learning for geostatistics and speaker recognition

With improved data acquisition methods, the amount of data that is being collected has increased severalfold. One of the objectives in data collection is to learn useful underlying patterns. In order to work with data at this scale, the methods not only need to be effective with the underlying data, but also have to be scalable to handle larger data collections. This thesis focuses on developing scalable and effective methods targeted towards different domains, geostatistics and speaker recognition in particular. Initially we focus on kernel based learning methods and develop a GPU based parallel framework for this class of problems. An improved numerical algorithm that utilizes the GPU parallelization to further enhance the computational performance of kernel regression is proposed. These methods are then demonstrated on problems arising in geostatistics and speaker recognition. In geostatistics, data is often collected at scattered locations and factors like instrument malfunctioning lead to missing observations. Applications often require the ability interpolate this scattered spatiotemporal data on to a regular grid continuously over time. This problem can be formulated as a regression problem, and one of the most popular geostatistical interpolation techniques, kriging is analogous to a standard kernel method: Gaussian process regression. Kriging is computationally expensive and needs major modifications and accelerations in order to be used practically. The GPU framework developed for kernel methods is extended to kriging and further the GPU's texture memory is better utilized for enhanced computational performance. Speaker recognition deals with the task of verifying a person's identity based on samples of his/her speech - "utterances". This thesis focuses on text-independent framework and three new recognition frameworks were developed for this problem. We proposed a kernelized Renyi distance based similarity scoring for speaker recognition. While its performance is promising, it does not generalize well for limited training data and therefore does not compare well to state-of-the-art recognition systems. These systems compensate for the variability in the speech data due to the message, channel variability, noise and reverberation. State-of-the-art systems model each speaker as a mixture of Gaussians (GMM) and compensate for the variability (termed "nuisance"). We propose a novel discriminative framework using a latent variable technique, partial least squares (PLS), for improved recognition. The kernelized version of this algorithm is used to achieve a state of the art speaker ID system, that shows results competitive with the best systems reported on in NIST's 2010 Speaker Recognition Evaluation

Growth and Characterization of Anthranilic acid Crystals

ABSTRACTSingle crystals of Anthranilic acid (AA) have been successfully grown and purity of materials has been increased by repeated recrystallization process. Single crystals have been grown by slow evaporation technique. The grown crystal was characterized by Single crystal X-Ray diffraction, Powder XRD, FTIR, UV-Vis, DTA/TGA, Dielectric studies and SHG respectively. The observed results from various characterization show the suitability of NLO application. The second harmonic generation of the grown crystal was checked using Kurtz and Perry technique. Thermal stability and melting point of the grown crystal were found by thermal analysis. The Physical strength of the grown AA crystal was measured from Vicker’s hardness test.Â