Doctor of Philosophy

dissertationSupercritical carbon dioxide is injected into underground formations to enhance oil recovery and for subsurface sequestration to minimize the impact of CO2 emissions due to global warming. The complex phase behavior of CO2 with oil determines the effectiveness of the CO2 injection for enhanced oil recovery. The injection of CO2 into the subsurface is also affected by the large and small scale heterogeneities in the formations. These two aspects of CO2 injection are examined in this research. Development of multiple-contact miscibility is important in the success of a carbon dioxide enhanced oil recovery. CO2 displacements are often designed to operate above the minimum miscibility pressure (MMP) to ensure the development of multiple contact miscibility between the oil and CO2. Compositional histories in different parts of a two-dimensional domain are examined in this study in relation to displacement pressure employed. The second part of this dissertation deals with the effect of faults on the CO2 sequestration process and on the integrity of storage. Outcrop-based studies of faulted, aeolian Navajo sandstone provide detailed, quantitative insight regarding the range of fault characteristics that might be encountered as injected CO2 migrates through the faulted aquifer. Faults can act as barriers, conduits, or integrated barrier-conduit systems. Uncertainty in knowing whether a subsurface fault will act as a barrier or conduit leads to uncertainty in evaluating the likelihood for economically sequestering CO2 in sandstone aquifers. EclipseR black oil reservoir simulator is used to explore how different, 3-D, fault-related permeability/porosity structures might impact CO2 injection into, migration through, and leakage from a sequestration aquifer. Sandstone permeability values range from 10s to 1000s of mD. Simulator output shows how fault conduits and barriers can restrict migration of CO2 through the aquifer as a consequence of bypassing (conduits) or compartmentalization (barriers). In addition, the simulation results reveal how the geoscientists' ability to quantify and discriminate between high-permeability versus low-permeability faults in sandstone aquifers can play an important role in designing CO2 sequestration operations

STUDIES ON THE GROWTH, CHARACTERIZATION, PHYSICOCHEMICAL PROPERTIES AND ANTI-BACTERIAL ACTIVITY OF FERULIC ACID CRYSTALS

Objective: The main objective of this research is to study the chosen Ferulic acid for pharmaceutical application through crystal growth technique. The interest of growing crystals helps in studying the physical and chemical properties of the title compound. The computational method provides a detailed interpretation of the compound under study.Methods: Crystallization from solution is a very crucial process in the manufacture of active pharmaceutical ingredients (APIs). Ferulic acid (FA) corresponds to monohydroxylatedcinnamic acid. The biological efficiency of this kind of phenolic systems is expected to be dose-and structure-dependent, which renders the studies for the understanding of their multi-functional biological action. In the present work, ferulic acid crystals were grown using slow evaporation technique. The crystalline nature was revealed from the powder x-ray diffraction technique. The functional groups were determined using FTIR and FT-RAMAN spectra and compared with the theoretical data obtained using computational DFT method. Thermal and physicochemical stability of the grown crystal was examined by Thermogravimetric analysis (TGA) and Differential thermal analysis (DTA) studies. The charge transfer within the molecule was studied with the help of Natural Bond Orbital (NBO) analysis. The anti-bacterial activity was carried out for the title compound using disc diffusion method. The test compounds were screened in vitro for their antibacterial activity against two Gram-positive species (B. cereus and B. substilis) and three Gram-negative species (E. coli, P. vulgaris and S. typhi) of bacterial strains by the disc diffusion method.Results: The grown crystal was pure and crystalline in nature. The functional groups were confirmed by FTIR and FT-Raman analysis. The melting point of the sample was found to be 172 °C. The HOMO-LUMO energy gap was calculated as 3.87eV. The first hyper polarizability was found to be 10.612 x 10-30 esu. The molecular geometry revealed the Cs symmetry of the molecule. NBO analysis confirmed the intramolecular charge transfer from lone pair oxygen atom to Ï€*(C1-C6) and σ*(C17-O19). The compound is dominant for the B. substilis organism which is revealed from the zone of inhibition.Conclusion: The grown Ferulic acid crystals confirmed to have good anti-bacterial activity and the theoretical study proves the biological activity of the compound.Â

STABILITY INDICATING RP-LC ASSAY METHOD FOR CARISOPRODOL

Objective: A reverse phase stability-indicating HPLC method was developed for the determination of Carisoprodol in pharmaceutical dosage forms. The chromatographic elution was achieved on C18, 250 mm × 4.6 mm, 5-μm particle size column.Methods: The mobile phase contains a mixture of water and acetonitrile in ratio of 60:40 v/v. The flow rate was 1.0 ml min-1 and was detected by Refractive index detector.Results: The method was proven to be linear over a range of 1 to 4 mg/ml with a mean correlation coefficient of 0.99998. The %mean recovery is in the range of 100.55% to 101.11% and %RSD was less than 1.0% between preparations. The % RSD for Assay results of initial sample preparation in different intervals of 0hr, 24 h, 30 h and 48 h was less than 1.0%. To establish stability-indicating capability of the method, drug product was subjected to the stress conditions of acid, base, oxidative, hydrolytic, thermal and photolytic degradation. The degradation products were well resolved from Carisoprodol.Conclusion: The developed method was validated as per international ICH guidelines with respect to specificity, linearity, accuracy, precision and robustness

Neural network modeling of convection heat transfer coefficient for the casson nanofluid

This paper presents applications of Artificial Neural Network (ANN) to develop a mathematical model of magnetohydrodynamic (MHD) flow and heat transfer in a Casson nanofluid. The model equations are solved numerically by Runge-Kutta Fehlberg method with shooting technique. In the developing ANN model, the performance of the various configuration were compared with various types of errors such as Mean Square Error (MSE), Mean Absolute Error (MAE) and Sum Square Error (SSE). The best ANN configuration incorporated two hidden layers with twenty five neurons in each hidden layer was able to construct convective heat transfer coefficients with MSE, MAE and SSE of 0.006346, 0.009813 and 1.015423%, respectively, and had R² of 0.741516. A good co-relation has been obtained between the predicted results and the numerical values.Publisher's Versio

An Enhanced Neural Graph based Collaborative Filtering with Item Knowledge Graph

Recommendation system is a process of filtering information to retain buyers on e-commerce sites or applications. It is used on all e-commerce sites, social media platform and multimedia platform. This recommendation is based on their own experience or experience between users. In recent days, the graph-based filtering techniques are used for the recommendation to improve the suggestions and for easy analysing. Neural graph based collaborative filtering is also one of the techniques used for recommendation system. It is implemented on the benchmark datasets like Yelp, Gowalla and Amazon books. This technique can suggest better recommendations as compared to the existing graph based or convolutional based networks. However, it requires higher processing time for convolutional neural network for performing limited suggestions. Hence, in this paper, an improved neural graph collaborative filtering is proposed. Here, the content-based filtering is performed before the collaborative filtering process. Then, the embedding layer will process on both the recommendations to provide a higher order relation between the users and items. As the suggestion is based on hybrid recommendation, the processing time of Convolutional neural network is reduced by reducing the number of epochs. Due to this, the final recommendation is not affected by the smaller number of epochs and also able to reduce its computational time. The whole process is realized in Python 3.6 under windows 10 environment on benchmark datasets Go Walla and Amazon books. Based on the comparison of recall and NDCG metric, the proposed neural graph-based filtering outperforms the collaborative filtering based on graph convolution neural network

A Study on Graph Theory of Path Graphs

In a simple graph the G is define as G = (V, E), here V is known as non-empty set of vertices and E is consider as edges. It is the set of unordered combination of unique elements of V. A simple graph has their points of confinement in demonstrating this present reality. Rather, we use multigraphs, which comprise of vertices and undirected edges between these vertices, with various edges between sets of vertices permitted. In this field of diagram hypothesis, a path graph or straight diagram is a graph whose vertices can be recorded in the request to such an extent that the edges are the place I = 1, 2, … , n ? 1. Proportionally, a way with in any event two vertices is associated and has two terminal (vertices that have degree 1), while all others (assuming any) have degree 2. The path graph of a diagram G is acquired by depicting the path in G by vertices and joining two vertices when the comparing path in G structure a path or a cycle The path graph of a graph G is obtained by describing the paths in G by vertices and joining two vertices when the corresponding paths in G form a path or a cycl

Contemporary Approach for Technical Reckoning Code Smells Detection using Textual Analysis

Software Designers should be aware of address design smells that can evident as results of design and decision. In a software project, technical debt needs to be repaid habitually to avoid its accretion. Large technical debt significantly degrades the quality of the software system and affects the productivity of the development team. In tremendous cases, when the accumulated technical reckoning becomes so enormous that it cannot be paid off to any further extent the product has to be abandoned. In this paper, we bridge the gap analyzing to what coverage abstract information, extracted using textual analysis techniques, can be used to identify smells in source code. The proposed textual-based move toward for detecting smells in source code, fabricated as TACO (Textual Analysis for Code smell detection), has been instantiated for detecting the long parameter list smell and has been evaluated on three sampling Java open source projects. The results determined that TACO is able to indentified between 50% and 77% of the smell instances with a exactitude ranging between 63% and 67%. In addition, the results show that TACO identifies smells that are not recognized by approaches based on exclusively structural information

Supramolecular gels: functions and uses

In recent years there has been immense interest in studying gels derived from low molecular mass gelators (supramolecular, or simply molecular gels). The motivation for this is not only to understand the fundamental aggregate structures in the gels at different length scales, but also to explore their potential for futuristic technological applications. Gels have been made sensitive to external stimuli like light and chemical entities by incorporating a spectroscopically active or a receptor unit as part of the gelator molecule. This makes them suitable for applications such as sensing and actuating. The diversity of gel structural architectures has allowed them to be utilized as templates to prepare novel inorganic superstructures for possible applications in catalysis and separation. Gels derived from liquid crystals (anisotropy gels) that can act as dynamically functional materials have been prepared, for example, for (re-writable) information recording. Supramolecular gels can be important in controlled release applications, in oil recovery, for gelling cryogenic fuels etc. They can also serve as media for a range of applications. This tutorial review highlights some of the instructive work done by various groups to develop smart and functional gels, and covers a wide spectrum of scientific interest ranging from medicine to materials science