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Development of Deep Learning Algorithm for Remote Sensing Image Classifications to Assess the Impacts of Coal Mining on Land Use/Land Cover Patterns
No full textAccurate classification of the satellite image is always a challenging task, and hence uncertainty involves in the change detection analysis in the land use/land cover (LULC) for mining regions. The present study attempts to demonstrate the development of an optimized deep convolutional neural network (DCNN) using Linear imaging self-scanning sensor-IV (LISS-IV) data for the classification of satellites image to assess the LULC pattern in a mining region. The primary requirement of the DCNN model development is an image dataset for different LULC types for training and validation of the model. In this study, an image dataset was initially prepared from the LISS-IV satellite image of the study region for optimized model development, and thereafter the model was tested with the Landsat dataset. The common LULC types identified in the specified mining regions are Barren Land (BL), Built-Up Area (BA), Coal Mining Region (CM), Vegetation (VE), and Waterbody (WB), respectively. The false-color composite (FCC) of three bands [B2 (Green): 0.52 - 0.59 μm, B3 (Red): 0.62 - 0.68 μm, and B4 (Near-infrared): 0.77- 0.86 μm] was used for extracting the image database. The image databases were prepared for three different benchmarks or dimensions (6×6, 12×12, and 24×24) for examining the effect of input image size on model performances. The number of image samples derived for each LULC type is 1250. Thus, a total of 6250 image samples of five classes (1250 for each class) were derived and labelled with class numbers and types. Out of 6250 LISS-IV image datasets, 70% of the data was assigned for training, and the rest 30% was assigned for validation of the DCNN model. The study examined the performances of the model using three optimizers including the Adaptive Moment Estimation (ADM), Root Mean Square Propagation (RMSPro), and Stochastic Gradient Descent with Momentum (SGDM) for identifying the best one by fixing the values of hyperparameters. Moreover, the values of the learning rate, momentum, minibatch size, maximum epoch, learning rate drop factor, learning rate drop period, L2Regularization, verbose frequency, and validation frequency were fixed as 0.001, 0.1, 10, 0.95, 0.0001, 300, 128, 50, and 30, respectively in the model for identifying the best optimizer. The performances of the DCNN model were found to be 99.04%, 97.1%, and 97.5% respectively with SGDM, RMSProp, and ADAM. The results indicate that the SGDM optimizer offers the highest accuracy as compared to others with the specified model hyperparameters and thus SGDM optimizer was further used for the optimization of the model hyperparameters through sensitivity analysis. The DCNN model hyperparameters were tuned in terms of the learning rate, epoch number, batch size, and momentum to obtain the best output. The model offers satisfactory results in terms of accuracy for both the training dataset (=99.04%) and the validation dataset (=87.50%). The classification accuracy was further evaluated on the testing dataset by randomly choosing the samples through visualization of the Google Earth Image Pro of the same time frame of the study area. Additionally, the model performances were measured using the six indices (accuracy, error, precision, recall, F1-score, and MCC), which were derived from the confusion matrix parameters (true positive, false positive, true negative, and false negative). After successful testing of the model with the LISS IV dataset, the performance of the same was further evaluated using Landsat images. For this, a new image database of (6 × 6) size was prepared from Landsat image for evaluating the optimized DCNN model. In this case, the total number of image samples extracted was the same as that used for LISS IV data. The results indicate that the DCNN model offers better accuracy with the Landsat data (training dataset: 94.73% and validation dataset: 89.03%). Moreover, the long-term data of the Landsat sensor are freely available and thus can be used for time-series analysis. Detection and delineation of coal mining regions using remote sensing data is a challenging task as the characteristics of the surface features are very close to barren lands. Thus, the study also examined the efficacy of the optimized DCNN model in the detection and delineation of coal mining regions (Jharia Coalfield). The study examined the effect of the image size of the training database on model performances. The model performances were tested using three different image size databases [DB6 ∈ (6×6), DB12 ∈ (12×12), and DB24 ∈ (24×24)]. The results indicated that the classification accuracies with DB6, DB12, and DB24 training and validation datasets are nearly the same (>99%) in each case but the boundary delineation with lower size image training dataset was more smooth. Therefore, the dataset of DB6 ∈ (6×6) was used for further study. The third objective of the study is to make a comparative evaluation of the DCNN and DNN-based LULC classification of mining regions using multi-sensors (LISS-IV, Landsat-8, and Sentinel-2A) fused data. The study designed DCNN and deep neural network (DNN) algorithms for LULC classification of the multi-sensor fused image, respectively. A discrete cosine transform (DCT) with a spatial correlation technique was used to derive the fused data from three different satellite sensor data. LULC for the specified region was classified into the same five broad categories including, barren land, built-up area, coal mining region, vegetation, and waterbody. The results reveal that the DCNN model consistently outperforms the DNN model, showcasing accuracy, error rates, precision, and recall ranging from 99.83% to 99.99%, 0.01% to 0.17%, 99.52% to 99.99%, and 99.40% to 99.99% on the training dataset, and 99.50% to 99.99%, 0.01% to 0.50%, 98.35% to 99.99%, and 98.33% to 99.99% on the validation dataset, respectively. In comparison, the DNN model demonstrates values ranging from 90.36% to 99.90%, 0.01% to 9.64%, 75.10% to 99.53%, and 66.99% to 99.99% on the training dataset, and 88.50% to 99.94%, 0.06% to 11.50%, 72.25% to 99.66%, and 62.50% to 99.99% on the validation dataset. These findings showed that the DCNN classification algorithm outperforms the DNN classification algorithm. Moreover, the comparative performances of the DCNN model with different datasets indicate that the model with fused images outperformed the model with individual sensor images. The last objective of the study is to make a time-series analysis of satellite data through transfer learning for assessing the impacts of coal mining activities on LULC change. The long-term impacts of mining activities in Jharia coalfield (JCF) on LULC patterns using transfer learning of the DCNN model. The study used three bands (Band 3, Band 5, Band 7 of Landsat 5 and Landsat 7, and Band 4, Band 6, Band 7 of Landsat 8) of Landsat series data from 1987 to 2021 at an interval of two years for time-series analysis. A new image database with a large number of image samples was prepared from the Landsat series data for generating the base model using optimized model hyperparameters. A total of 2000 image samples of 6×6 size were prepared for each class for base model development to classify the LULC into five different classes (barren land, built-up area, coal mining region, vegetation, and waterbody), and thus the total number of image samples was 10000. The image database was partitioned into training and validation of the proposed DCNN model in the ratio of 7: 3. The study results revealed that the model offers an accuracy level of 95% and 88 % on the training and the validation dataset, respectively. The base model learning algorithm was subsequently transferred for classifying the time-series Landsat data to evaluate the long-term impacts of mining activities on land-use patterns. The results indicate that barren land, coal mining region, and waterbody have decreased from 237.30 sq. km. (=39.88 %) to 171.25 sq. km (=28.78 %), 118.77 sq. km. (=19.96 %) to 68.73 sq. km (=11.55 %), and 35.58 sq. km (=5.98 %) to 18.68 sq. km (=3.14 %) during 1987 to 2021, respectively. On the other hand, the built-up area and vegetation have increased from 120.14 sq. km (=20.19 %) to 233.02 sq. km (=39.16 %) and 83.19 sq. km (=13.98 %) to 103.36 sq. km (=17.37 %) during 1987 to 2021. The study also analyzed the time-series correlation to understand the sensitivity of transforming one land-use type into other. The time-series correlation results indicate that coal mining is the most sensitive land use type from 1987 to 2021, whereas barren land is least sensitive up to 2011, and thereafter vegetation is the least sensitive LULC class
CoFe2O4/Cr2O3 Nanoparticles: A Candidate for Room-temperature and above Magnetoelectricity
No full textIn response to the recent scientific and technological interest, and to meet the need for increased power efficiency, miniaturization, and reduced manufacturing costs, new materials need to be considered. Potential candidates include complex oxides that exhibit magnetoelectric (ME) coupling, the ability to control magnetization through the applied electric field, and conversely influence the electric polarization by the application of the magnetic field. They usually are materials with new and enhanced physical and chemical properties combined in one single multi-functional platform. Moreover, materials must exhibit room temperature and above magnetoelectricity if they are to be utilized for practical applications. In this regard, through appropriate material selection, multi-phase materials exhibit enhanced ME coupling, usually absent or found at cryogenic temperature with a lower magnitude in single-phase systems. In this thesis, CoFe2O4 (CFO)/Cr2O3 (CRO) nanoparticles are investigated for their structural, magnetic, electric, and magnetoelectric properties. This thesis work provides an effective approach to increase the ME operating temperature of primordial sesqui oxide CRO. The bi-magnetic CFO/CRO nanoparticles having core/shell architectures as well as in the form of composites are synthesized via the sol-gel auto-combustion method by varying relative molar concentrations of CFO and CRO phases. The presence of distinct X-ray diffraction (XRD) peaks in all of the prepared bi-phasic systems corresponds to the cubic structure of CFO having space group Fd-3m (No. 227) and the rhombohedral structure of CRO with space group R-3c (No. 167). The micro-strain induced in the CFO (compressive strain) and CRO (tensile strain) phases have been validated from the Rietveld refinement of XRD data, and are complementary to the Fourier transform infrared spectroscopy and Raman spectroscopy studies. Field emission scanning electron micrographs display the homogeneous distribution of spherical shape particles with average particle sizes of ~100-120 nm. Transmission electron microscopy measurement confirms the core/shell configuration of the nanoparticles. The magnetic properties of CFO/CRO nanoparticles are significantly different in many aspects from the bare CFO nanoparticles, caused by the magnetic proximity effect. The thermal variations in magnetization measurements reveal a considerable decrease in ferrimagnetic transition temperature (TC) for the nanocomposite samples compared to bare CFO. The magnetic field variation in magnetization measurements suggests screening of ferromagnetic interaction of CFO (core) due to CRO shell over it, such that core/shell nanoparticles respond like single domain particles. A careful inspection of the impedance and modulus data suggests single relaxation in the studied frequency/temperature range for all the compositions and are well described by the impedance-based Havriliak-Negami expression. Both, the relaxation and the conduction processes are found to be polaronic, obeying Mott variable range hopping mechanism. The relaxation spectra are found to be significantly affected by the applied magnetic field, thereby manifesting the signature of the ME effect. Direct ME measurements on nanocomposites display linear ME responses up to ~350 K, above which it is dominated by magnetoconductivity/magnetoloss which results in a ‘U’ shaped nature with negligible magnetoelectricity. Nonetheless, core/shell samples exhibit the presence of linear magnetoelectricity for temperatures as high as 400 K ―a hallmark of enhancement in the ME operating temperature of the parental CRO phase. The results presented in this thesis elucidate the role of magnetic proximity effects on enhancing the magnetoelectric operating temperature and may provide insight into the development of new room temperature and above magnetoelectric devices whose capacity for storage, speed, and energy efficiency may surpass the current state-of-the-art
Effect of Tool Material and Lubrication Condition on Machinability of Ti64 Alloy
No full textUndoubtedly, extensive application of Ti64 alloy in numerous application areas (such as defense and aerospace; petrochemical refineries, marine industries, chemical and biomedical applications, etc.) invites many challenges both for the manufacturing industries as well as the research community. This alloy is experienced as ‘difficult-to-cut’ mostly during conventional machining. The poor thermal property of this alloy is, however, responsible for premature failure of cutting tools as the cutting zone instigates evolution of huge amount of heat; although strength of the alloy remains unaltered. Therefore, application of coolants or Metal Working Fluids (MWFs) comes into picture to reduce cutting zone temperature thereby improving tool life and machined surface integrity. Keeping this in mind, initial machining trials are carried out employing MWFs by different delivery strategies where conventional uncoated carbide tool is used. First set of experiments incorporate dry machining (longitudinal turning), machining under supply of pressurized air and distilled water based Minimum Quantity Lubrication (MQL). Along with tool-tip temperatures, characteristic features of spatial temperature distribution profiles at the vicinity of tool-tip, severity of induced-vibrations (at varying cutting speeds as well as cooling media) and wear progression in cutting tools are studied. The results obtained from the experiments witness beneficial effects of water-MQL medium for machining of Ti64 as noticeable reductions in tool-tip temperature, amplitude (absolute value) of acceleration of vibrations and tool wear are observed. Though water possesses higher heat capacity than oils; supplied water (under MQL) is likely to be vaporized easily at the machining induced temperatures causing poor heat removal from the cutting zone. In order to take care of several alarming issues (including vaporization of MWFs, environmental protection and occupational health hazards), the second set of experiments explores application feasibility of vegetable oil (Jatropha oil) as MWF for machining of Ti64. Non-edible Jatropha oil is used as the base fluid both for MQL and nanofluid MQL (NFMQL) conditions due to its biodegradability. Graphene nanoplatelets are dispersed within Jatropha oil to prepare nanofluids. During machining, assisted by MQL and NFMQL both, tool wear morphology reveals existence of ‘unaffected zones’ which clearly indicate sustenance of strong hydrodynamic tribo-film thus protecting localized portions of tool surface against wear. Apart from this, cutting force magnitude, tool-tip temperature, chip’s macro/ micro-morphology and surface roughness of the machined work part, etc., are studied in detail. Inadequate penetration of MWFs to the core machining sites, especially at higher cutting speeds (due to faster pace of evolved chips), and sedimentation/ local agglomeration of nano-additives (due to lack of suitable surfactant addition and improper dispersion within base fluid) are some of the major causes for which application of MWFs gradually becomes to be unpopular for industrial practices. Therefore, emphasis is given towards dry machining of Ti64 which requires momentous attention for improvement in tooling system. Selection of compatible tool material and coatings, appropriate tool geometrical parameters and precise control of cutting parameters are of vital importance towards achieving satisfactory machining yield under dry cutting condition. In this context, performances of MT CVD TiCN-Al2O3 bi-layered coated carbide, PVD TiN-TiCN-TiN multi-layered coated cermet and CVD TiCN-Al2O3 bi-layered coated SiAlON inserts are studied first during dry machining of Ti64 within cutting speed range of 50-130 m/min; at constant feed ~ 0.1 mm/rev and depth-of-cut ~ 0.35 mm. It is experienced that cermet tool performs better than remaining two counterparts in purview of lower tool-tip temperature, reduced tool flank wear and better machined surface integrity. Secondly, performances of MT CVD TiCN-Al2O3-TiOCN multi-layered coated carbide and PVD TiN single layered coated composite ceramic (Al2O3/ TiCN) inserts are compared during dry machining of Ti64. Though coating peel-off and tool flaking are witnessed when using ceramic insert; it exhibits lower tool flank wear than carbide tool up to ~ v = 130 m/min (v refers to the cutting speed). Finally, application feasibility of HSN2 coated carbide tool (as compared to uncoated counterpart) is unfolded during dry machining of Ti64 by analyzing cutting force components, tool-tip temperature, tool wear morphology, morphology of evolved chips and machined surface quality. With lesser machining forces, HSN2 (TiAlxN supernitride) coated tool endorses about 20.45 % reduction in tool-tip temperature at the highest cutting speed (v = 146 m/min) which causes reduced severity of wear modes of the cutting tool. This helps to produce superior machined surface associated with better surface integrity when compared to that of using uncoated counterpart
Aluminosilicate Waste derived Novel Zeolite A based Nanocomposite for Treating Wastewater
No full textRapid industrialization without sufficient emphasis on ecologically sound practices contributes significantly to solid refuse and water pollution. The current waste management system mainly focuses on end-of-pipe solutions and short-term effects, which may be reactive but fail to achieve sustainability. The Cradle-to-Cradle approach promotes a transition from the linear model of "take-make-dispose" to a circular model that encourages resource conservation and regeneration. Thus priority is to move towards a circular economy approach in waste management by following the steps (i) reclamation of solid industrial waste (such as fly ash and red mud) into useful zeolite material by recovering resources within wastes, (ii) reduction of organic and inorganic contaminant by green technologies without involving in any secondary pollution, (iii) treated water can be recycled into the environment protecting its safeguards. This research contributes to a more sustainable future by reducing waste, conserving resources, and minimizing the negative environmental impacts of waste pollutants. The entire thesis work is divided into four major chapters. The first part of the thesis is the sustainable synthesis procedure of zeolite 4A utilizing resources from industrial solid wastes without adding external chemical precursors and its structural and morphology control optimization study using a central composite design. High-modulus silicate and aluminate are extracted from fly ash and red mud, abundantly found in the Eastern part of India, Odisha, by low-temperature alkali fusion followed by ultrasonication. The colloidal aluminosilicate sol to a stable crystal of zeolite 4A by the hydrothermal method was investigated to observe morphology changes and tentative crystal growth mechanism. A highly pure zeolite was obtained under the optimized FA/RM extract=1.02, crystallization temperature=90°C, crystallization time=10.25hrs. Three different types of surface morphology of zeolite 4A are observed sharp edge cube when FA/RM extract is higher than 1.02 (Run 19), truncated edge cube when FA/RM extract is equal to 1.02 (Run 2), and rounded edge cube when FA/RM extract is less than 1.02 (Run 18). From the desirability function evaluation, we can conclude that the desired goal of maximum crystallinity has been achieved by optimizing three independent variables at a 95% confidence level. The next objective of the thesis is to synthesize a novel core-shell nanocomposite for multiple refractory organic in wastewater using waste-derived zeolite A as core material. We developed a novel core-shell zeolite A@oxygen-deficient ZnO (ZA@ZnO1-X) nanocomposite for the degradation of PAHs mixture containing fluorene, phenanthrene, and anthracene. The hierarchical structure helps to reduce the recombination of photogenerated electron and holes pair, thereby availing more active species for photodegradation. The enhanced photodegradation efficacy is due to the synergistic effect of oxygen-defect sites in ZA@ZnO1-X and photoactive species. Core-shell ZA@ZnO1-X shows improved catalytic properties with a band gap value of 2.65eV lower than the sole ZnO nanosheet (3.03eV). The porous shell of the ZnO1-X structure provides an enhanced adsorption site and fully utilizes photon energy (visible light) with no aggregation because of ZA support. The maximum photocatalytic degradation is achieved by optimizing reaction parameters of 96%, 95.1%, and 93% of fluorene (0.02436min-1), phenanthrene (0.02421min-1), and anthracene (0.02102min-1), respectively in 2 hours at neutral pH and catalyst load 1g/l. The primary active species responsible for PAHs degradation is h+, followed by HO•, O2•- radicals analyzed by quenching experiment. The degradation intermediates and degraded products analysis by GC-MS gives a plausible general reaction pathway of PAHs and reveals primary intermediate is a phthalate derivative. The regeneration of active catalytic sites in the ZA@ZnO1-X photocatalyst shows its stability and reusability over five consecutive cycles. The subsequent section of the thesis involves toxic heavy metal Chromium removal of both state Cr(VI) and Cr(III) by ZA@ZnO1-X nanocomposite. ZA@ZnO1-X has removed 98.3% of total Cr, achieving a discharge limit of 0.05ppm of Cr(VI) rather than transforming it to less harmful Cr(III) after the photoreduction process. The effect on photocatalytic reduction efficiency (PRE) is systematically studied on varying operational variables like pH, citric acid and initial Cr(VI) concentration, and catalyst load. ZA@ZnO1-X removed Cr(VI) (2.5 ppm) and photo-reduced Cr(III) simultaneously by 98.5% PRE with catalyst load 0.4g/100ml, CA=5mM, pH=5.06 under halogen light irradiation(300W, 240V) for 50mins. This experimental study evaluates the prominent role of oxygen vacancy of ZA@ZnO1-X for photocatalytic reduction of Cr(VI) and enhanced adsorption of Cr(III). The kinetic study reveals adsorption of both Cr(VI) and Cr(III) follows pseudo-second-order kinetics, whereas photoreduction of Cr(VI) follows pseudo-first-order kinetics. A possible mechanism of total Cr removal is sketched, supporting enhanced adsorption due to unsaturated Zn atoms and unpaired e- at the oxygen defect site, followed by photoreduction by photogenerated e- and CO2-• radical. The ZA@ZnO1-X performance is remarkable (reduced by 2.5%) after five consecutive runs without deformation. The easy regeneration process makes it suitable for toxic total Cr removal, avoiding any secondary pollution. In the final objective, hybrid technology has been studied using a fabricated membrane photoreactor in a continuous mode of operation on a bench scale. The work is based on the synergistic effect of photocatalyst, microbubble, and membrane separation for enhanced degradation of Bismarck Brown R, which is otherwise very low in the standalone process. The fabricated bench-scale membrane setup is of submerged type, a modified tubular membrane with the novel ZA@ZnO1-X nanocomposite, and detailed analytical characteristics are investigated. The thin layer coating (average thickness 24.6μm) of oxygen-deficient ZnO1-X photocatalyst gives a high photocatalytic efficiency under visible light without hampering the rejection rate. Here, the hybrid process treated Bismarck Brown R contaminated wastewater in the prototype setup with simulated and natural wastewater collected from a local dying factory. The operational parameters such as Bismarck Brown R concentration, solution pH, temperature, and flux were varied and analyzed optimized reaction conditions favorable for actual wastewater treatment. The hybrid ZA@ZnO1-X nanocomposite-based tubular membrane improved degradation to 95.4% decolorization and 94% COD removal rate in 90 mins at pH 8.15, solution flux 120 ml/min, and temperature 30°C. The scavenging experiment resolved responsible reactive species for organic dye degradation: holes and HO• radical. The tentative mechanistic degradation pathway involved homolytic cleavage of the azo bond followed by phenyl radical generation to a small intermediate of hydroxyquinol. Thus the work represents an efficient alternative to conventional membrane technology with lesser membrane fouling tendency and enhanced catalytic efficiency in assistance with microbubbles simultaneously
Uncertainty Quantification in the Behaviour of Lap Joints in FRP Laminates
No full textAdhesive bonding and mechanical fastening have been continuously in use to fabricate fibre-reinforced plastic (FRP) laminated composite structures. The use of adhesive bonding has its roots in aircraft structures for more than 50 years, where durable and lightweight bonds are necessary. On the other hand, bolted connections/joints use nuts, bolts, and washers to join two adherends together, making it easier to install and disassemble the connections. The performance of joints can be affected by several uncertain preconditions, such as uncontrolled manufacturing, test procedures, material properties, loading, and environmental conditions. There could be uncertainty in the performance of the joints even if identical parameters are followed to fabricate the joints. Such variability in the performance of the joints must be assessed for the probabilistic analysis of various composite structures. In the present study, experimental investigations are carried out to study the performance and behaviour of single-lap joints in both connection categories, i.e. adhesive and bolted, between glass fibre-reinforced plastic (GFRP) adherends. The shear strength of the single-lap joints (SLJs) in GFRP adherends is evaluated under tensile loading in the longitudinal direction. Carbon nanotubes (CNTs) are incorporated into the adhesive matrix to attempt the shear strength enhancement in the SLJs. Uncertainty in the shear strength is quantified and modelled using Goodness-of-Fit (GOF) tests, such as Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D), with statistical distributions. SLJs are also investigated to predict their performances numerically using finite element methods. An acceptable agreement between the experimental and numerical performance is observed. The peel stress in the adhesive is obtained from finite element simulation. A sensitivity analysis is carried out to determine the material property that influences the peel stress the most. Uncertainty in the peel stress due to the induced uncertainty in the material properties is modelled using GOF tests. Similarly, single-shear joints in GFRP adherends with a single bolt are investigated experimentally. The uncertainty in the ultimate load capacity of the bolted joints under tensile loading is quantified using GOF tests. Numerical simulation is also carried out for the joints to obtain the performances such as the joint stress, stiffness and maximum out-of-plane displacement. Sensitivity analysis is performed to obtain the most affecting parameter, among the material properties and bolt tightening torque. The GOF tests are used with statistical distributions to quantify the uncertainty in the load capacity caused by the uncertainty induced in the sensitive material properties. The use of sandwich composite, in the recent past, has gained attention for their investigation in aerospace industries, as well as other fields such as marine and automotive. In the present study, the dynamic performances, such as fundamental natural frequencies and mode shapes, of the sandwich composite plate made of GFRP face sheets and PVC (polyvinyl chloride) core are evaluated through free vibration analysis. Sensitivity analysis is carried out to identify the most influential material property to the natural frequency of the sandwich plate. The uncertainty in the natural frequency of the sandwich plate is modelled using GOF tests. An elite statistical distribution, obtained by GOF tests, is recommended to describe or model the uncertainty. The performance of the recommended distribution is studied using various probabilistic plots, such as probability density function, cumulative distribution function, survival, hazard, P-P and probability difference plots. The outcome and knowledge of the present study will enlighten the industry to control the manufacturing procedure of the connections/joints in FRP structures, as well as the sandwich composite. Furthermore, the present study will form a basis for the probabilistic analysis of FRP and sandwich structures
Low Complexity Direct Power Control Methods for Active Front end Rectifiers and Applications
No full textIn modern power systems, Low Voltage Direct Current (LVDC) distribution system is an emerging technology that enables increased penetration of renewable energy sources and provides scope for a number of applications like Electric Vehicle charging, DC lighting and illuminations, Electrical Drives, data centers, etc. As LVDC systems are located at the load end and operate at low power, electrical energy is taken from the AC grid during shortage and given to the AC grid during surplus. A bidirectional flow of AC to DC conversion is achieved by using a power electronic converter and advanced control algorithms. Direct Power Control (DPC) has evolved as one of the well-established control methods which was initially demonstrated for AC to DC conversion but in later years applied to DC to AC conversion as well. The literature describes many advanced and complex methods of DPC having high computational complexity. However, there are only a few works that are intended to reduce the computational complexity and focus on low-complexity switching vector selection methods. Realizing the importance of the lower computational complexity, this dissertation attempts to develop the low complexity DPC methods that can be implemented using low-cost microcontrollers. Classical DPC operates with 12 sectors and it is based on a predefined table. However, a table that changes according to the load requirements and system parameters is always preferred. It is also investigated that with the increase in the number of sectors, the resolution of sector detection will increase and provides a better response. Hence a dynamically adjustable switching algorithm method is developed which is based upon an 18-sectored division of the phase angle of the grid voltage vector. This dissertation also attempts to hybridize the highly accurate predictive group of the controllers which have high computational complexity with the simplest control of switching table-based DPC (ST-DPC). It is achieved by providing compensation for the unavoidable control delay present in the real-time environment by the delay compensated multifold switching table DPC (DCMST-DPC). The aim of such a controller is to substantially improve the performance without significant additional computation so that it can be implemented with a lower-cost microcontroller. The practical power system does not always have a balanced three-phase supply system. Hence adaptive controllers are required for the PWM rectifier so that the converter performance does not deteriorate even during grid unbalance. This dissertation proposes one of such controllers which is able to accurately estimate the instantaneous powers without grid voltage sensors even in an unbalanced three-phase system. A simpler algorithm deadbeat DPC (DB-DPC) has been chosen for the switching vector selection, which follows a more detailed system modeling yet has a lower computational requirement. Most of the articles in the literature are related to one application of the DPC technique, which is the PWM rectifier control. Apart from that, the other potential applications of DPC have been less explored. Hence, this dissertation presents a detailed study of new sights of the control technique in different fields of application. Additionally, this dissertation also focuses on DPC applications in the shunt active power filter (SAPF) and battery charging systems. In this work, a SAPF control method is developed which can handle different non-ideal characteristics of the grid and operate at a constant switching frequency. On the other hand, in the field of battery charging applications, this work proposes a single-stage converter, unlike the classical methods, which employ a two-stage power converter. The proposed battery charging system uses the dynamic dc link to abolish the dc-dc converter, which constitutes the second stage of the two-stage power converter in the existing methods. To evaluate the feasibility and performance of the developed techniques, the respective system models have been tested in MATLAB Simulink, RT-LAB, and a real-time environment with an extensive analysis of the obtained results
The Effect of Intercalation on the Structure and Properties of Layered MSe2 (M = Nb, Ti)
No full textLayered (2D) materials are an intriguing class of materials which has fascinated the researchers around the globe due to their excellent mechanical flexibility, reduced dimensionality and interlayer van der Waals interaction. The tunable dimensionality, availability of larger surface area and weak interlayer interaction of these 2D materials provides manifold applications in the field of electronics, optoelectronics, catalysis, energy generation and storage, spintronics, chemical and biological sensors, solar cells, supercapacitors, etc. 2D materials covers various classes of layered materials starting from organic layered materials, layered oxides, layered halides and layered chalcogenides. Particularly, layered transition metal dichalcogenides (LTMDs) have attracted enormous research attention because of their broad range of electronic properties from insulator to superconductors. Further, the structural and physical properties of LTMDs, specifically group IV, group V, and group VI metal chalcogenide, can be tuned by intercalating with various intercalants. The thesis highlights the effect of various type of intercalant on the structure and properties of 2H–NbSe2 and 1T–TiSe2. The thesis can be divided majorly into seven chapters, where the first two chapters include introduction to LTMDs, synthetic process and characterization tools used to study the various effects of intercalation. An introduction to the LTMDs, the polytypic behavior, charge density wave (CDW), superconductivity, intercalation process is briefly presented in chapter one. Chapter two details a comprehensive synthetic process i.e., high temperature solid-state method and the associated processes for synthesis of pure phase intercalated LTMDs. Further to identify and analyze the phases, various characterization techniques that applied along with the working theories, instrument details and data collection conditions are entailed in chapter two. The third, fourth and fifth chapters detail the effect of p-block, s-block and d-block element intercalant, respectively on the structural and physical properties of 2H–NbSe2. The detrimental effect of Sn (p-block) intercalation on the superconductivity of 2H–NbSe2 is studied in chapter three on the basis of data obtained from powder XRD, Raman spectroscopy, magnetic and resistivity properties measurements. The intercalation process suppresses the superconductivity of 2H–NbSe2, which is related to the lattice expansion due to the insertion of Sn in the vdW gap and a plausible explanation on the valence factor of intercalant on the properties is included. On the other hand, Mg (s-block element) have a minimal effect on the superconductivity and also on the structure. The said effect has been explained through theoretical and experimental studies and attributed to the unfavorable interaction of Mg with NbSe2 layers in MgxNbSe2. Furthermore, Fe (d-block element) has an interesting effect on the structural property of 2H–NbSe2. At 900 ℃, a mixture phase (2H+4H) is obtained for pristine NbSe2. Intercalation of Fe in the octahedral void of vdW gap results in three distinct type of phase transition in FexNbSe2. In composition range 0 ≤ x < 0.2 the mixture phase transforms to 2H phase where Fe is randomly distributed throughout the vdW gap. Further increase in Fe content in FexNbSe2 (0.2 < x ≤ 0.25), the 2H phase transform to an ordered 2 x 2 x 1 superstructure denoted as 2H(I) phase, which crystallizes in the same space group (P63/mmc) as that of the parent compound and Fe atoms occupy alternate octahedral (Oh) void in an ordered manner. Finally in the range of 0.25 < x ≤ 0.5, the 2 x 2 x 1 superstructure transforms to √3 x √3 x 1 superstructure with a polar space group P6322. Intercalation of Fe induces antiferromagnetism. The disordered phase shows weak short range antiferromagnetism and spin glass like behavior below 25 K. The ordered phase, Fe0.25NbSe2, show a long range antiferromagnetic ordering at a much higher temperature as compared to the disordered phase with TN = 144 K. Further increase in Fe content, the antiferromagnetic ordering temperature decreases to 60 K for Fe0.40NbSe2. The variation of the magnetic ordering temperature strongly correlated with the structure and position of magnetic ions. The sixth chapter describes the effect of Sn, Pb and Dy intercalants on the structure and properties of 1T–TiSe2. Particularly, a detailed study on the effect of Dy intercalation on the structure and magnetic properties has been carried out. Dy has a minimal effect on the structure of 1T–TiSe2, whereas intercalation of Dy induces paramagnetism till 4% and further intercalation results in antiferromagnetic ordering below 4.5 K. The last chapter includes the summary, future works and bibliography. In summary, the various factors which has been found to affect the extent of intercalation and the effect of intercalation on 2H–NbSe2 and 1T–TiSe2, has been correlated and categorized on the basis of type of intercalant
Influence of Temperature on Mechanical Performance of Glass Fiber/epoxy Composite with Continuous and Discontinuous Secondary Carbon Fiber Reinforcement
No full textIn the current world of structural materials, fiber reinforced polymer (FRP) composites have become a revolutionary material due to their excellent mechanical performance, low density and corrosion resistance. However, these laminated composites usually experience catastrophic failure in nature and poor out-of-plane properties, which raises the possibility that scientifically structured fiber hybridization and modified matrix with short fibers could be used for improved mechanical performance. Carbon fibers have always been of interest to material scientists due to their lack of structural defects and unique mechanical properties. This directs the possibility of incorporating them into widely used glass/epoxy composites through fiber hybridization technic in the form of continuous fibers or modifying the matrix of glass/epoxy composite with waste short carbon fibers to achieve superior mechanical stability. In order to accept these materials for use in different high-end applications, their performance in various in-service environments must be well assured. The present study starts with assessing the three-point flexural and tensile performance of inter-ply glass/carbon/epoxy hybrid composite by altering the hybrid ratio and stacking position. These materials were tested at various elevated temperatures (30 °C, 50 °C, 70 °C, and 110 °C) at 1 mm/min loading speed. In addition, tensile behavior was analyzed at 30 °C and 110 °C temperatures with 0.1, 1, 10 and 100 mm/min loading speeds. The test results of hybrid composites were compared with glass/epoxy (GE) and carbon/epoxy (CE) composites. The stacking position of carbon fibers in GE composite plays a vital role while deformed under a flexural load. Incorporating two stiffer carbon fibers in GE composite, i.e. C2G3 and C1G3C1, performed remarkable increment in flexural strength. All these composites have shown relatively ductile deformation at 110 °C. In tensile test, placing a carbon fiber ply in GE composite (G2C1G2) imparts pseudo-ductility as well as positive hybrid effect in the composite. On the other hand, replacing a glass fiber ply with carbon fiber at both ends (C1G3C1) imparted improved strain to failure and positive hybrid effect in the composites. Further, emphasis was given to the mode-I and II interlaminar fracture toughness (ILFT) of hybrid composites with five layers of carbon fiber (stacking sequence: C5G5, and (G1C1)5) in GE composites at different temperatures (30, 50, and 70 °C) and compared with neat GE and CE composites. The test results exhibited that the stacking position of glass/carbon fibers and test temperature substantially influence the fracture toughness of hybrid composites. At 30 °C, mode I ILFT value of alternative glass and carbon fiber stacking sequence (G1C1)5 of hybrid composite showed 29.38% improvement than CE composite. Further, mode II ILFT of C5G5 and CE composites was 22.29% and 42.13% higher than that of GE composite, respectively. Increasing the test temperature of all composites improved their GIC and GIIC values. The next step towards a comprehensive understanding of the in-service temperature impact on composites is to study the durability of these composites at cryogenic temperature (CT). For doing the same two types of characterizations have been performed, (i) in-situ cryogenic flexural testing of all the composites (i.e., while the sample, as well as the testing fixture were completely submerged in liquid nitrogen bath during the entire testing period), and (ii) ex-situ ageing of the composite samples for various time periods in liquid nitrogen bath followed by flexural testing at ambient temperature. Among all the hybrid composites considered in this study, C2G3 presented the highest enhancement in flexural performance at all testing conditions, which were ∼38.16% at RT, ∼ 29.03% at CT. At CT, all the combinations of hybrid composites showed higher flexural performance than the neat G5 composite. C2G3 achieved the maximum flexural strength, which was 27.82% higher than G5 composite, after 8 h of ageing. The next objective is aimed to utilize the waste carbon fibers, generated from our regular fabrication process in the laboratory, which was cut in the length range of 2 – 5 mm and termed as short carbon fiber (SCF). These SCFs were added to GE composite in varying contents (0.1, 0.3, and 0.5 wt.%) as secondary reinforcement. The flexural and tensile behavior of the SCF modified GE composites were assessed at ambient (30 °C) and elevated (50 °C, 70 °C, and 110 °C) temperatures. The most significant improvement in mechanical performance was achieved by adding only 0.1 wt.% of SCF into GE composite across most of the testing temperatures. At elevated temperatures, all the SCF modified GE composites showed superior mechanical performance over the neat GE composite. The effects of waste SCF reinforcement in GE composite on the overall damage tolerance of the structural composite were examined by both mode I and mode II ILFT in terms of both crack development and propagation. Effect of various SCF content (0.1wt.%, 0.3wt.%, and 0.5wt.%) in GE composites are evaluated experimentally at room as well as at elevated temperatures (30 °C, 50 °C, and 70 °C). Based on the experimental results, GE composite with 0.1 wt.% of SCF at ambient temperature revealed 13.49% and 20.45% increment in mode I and mode II ILFT, respectively, than GE. A positive reinforcement effect is noticed for GIC and GIIC values up to 50 °C. However, due to unfavorable residual interfacial stresses resulting in interfacial debonding and epoxy softening, a negative reinforcement effect is noticed at 70 °C. Flexural tests were performed to assess the integrity and durability of composites at in-situ CT and after ex-situ cryo-ageing in liquid nitrogen for various time intervals (0.25 hrs, 0.5 hrs, 1 hr, 2 hrs, 4 hrs, 8 hrs, and 16 hrs). The composite with 0.1 wt.% of SCFs showed the highest enhancement in flexural performance in all testing conditions, i.e., ∼16% at RT, ∼ 12% at CT, and between 13% - 39% after cryo-ageing over neat GE. Composites with SCFs retained their strength at CT and after cryo-ageing, suggesting that the waste fibers could be economically utilized and preferred over other expensive nanofillers as secondary reinforcements in GE composites for cryogenic applications
A Study on Carbon-based Hybrid Supercapacitor Electrodes: Design, Fabrication, and Testing
No full textIn recent years due to the lack of fossil fuels, population growth and the development of portable electronic devices, it is essential to design a novel energy conversion and storage devices. Various energy storage devices are commercially available, among them supercapacitors (SCs) have been given special attention because of their high energy and power density, longer cycle-life (> 100000), fast charging-discharging process, a wide range of operating temperatures and high efficiency. These outstanding properties of SC makes a wide range of potential applications, such as in-memory backup systems, hybrid electric vehicles, LED drivers, energy management fields, industrial power supplies, etc. The SCs bridge energy and power gap between the conventional capacitor and battery. However, low energy density is the main concern for SC. Hence, active research is going on in search of novel electrode materials and new designs to improve energy density without losing its power density. SCs are classified based on their electrode materials, such as electrical double-layer capacitors (EDLC) in which mostly carbon materials are used as electrodes. The second one is pseudocapacitors (PCs) that combine transition metal oxides and conductive polymers as electrode material. Another class is the hybrid capacitor (HC), which combines carbon and metal oxide/conductive polymer for electrode material. Carbon materials (carbon nanotubes, graphene, activated carbon etc.) are given much attention to SCs owing to their unique physical and chemical properties like high specific surface area, good electrical conductivity, developed porous structure, environmentally friendly, non-toxic, good thermal and mechanical stability. However, carbon-based SC's, energy density is the limitation for the realization of commercial applications as per the energy demand globally. The metal oxide and conductive polymers involve reversible redox reactions during their charging-discharging time which results in electrodes accumulating more electrolyte ions. Hence, the performance of carbon-based SC could be further improved by integrating it with metal oxides or conductive polymers. In this thesis, the fabrications of various carbon composite electrodes have been investigated for SC application, where the proper optimization of carbon composite has been carried out. Furthermore, the presence of metal oxides in the carbon composite for SC application was also investigated. The symmetric SC devices were also fabricated for practical application. The novelty of this research work is the cost-effective synthesis of the electrode materials, new design of the device, and the accomplishment of outstanding properties. The selected carbon materials chosen are carbon nanotubes (multiwalled carbon nanotubes (MWCNTs)), few-layer graphene (FLGR), mixed phase carbon material (MPCM) and activated carbon (AC). Though there are various metal oxides, ZnFe2O4 (ZFO) was chosen and incorporated in different carbon structures to make SC electrodes. The electrochemical properties of the electrode materials were investigated using cyclic voltammetry (CV), Galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS). It has been observed that carbon-based SC results good storage performance, i.e., the maximum specific capacitance obtained by the MWCNTs-5/AC electrode was 222 F/g at 1 Ag-1 and 239 F/g at 5 mV/s. The functionalized MWCNTs/AC electrode has a capacitance of 372 F/g at 60 Ag-1 and 395 F/g at 5 mV/s. The cyclic stability test of the fabricated electrodes has been investigated and the MWCNTs-5/AC retained a maximum capacitance of 90%, whereas functionalized MWCNTs/AC retained 79% over 10,000 cycles. The fabricated 10 wt. % of FLGR in AC (FLGR-10/AC) results excellent SC properties, and the maximum obtained specific capacitance was 176 Fg-1 at 1 Ag-1 and 180 Fg-1 at 5 mV/s. The fabricated FLGR-30/MPCM showed a maximum specific capacitance of 283 F g-1 at 1 A g-1 and 282 F g-1 at 5 mV/s. The fabricated electrodes have been further subjected to a cyclic stability test for 10,000 cycles. The FLGR-30/MPCM retained a maximum capacitance of 93%, whereas FLGR-10/AC retained 91%. The performances of carbon-based SC have been further improved by incorporating ZFO. The maximum specific capacitance of the ZFO-10/MWCNTs/AC electrode was 613 F g-1 at 5 mV/s and 609 F g-1 at 1 Ag-1. In a two-electrode symmetric device, ZFO-10/MWCNTs/AC electrode based device has shown the highest specific capacitance i.e. 323 Fg-1 at 1 Ag-1 and 313 Fg-1 at 5 mV/s. The energy density and power density of the ZFO-10/MWCNTs/AC device were evaluated following standard relation. The maximum obtained energy density is 16.15 Wh/kg and a power density of 6000 W/kg among the other two devices. Moreover, ZFO-10/MWCNTs/AC device shows outstanding cyclic stability performance i.e. 97 % capacitance has been retained over 10,000 cycles. Therefore, the presence of ZFO in carbon composites enhances storage performances and offers excellent cyclic stability. The finding suggests that the prepared carbon-based hybrid electrodes have performed excellent storage performance and cycle stability. The obtained energy and power density of carbon-based hybrid SC might contribute to the current challenges towards the energy and power-efficient SC device requirement
Biometric System for Person Identification: Exploring Unimodal and Multimodal Techniques
No full textBiometric representation of humans often require the tasks such as identification and verification using human computer interaction (HCI), which can be achieved using various modalities such as fingerprint, face, retina, voice, signature, etc. However, multiple attacks can challenge the security and performance of existing biometric systems. Electroencephalography (EEG) is considered an alternative to developing a robust biometric system. Brain activities represented using EEG signals are more sensitive, secure, and difficult to copy and steal. We have studied various aspects and understood the shortcomings in the existing schemes. Many person identification systems have been designed using machine learning and deep learning approaches in the existing literature. The available biometric systems need performance improvement, keeping security into consideration. In this research, we have tried to address these issues towards improving the system’s overall performance. In this thesis, a Spatio-temporal dense architecture for EEG-based person identification/authentication is designed and explored. In the proposed scheme, the raw EEG data are processed to extract robust and informative spatial features using Convolutional Neural Networks (CNN), known for automatic feature extraction from the raw data. Then, a long short-term memory (LSTM) network is utilized to process temporal data, and person identification is carried out. The experiment has been carried out on a publicly available dataset consisting of an EEG of 109 subjects. The architecture is tested on two baseline situations, i.e., eye closed (EC) and eye opened (EO). A Spatio-temporal identification/ authentication model for a publicly available emotion-based DEAP (Database for Emotion Analysis;Using Physiological Signals) dataset has been designed to explore and prove the robustness. Person identification rates of 99.95%, 98%, and 93.87% have been recorded for EC, EO states, and emotion using the proposed scheme. Experimental results demonstrate the robustness of the proposed scheme in terms of person identification and outstrip existing works. EEG-based biometric are putting forward solutions because of their high-safety capabilities and handy transportable instruments. In the Spatio-temporal model, more parameters and dimensionality constraints affect the overall training performance. Motor imagery EEG (MI-EEG) is a broadly centered EEG signal exhibiting a subject’s motion intentions without actual actions. This research proposes an unsupervised framework for feature learning based on autoencoder. It learns sparse feature representations for EEG-based person identification. Autoencoder-CNN exhibits the person identification task for signal reconstruction and recognition. The framework proved to be a practical approach in managing the massive volume of EEG data and identifying the person based on their different task with resting states. The experiments have been conducted on the standard publicly available Motor imagery EEG dataset with 109 subjects and an emotion-based dataset with 32 users. Different task and emotion-based models using autoencoder and CNN have been explored in this research. We have noted the highest recognition rate of 87.60% for task based identification, 98.43% for the emotion-based identification model. Moreover, a maximum 99.89% recognition rate for resting-state has been recorded using the Autoencoder-CNN model. The outcomes imply that the overall performance of the proposed framework is similar or advanced to that of a novel method. The shape is proved to be a realistic technique to control the full-size extent of EEG data and pick out the individual, primarily based on their specific task. In the above-proposed schemes, a single trait is used for designing person identification systems. This can be made much more secure and reliable using more traits, which is addressed in this research to explore multimodality. A novel multimodal biometric person identification system by using two closely connected traits, i.e., signature and brain signals as Electroencephalography (EEG), is proposed in this research. The strokes while signing stimulates the brain signals. The response of the activity is unique for a user in the brain, and this relationship is explored in this research in designing a more secure and robust person identification system. The performance accuracy of 97.54% for CNN, 98.61% for CNN-LSTM, and 98.74% for CNN with PCA are observed. To improve the recognition rate and to secure the authentication approaches, multimodal biometric is proved as a novel technique