Relevant bibliographies by topics / Coal Analysis Data processing

Academic literature on the topic 'Coal Analysis Data processing'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Coal Analysis Data processing"

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Lyrshchikov, Sergey Yu, and Larisa V. Sotnikova. "Comparative analysis of the structure of coals of different stages of metamorphism according to 13C NMR data." Butlerov Communications 63, no. 8 (August 31, 2020): 53–57. http://dx.doi.org/10.37952/roi-jbc-01/20-63-8-53.

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In this paper, the method of cross-polarization with magic angle rotation and decoupling from protons (CPMAS) 13C NMR spectroscopy obtained quantitative data on the distribution of carbon over structural fragments and calculated the degree of aromaticity (fa) of some coal samples from various Siberian deposits of a wide range of metamorphism. All the coals used in the work were characterized by standard methods (proxymate and ultimate analysis). The optimal parameters of the pulse program for recording the spectra of coals have been determined. To obtain quantitative data, the spectra were simulated. The spectrum model included from 9 to 13 components, depending on the stage of coal metamorphism. The dependences of the degree of aromaticity and the sum of oxygen-containing functional groups on the stage of coal metamorphism were constructed. The results obtained show that the structure of coals regularly changes depending on the stage of their metamorphism. The revealed relationships of the structure and properties of coals, together with the parameters of their reactivity, can ensure the safe behavior of coals in the processes of mining and processing, as well as in determining possible ways of using the studied coal samples as a valuable chemical raw material.
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Li, Fan Xiu, Xing Ping Wen, and Shao Jin Yi. "Numerical Measurement and Data Processing of Air Pollution." Applied Mechanics and Materials 577 (July 2014): 1219–22. http://dx.doi.org/10.4028/www.scientific.net/amm.577.1219.

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Relational analysis method was a data process method used to sort out the correlation extent of effect factors in a system with uncertain information. Common mathematical methods were not applicable for describing the relationship. A new method, equivalent numerical relational degree (ENRD) model was developed to evaluate the effect of different factors on air pollution. The effects of different factors-the port throughput, amount of coal, industrial output, and motor vehicle ownership, investment in fixed assets, real estate development and construction of housing construction area on the quality of atmospheric environment were studied. The degrees of correlation were calculated according to ENRD and the values of the port throughput, amount of coal, industrial output, motor vehicle ownership, investment in fixed assets, real estate development and construction of housing construction area were 0.7947, 0.7943, 0.7289, 0.7238, 0.6702 and 0.6527, respectively. From these values, the relations of these factors to the quality of atmospheric environment could be described and evaluated, and the port throughput and amount of coal were relatively major.
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Sheta, Sahar, Muhammad Sher Afgan, Zongyu Hou, Shun-Chun Yao, Lei Zhang, Zheng Li, and Zhe Wang. "Coal analysis by laser-induced breakdown spectroscopy: a tutorial review." Journal of Analytical Atomic Spectrometry 34, no. 6 (2019): 1047–82. http://dx.doi.org/10.1039/c9ja00016j.

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This review article forms a guideline for LIBS contribution in coal analysis, encompassing fundamental aspects, operation modes, data processing, and analytical results. LIBS applications related to coal utilization are also highlighted (fly ash analysis and combustion monitoring).
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Choi, Jae Seung, Choong Mo Ryu, Jung Hyun Choi, and Seung Jae Moon. "Improving the Analysis of Sulfur Content and Calorific Values of Blended Coals with Data Processing Methods in Laser-Induced Breakdown Spectroscopy." Applied Sciences 12, no. 23 (December 4, 2022): 12410. http://dx.doi.org/10.3390/app122312410.

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In Situ monitoring of the calorific value of coal has the advantage of reducing the amount of unburned carbon by injecting an appropriate amount of combustion air immediately to induce complete combustion. High sulfur concentrations cause severe environmental problems such as acid rain. In order to estimate the calorific value and measure the sulfur concentration, a new powerful technique for mixed coals was studied. Laser-induced breakdown spectroscopy (LIBS) does not require sample preparation. Several blended coals were used for the experiment to replicate the actual coal-fired power plant conditions. Two well-known data processing methods in near-infrared spectroscopy have been adopted to enhance the weak sulfur emission lines. The performance of the partial least square regression model was established by the parameters such as coefficient of determination, R2, relative error, and root mean square error (RMSE). The RMSE average was compared with the results of previous studies. As a result, the values from this study were smaller by 6.02% for the calibration line and by 4.5% for the validation line in near-infrared spectroscopy. The RMSE average values for calorific values were calculated to be less than 1%.
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Atamanyuk, O. A. "ANALYSIS AND PROSPECTS OF SEPARATE ENVIRONMENTAL PROTECTION SYSTEMS AT COAL MINING AND COAL PROCESSING ENTERPRISES OF UKRAINE." Journal of Coal Chemistry 5 (2021): 15–25. http://dx.doi.org/10.31081/1681-309x-2021-0-5-15-25.

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The article describes the organizational regional structure of industrial waste management in modern Ukraine, which will improve the efficiency of industrial waste management systems and industrial energy facilities in Ukraine. The structure of industrial waste management at different system levels, from the national to the level of an industrial enterprise, is described. The data on the recommendations of the World Health Organization on the emissions of toxicants into the air and the data of the State Statistics Service of Ukraine on the content of the main pollutant gases in air emissions from stationary sources of Ukraine from 1990 to 2018 are presented. The scheme of the negative impact on the environment of emissions of pollutants from stationary sources – production facilities of coal mining and coal processing is characterized. Comparative data on the indicators of the volumes of toxic waste generation at the enterprises of European countries and the heavy industry of Ukraine are given. It is shown that the main sources of the impact of coal processing enterprises and coal processing industries on all spheres of the environment are organized and unorganized dust and gas emissions, discharges of process wastewater and effluents from the surface and from the volumes of dumps and sludge collectors of coal processing plants. As a result of comprehensive research by the authors, a number of coal dumps of concentrating factories in Eastern Ukraine have been studied, migration routes and concentration of rare and heavy metal compounds in the soil near coal dumps have been investigated. Rather stringent standards for the content of hazardous substances in industrial waste require special methods for neutralizing coal sludge before final disposal and / or disposal.of the raw materials used to obtain humic acids. The corresponding equations are given. Keywords: coal sludge, coal preparation plants, coal dumps, sludge collectors, surface runoff, environmental pollution, emissions into the atmosphere, gaseous, liquid and solid toxicants. Corresponding author O.М. Kasimov, e-mail: nto@ukhin.org.ua
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Sujatha, CN. "Coal Production Analysis using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 14, 2021): 919–26. http://dx.doi.org/10.22214/ijraset.2021.35130.

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Coal will keep on giving a significant segment of energy prerequisites in the US for at any rate the following quite a few years. It is basic that exact data portraying the sum, area, and nature of the coal assets and stores be accessible to satisfy energy needs. It is likewise significant that the US separate its coal assets productively, securely, and in a naturally mindful way. A restored center around government support for coal-related examination, facilitated across offices and with the dynamic cooperation of the states and modern area, is a basic component for every one of these necessities. In this project we attempt to predict the coal production using various features given the data set. We attempt to implement regression algorithms and find the best algorithm along with fine tuning the parameters of the algorithm. The existing system uses the linear regression model one of the main issues with this basic linear regression is that it does not have a regularization parameter and hence overfits the data. The system also does not provide enough pre-processing and visualization or Exploratory Data Analysis (EDA). We aim to build advanced regression models like ridge and lasso regression and also fine tune the parameters of the model. These models would be trained on a data set which will be engineered carefully after performing the feature engineering.
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Jia, Rui Sheng, Hong Mei Sun, Chong Qing Zhang, and Xue Ting Lv. "Modeling for Safety Evaluation of Coal Mine Roof Based on Information Fusion." Advanced Materials Research 143-144 (October 2010): 439–43. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.439.

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Factors that affect the safety of coal mine roof is a multi-faceted, information fusion technology can take full advantage of multi-source information complementary, comprehensive, and improving information quality and credibility of coal mine roof safety. In analyzing the current monitoring means, a coal mine roof safety evaluation model is presented based on information fusion, and given information processing steps of multi-sensor data analysis, processing, distribution and integration based on Dempster-Shafer evidence theory; For the elimination of multi-source data fusion of uncertain factors, proposed coal mine roof safety decision-making rules; The simulation analysis shows that the validity of the model and practicality.
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Gordienko, M. O. "The selection of technological basis of deep processing of coal." Journal of Coal Chemistry 4 (2021): 15–21. http://dx.doi.org/10.31081/1681-309x-2021-0-4-15-21.

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THE SELECTION OF TECHNOLOGICAL BASIS OF DEEP PROCESSING OF COAL © M.O. Gordienko (State Enterprise "Ukrainian State Research Coal Chemical Institute (UHIN)", 61023, Kharkov, Vesnina st., 7, Ukraine) The article is devoted to the analysis of the possibility of expanding the raw material base of thermal energy, as well as meeting the demand for motor fuels and chemical products through the thermochemical processing of coal, the reserves of which are large enough and available for extraction and transportation. Moreover, in contrast to technologies such as methanization and liquefaction, the most promising type of deep processing of coal seems to be its gasification. This process is carried out in sealed devices of high power according to the technologies that have a long history of improvement on an industrial scale by the world's leading companies. It was emphasized that Ukraine has significant reserves of low-calorie coal (constantly expanding due to waste of coal preparation), the thermochemical processing of which can significantly expand the domestic energy base. The basic principles of classification and technological foundations of existing industrial and industrial research installations for gasification of coal and similar materials are given. The basic diagrams and main parameters of the existing installations, which carry out the gasification process at temperatures below the melting point of the mineral (ash-forming) components of the raw material, are described - Sasol Lurgi and SES Gasification Technology (SGT). Based on the data on the world experience in the operation of thermochemical coal processing units, it is shown that low-temperature (carried out at a temperature below the melting point of the mineral ashforming components) gasification of various types of non-coking coal with certain technological solutions can be no less effective than more complex and expensive high-temperature technologies. There are grounds for believing that the efficiency of gasification with ash removal in a solid state can be further increased by using some of the technological capabilities available in coke production. Keywords: brown coal, non-coking coals, thermochemical processing, gasification, efficiency, degree of carbon conversion, energy carriers, synthesis gas, environmental safety. Corresponding author M.O. Gordienko, е-mail: yo@ukhin.org.ua
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Gordienko, M. O. "The selection of technological basis of deep processing of coal." Journal of Coal Chemistry 4 (2021): 15–21. http://dx.doi.org/10.31081/1681-309x-2021-0-4-15-21.

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THE SELECTION OF TECHNOLOGICAL BASIS OF DEEP PROCESSING OF COAL © M.O. Gordienko (State Enterprise "Ukrainian State Research Coal Chemical Institute (UHIN)", 61023, Kharkov, Vesnina st., 7, Ukraine) The article is devoted to the analysis of the possibility of expanding the raw material base of thermal energy, as well as meeting the demand for motor fuels and chemical products through the thermochemical processing of coal, the reserves of which are large enough and available for extraction and transportation. Moreover, in contrast to technologies such as methanization and liquefaction, the most promising type of deep processing of coal seems to be its gasification. This process is carried out in sealed devices of high power according to the technologies that have a long history of improvement on an industrial scale by the world's leading companies. It was emphasized that Ukraine has significant reserves of low-calorie coal (constantly expanding due to waste of coal preparation), the thermochemical processing of which can significantly expand the domestic energy base. The basic principles of classification and technological foundations of existing industrial and industrial research installations for gasification of coal and similar materials are given. The basic diagrams and main parameters of the existing installations, which carry out the gasification process at temperatures below the melting point of the mineral (ash-forming) components of the raw material, are described - Sasol Lurgi and SES Gasification Technology (SGT). Based on the data on the world experience in the operation of thermochemical coal processing units, it is shown that low-temperature (carried out at a temperature below the melting point of the mineral ashforming components) gasification of various types of non-coking coal with certain technological solutions can be no less effective than more complex and expensive high-temperature technologies. There are grounds for believing that the efficiency of gasification with ash removal in a solid state can be further increased by using some of the technological capabilities available in coke production. Keywords: brown coal, non-coking coals, thermochemical processing, gasification, efficiency, degree of carbon conversion, energy carriers, synthesis gas, environmental safety. Corresponding author M.O. Gordienko, е-mail: yo@ukhin.org.ua
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Kamoza, Ekaterina S. "Studying the effect the physicochemical properties of fine size raw coal organic and mineral composition have on beneficiation efficiency." Izvestiya vysshikh uchebnykh zavedenii Gornyi zhurnal 6 (September 15, 2021): 65–75. http://dx.doi.org/10.21440/0536-1028-2021-6-65-75.

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Introduction. This work presents the results of the research on the effect the petrographic characteristics, elemental and mineral composition have on the Kedrovsko-Krohalevskoe fine coal beneficiation efficiency. The data has been analyzed of raw coal physochemical properties at the stage of mining and dressing mill raw material base formation. Research objective is to study thermal coal by physicochemical methods of analysis to identify the main parameters affecting the quality of beneficiation products. Based on the data obtained, the research aims to develop the method of assessing the indicators affecting the beneficiation process and further forecasting during the development of new areas and coal seams. Methods of research. The structural features of raw KCN (coking low-caking low-metamorphosed) coal were determined by comparative analysis of three samples according to the following parameters: laboratory flotation results, general technical parameters, maceral composition, organic matter elemental composition, and ash mineral composition. Results. It has been established that coals of a similar nature of origin and stage of metamorphism, can have different indicators in vaarious parameters: particle size, number and morphology of microcomponents according to quantitative petrographic analysis, as well as the presence of hydrophilic functional groups on the surface of coals. Conclusions. The results of the studies made it possible to identify a number of dependencies that can be used in a preliminary assessment of the quality of raw coal sent to a coal processing enterprise.
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Dissertations / Theses on the topic "Coal Analysis Data processing"

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CRAESMEYER, GABRIEL R. "Tratamento de efluente contendo urânio com zeólita magnética." reponame:Repositório Institucional do IPEN, 2013. http://repositorio.ipen.br:8080/xmlui/handle/123456789/10578.

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Made available in DSpace on 2014-10-09T12:42:11Z (GMT). No. of bitstreams: 0
Made available in DSpace on 2014-10-09T14:05:08Z (GMT). No. of bitstreams: 0
Dissertação (Mestrado)
IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
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Leggett, Miles. "Crosshole seismic processing of physical model and coal measures data." Thesis, Durham University, 1992. http://etheses.dur.ac.uk/5623/.

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Crosshole seismic techniques can be used to gain a large amount of information about the properties of the rock mass between two or more boreholes. The bulk of this thesis is concerned with two crosshole seismic processing techniques and their application to real data. The first part of this thesis describes the application of traveltime and amplitude tomographic processing in the monitoring of a simulated EOR project. Two physical models were made, designed to simulate 'pre-flood' and 'post-flood' stages in an EOR project. The results of the tomography work indicate that it is beneficial to perform amplitude tomographic processing of cross-well data, as a complement to traveltime inversion, because of the different response of velocity and absorption to changes in liquid/gas saturations for real reservoir rocks. The velocity tomograms image the flood zone quite accurately. Amplitude tomography shows the flood zone as an area of higher absorption but does not image its boundaries as precisely, because multi-pathing and diffraction effects are not accounted for by the ray-based techniques used. Part two is concerned with the crosshole seismic reflection technique, using data acquired from a site in northern England. The processing of these data is complex and includes deconvolution, wavefield separation and migration to a depth section. The two surveys fail to pin-point accurately the position of a large fault; the disappointing results, compared to earlier work in Yorkshire, are attributed to poorer generation of compressional body waves in harder Coal Measures strata. The final part of this thesis describes the results from a pilot seismic reflection test over the Tertiary igneous centre on the Isle of Skye, Scotland. The results indicate that the base of a large granite body consists of interlayered granites and basic rocks between 2.1 and 2.4km below mean sea level.
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Irick, Nancy. "Post Processing Data Analysis." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606091.

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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada
Once the test is complete, the job of the Data Analyst has begun. Files from the various acquisition systems are collected. It is the job of the analyst to put together these files in a readable format so the success or failure of the test can be attained. This paper will discuss the process of breaking down these files, comparing data from different systems, and methods of presenting the data.
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Jifon, Francis. "Processing and modelling of seismic reflection data acquired off the Durham coast." Thesis, Durham University, 1985. http://etheses.dur.ac.uk/9315/.

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Off the Durham coast, the Permian succession above the Coal Measures contains limestones and anhydrite bands with high seismic velocities and reflection coefficients. The consequent reduction in penetration of seismic energy makes it difficult to determine Coal Measures structure by the seismic reflection method. Seismic data sets acquired from this region by the National Coal Board in 1979 and 1982 are used to illustrate that satisfactory results are difficult to achieve. Synthetic seismograms, generated for a simplified geological section of the region, are also used to study various aspects of the overall problem of applying the seismic technique in the area. Standard and non-standard processing sequences are applied to the seismic data to enhance the quality of the stacked sections and the results are discussed. This processing showed that in the 1979 survey, in which a watergun source and a 600m streamer were used, some penetration was achieved but Coal Measures resolution on the final sections is poor. The 1982 data set, shot along a segment of the 1979 line using a sleeve exploder source and a 150m streamer, showed no Coal Measures after processing. Synthetic seismograms, generated using the reflectivity method and a broadband source wavelet, are processed to confirm that a streamer with a length of 360 to 400m towed at a depth of 5-7.5m will be optimal for future data acquisition in the area. It is also shown that the erosion of the surface of the limestone lowers the horizontal resolution of the Coal Measures. Scattering
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Bilalli, Besim. "Learning the impact of data pre-processing in data analysis." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/587221.

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There is a clear correlation between data availability and data analytics, and hence with the increase of data availability --- unavoidable according to Moore's law, the need for data analytics increases too. This certainly engages many more people, not necessarily experts, to perform analytics tasks. However, the different, challenging, and time consuming steps of the data analytics process, overwhelm non-experts and they require support (e.g., through automation or recommendations). A very important and time consuming step that marks itself out of the rest, is the data pre-processing step. Data pre-processing is challenging but at the same time has a heavy impact on the overall analysis. In this regard, previous works have focused on providing user assistance in data pre-processing but without being concerned on its impact on the analysis. Hence, the goal has generally been to enable analysis through data pre-processing and not to improve it. In contrast, this thesis aims at developing methods that provide assistance in data pre-processing with the only goal of improving (e.g., increasing the predictive accuracy of a classifier) the result of the overall analysis. To this end, we propose a method and define an architecture that leverages ideas from meta-learning to learn the relationship between transformations (i.e., pre-processing operators) and mining algorithms (i.e., classification algorithms). This eventually enables ranking and recommending transformations according to their potential impact on the analysis. To reach this goal, we first study the currently available methods and systems that provide user assistance, either for the individual steps of data analytics or for the whole process altogether. Next, we classify the metadata these different systems use and then specifically focus on the metadata used in meta-learning. We apply a method to study the predictive power of these metadata and we extract and select the metadata that are most relevant. Finally, we focus on the user assistance in the pre-processing step. We devise an architecture and build a tool, PRESISTANT, that given a classification algorithm is able to recommend pre-processing operators that once applied, positively impact the final results (e.g., increase the predictive accuracy). Our results show that providing assistance in data pre-processing with the goal of improving the result of the analysis is feasible and also very useful for non-experts. Furthermore, this thesis is a step towards demystifying the non-trivial task of pre-processing that is an exclusive asset in the hands of experts.
Existe una clara correlación entre disponibilidad y análisis de datos, por tanto con el incremento de disponibilidad de datos --- inevitable según la ley de Moore, la necesidad de analizar datos se incrementa también. Esto definitivamente involucra mucha más gente, no necesariamente experta, en la realización de tareas analíticas. Sin embargo los distintos, desafiantes y temporalmente costosos pasos del proceso de análisis de datos abruman a los no expertos, que requieren ayuda (por ejemplo, automatización o recomendaciones). Uno de los pasos más importantes y que más tiempo conlleva es el pre-procesado de datos. Pre-procesar datos es desafiante, y a la vez tiene un gran impacto en el análisis. A este respecto, trabajos previos se han centrado en proveer asistencia al usuario en el pre-procesado de datos pero sin tener en cuenta el impacto en el resultado del análisis. Por lo tanto, el objetivo ha sido generalmente el de permitir analizar los datos mediante el pre-procesado y no el de mejorar el resultado. Por el contrario, esta tesis tiene como objetivo desarrollar métodos que provean asistencia en el pre-procesado de datos con el único objetivo de mejorar (por ejemplo, incrementar la precisión predictiva de un clasificador) el resultado del análisis. Con este objetivo, proponemos un método y definimos una arquitectura que emplea ideas de meta-aprendizaje para encontrar la relación entre transformaciones (operadores de pre-procesado) i algoritmos de minería de datos (algoritmos de clasificación). Esto, eventualmente, permite ordenar y recomendar transformaciones de acuerdo con el impacto potencial en el análisis. Para alcanzar este objetivo, primero estudiamos los métodos disponibles actualmente y los sistemas que proveen asistencia al usuario, tanto para los pasos individuales en análisis de datos como para el proceso completo. Posteriormente, clasificamos los metadatos que los diferentes sistemas usan y ponemos el foco específicamente en aquellos que usan metadatos para meta-aprendizaje. Aplicamos un método para estudiar el poder predictivo de los metadatos y extraemos y seleccionamos los metadatos más relevantes. Finalmente, nos centramos en la asistencia al usuario en el paso de pre-procesado de datos. Concebimos una arquitectura y construimos una herramienta, PRESISTANT, que dado un algoritmo de clasificación es capaz de recomendar operadores de pre-procesado que una vez aplicados impactan positivamente el resultado final (por ejemplo, incrementan la precisión predictiva). Nuestros resultados muestran que proveer asistencia al usuario en el pre-procesado de datos con el objetivo de mejorar el resultado del análisis es factible y muy útil para no-expertos. Además, esta tesis es un paso en la dirección de desmitificar que la tarea no trivial de pre-procesar datos esta solo al alcance de expertos.
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Chen, Chuan. "Numerical algorithms for data processing and analysis." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/277.

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Magnetic nanoparticles (NPs) with sizes ranging from 2 to 20 nm in diameter represent an important class of artificial nanostructured materials, since the NP size is comparable to the size of a magnetic domain. They have potential applications in data storage, catalysis, permanent magnetic nanocomposites, and biomedicine. To begin with, a brief overview on the background of Fe-based bimetallic NPs and their applications for data-storage and catalysis was presented in Chapter 1. In Chapter 2, L10-ordered FePt NPs with high coercivity were directly prepared from a novel bimetallic acetylenic alternating copolymer P3 by a one-step pyrolysis method without post-thermal annealing. The chemical ordering, morphology and magnetic properties were studied. Magnetic measurements showed that a record coercivity of 3.6 T (1 T = 10 kOe) was obtained in FePt NPs. By comparison of the resultant FePt NPs synthesized under Ar and Ar/H2, the characterization proved that the incorporation of H2 would affect the nucleation and promote the growth of FePt NPs. The L10 FePt NPs were also successfully patterned on Si substrate by nanoimprinting lihthography (NIL). The highly ordered ferromagnetic arrays on a desired substrate for bit-patterned media (BPM) were studied and promised bright prospects for the progress of data-storage. Furthermore, we also reported a new FePt-containing metallopolymer P4 as the single-source precursor for metal alloy NPs synthesis, where the metal fractions were on the side chain and the ratio could be easily controlled. This polymer was synthesized from random copolymer poly(styrene-4-ethynylstyrene) PES-PS and bimetallic precursor TPy-FePt ([Pt(4’-ferrocenyl-(N^N^N))Cl]Cl) by Sonogashira coupling reaction. After pyrolysis of P4, the stoichiometry of Fe and Pt atoms in the synthesized NPs (NPs) is nearly close to 1:1, which is more precise than using TPy-FePt as precursor. Polymer P4 was also more favorable for patterning by high throughout NIL as compared to TPy-FePt. Ferromagnetic nanolines, potentially as bit-patterned magnetic recording media, were successfully fabricated from P4 and fully characterized. In Chapter 3, a novel organometallic compound TPy-FePd-1 [4’-ferrocenyl-(N^N^N)PdOCOCH3] was synthesized and structurally characterized, whose crystal structure showed a coplanar Pd center and Pd-Pd distance (3.17 Å). Two metals Fe and Pd were evenly embedded in the molecular dimension and remained tightly coupled between each other benefiting to the metalmetal (Pd-Pd) and ligand ππ stacking interactions, all of which made it facilitate the nucleation without sintering during preparing the FePd NPs. Ferromagnetic FePd NPs of ca. 16.2 nm in diameter were synthesized by one-pot pyrolysis of the single-source precursor TPy-FePd-1 under getter gas with metal-ion reduction and minimal nanoparticle coalescence, which have a nearly equal atomic ratio (Fe/Pd = 49/51) and exhibited coercivity of 4.9 kOe at 300 K. By imprinting the mixed chloroform solution of TPy-FePd-1 and polystyrene (PS) on Si, reproducible patterning of nanochains was formed due to the excellent self-assembly properties and the incompatibility between TPy-FePd-1 and PS under the slow evaporation of the solvents. The FePd nanochains with average length of ca. 260 nm were evenly dispersed around the PS nanosphere by self-assembly of TPy-FePd-1. In addition, the orientation of the FePd nanochains could also be controlled by tuning the morphology of PS, and the length was shorter in confined space of PS. Orgnic skeleton in TPy-FePd-1 and PS were carbonized and removed by pyrolysis under Ar/H2 (5 wt%) and only magnetic FePd alloy nanochains with domain structure were left. Besides, a bimetallic complex TPy-FePd-2 was prepared and used as a single-source precursor to synthesize ferromagnetic FePd NPs by one-pot pyrolysis. The resultant FePd NPs have a mean size of 19.8 nm and show the coercivity of 1.02 kOe. In addition, the functional group (-NCMe) in TPy-FePd-2 was easily substituted by a pyridyl group. A random copolymer PS-P4VP was used to coordinate with TPy-FePd-2, and the as-synthesized polymer made the metal fraction disperse evenly along the flexible chain. Fabrication of FePd NPs from the polymers was also investigated, and the size could be easily controlled by tuning the metal fraction in polymer. FePd NPs with the mean size of 10.9, 14.2 and 17.9 nm were prepared from the metallopolymer with 5 wt%, 10 wt% and 20wt% of metal fractions, respectively. In Chapter 4, molybdenum disulfide (MoS2) monolayers decorated with ferromagnetic FeCo NPs on the edges were synthesized through a one-step pyrolysis of precursor molecules in an argon atmosphere. The FeCo precursor was spin coated on the MoS2 monolayer grown on Si/SiO2 substrate. Highly-ordered body-centered cubic (bcc) FeCo NPs were revealed under optimized pyrolysis conditions, possessing coercivity up to 1000 Oe at room temperature. The FeCo NPs were well-positioned along the edge sites of MoS2 monolayers. The vibration modes of Mo and S atoms were confined after FeCo NPs decoration, as characterized by Raman shift spectroscopy. These MoS2 monolayers decorated with ferromagnetic FeCo NPs can be used for novel catalytic materials with magnetic recycling capabilities. The sizes of NPs grown on MoS2 monolayers are more uniform than from other preparation routines. Finally, the optimized pyrolysis temperature and conditions provide receipts for decorating related noble catalytic materials. Finally, Chapters 5 and 6 present the concluding remarks and the experimental details of the work described in Chapters 2-4.
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Chen, George C. M. "Strategic analysis of a data processing company /." Burnaby B.C. : Simon Fraser University, 2005. http://ir.lib.sfu.ca/handle/1892/3624.

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Research Project (M.B.A.) - Simon Fraser University, 2005.
Research Project (Faculty of Business Administration) / Simon Fraser University. Senior supervisor : Dr. Ed Bukszar. EMBA Program. Also issued in digital format and available on the World Wide Web.
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Purahoo, K. "Maximum entropy data analysis." Thesis, Cranfield University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260038.

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Aygar, Alper. "Doppler Radar Data Processing And Classification." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609890/index.pdf.

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In this thesis, improving the performance of the automatic recognition of the Doppler radar targets is studied. The radar used in this study is a ground-surveillance doppler radar. Target types are car, truck, bus, tank, helicopter, moving man and running man. The input of this thesis is the output of the real doppler radar signals which are normalized and preprocessed (TRP vectors: Target Recognition Pattern vectors) in the doctorate thesis by Erdogan (2002). TRP vectors are normalized and homogenized doppler radar target signals with respect to target speed, target aspect angle and target range. Some target classes have repetitions in time in their TRPs. By the use of these repetitions, improvement of the target type classification performance is studied. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for doppler radar target classification and the results are evaluated. Before classification PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are implemented and applied to normalized doppler radar signals for feature extraction and dimension reduction in an efficient way. These techniques transform the input vectors, which are the normalized doppler radar signals, to another space. The effects of the implementation of these feature extraction algoritms and the use of the repetitions in doppler radar target signals on the doppler radar target classification performance are studied.
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Roberts, G. "Some aspects seismic signal processing and analysis." Thesis, Bangor University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.379692.

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Books on the topic "Coal Analysis Data processing"

1

T, Harper D. A., ed. Paleontological data analysis. Malden, MA: Blackwell Pub., 2006.

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Bock, R. K. The data analysis briefbook. Berlin: Springer, 1998.

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Ambrose, Carol Morgans Tremain. Colorado coal quality data. Denver, Colo: Colorado Geological Survey, 2001.

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Successful data processing system analysis. 2nd ed. Englewood Cliffs, N.J: Prentice-Hall, 1985.

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Wolff, Karl Erich, Dmitry E. Palchunov, Nikolay G. Zagoruiko, and Urs Andelfinger, eds. Knowledge Processing and Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22140-8.

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Frauke, Kreuter, ed. Data analysis using stata. 2nd ed. College Station, Tex: Stata Press, 2009.

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1962-, Martinez V. J., ed. Data analysis in cosmology. Berlin: Springer, 2009.

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V, Di Gesù, ed. Data analysis in astronomy. New York: Plenum Press, 1985.

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E, Basham Randall, ed. Data analysis with spreadsheets. Boston: Pearson/Allyn & Bacon, 2006.

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Data analysis using SAS. Thousand Oaks, Calif: SAGE Publications, 2009.

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Book chapters on the topic "Coal Analysis Data processing"

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Bingham, John. "Systems Analysis." In Data Processing, 91–105. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_8.

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Bingham, John. "The Techniques of Systems Analysis." In Data Processing, 106–38. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-19938-9_9.

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Akram, Jubran. "Microseismic Data Processing." In Understanding Downhole Microseismic Data Analysis, 55–122. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34017-9_3.

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Chekanov, Sergei V. "Data Analysis and Data Mining." In Advanced Information and Knowledge Processing, 431–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28531-3_12.

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Johansson, Robert. "Data Processing and Analysis." In Numerical Python, 285–311. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0553-2_12.

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Johansson, Robert. "Data Processing and Analysis." In Numerical Python, 405–41. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4246-9_12.

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Malley, Brian, Daniele Ramazzotti, and Joy Tzung-yu Wu. "Data Pre-processing." In Secondary Analysis of Electronic Health Records, 115–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43742-2_12.

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Cellerino, Alessandro, and Michele Sanguanini. "RNA-seq raw data processing." In Transcriptome Analysis, 27–44. Pisa: Scuola Normale Superiore, 2018. http://dx.doi.org/10.1007/978-88-7642-642-1_3.

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Ghosh, Chandril. "Data Pre-processing." In Data Analysis with Machine Learning for Psychologists, 55–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14634-3_3.

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Shi, Xizhi. "Data Analysis and Application Study." In Blind Signal Processing, 301–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11347-5_10.

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Conference papers on the topic "Coal Analysis Data processing"

1

Dong, Yanfang, Bihong Fu, and Ninomiya Yoshiki. "DEM generation methods and applications in revealing of topographic changes caused by coal mining activities." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815727.

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Kate, D. M., N. K. Choudhari, and A. R. Chaudhari. "Ultrasonic method for extraction of %C, %H, %N, %S of coal using signal processing application." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8390093.

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Yu, Yuefeng, Jun Ma, and Haojie Fan. "Spectral Analysis of Pulverized Coal Combustion Stability." In ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50323.

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Flame detecting and diagnosis of combustion in modern coal-fired boilers are very important to the safe and economical operating of power generation unit. So the effective real-time detection of combustion flame stability and in-time judgment are necessary. One of the important characteristics of combustion flame is a combination of flame jet fluctuation and flickering of flame radiation, which is a time random variable and reflects the combustion conditions. In this paper, after the data are acquired through the tests in our university’s laboratory, the power spectrum analysis using algorithm of fast Fourier transformation (FFT) and a self-organized neural network are applied into a diagnostic system for combustion conditions. At first, a time series of radiation intensity values of the flame, which fluctuate at a mean intensity value with a certain frequency are obtained through the photoelectric sensor. And then the time signal is converted to the power spectrum signal through the processing of FFT. Under the stable and unstable combustion conditions, the spectral intensity of the low frequency component of the converted signal has distinct magnitude. According to this method, software for the power spectrum analysis and the self-organized neural networks has been developed.
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Rodionova, N. V. "Satellite monitoring of the environment in the area of the Iskitim coal mines in 2013–2020." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.39.39.042.

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The paper considers the use of multispectral data from the Landsat-8, Sentinel-2, Aqua and Terra satellites for monitoring pollution in the areas of open-pit coal mines in the Iskitim district of the Novosibirsk region for the period 2013–2020. The change in the values of the reflection coefficient (RC) from the surface and water bodies, the snow index NDSI during the snowmelt period, the change of NDVI in the summer, in the area of Kolyvansky and Vostochny coal mines and in the area of the Linevo village are considered. The dynamics of the aerosol optical thickness (AOT) changes, CO and CH4 concentrations in the atmosphere of the Iskitim district using the Giovanni data analysis and visualization system are shown.
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Yuan, Yijun, Yuan Gao, and Ruifeng Zhang. "Imaging on Coal Seismic Data." In 2009 2nd International Congress on Image and Signal Processing (CISP). IEEE, 2009. http://dx.doi.org/10.1109/cisp.2009.5305804.

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Ma, Xianmin, and Jie Zang. "Coal Gangue Image Process Approaches with Wavelet Analysis." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.224.

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Song, Quanbin, Aibin Fang, Gang Xu, Yanji Xu, and Weiguang Huang. "Experimental Investigation of Thermoacoustic Oscillations in Syngas Premixed Multi-Swirler Model Combustors." In ASME Turbo Expo 2009: Power for Land, Sea, and Air. ASMEDC, 2009. http://dx.doi.org/10.1115/gt2009-59882.

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This paper presents the experimental results of the thermoacoustic oscillations in several premixed syngas multi-swirler model combustors. Multi-swirler lean premixed combustion technology has been successfully applied to achieve an anticipative stability, lower noise and high efficiency in industrial gas turbines that burn natural gas or distillated oil. However, some critical operational issues, including combustion oscillations, flashback, blowout and autoignition, should be considered and balanced when burning the coal-derived syngas mainly composed of H2 and CO. In this paper several multi-swirler combustors are tested on an atmospheric-pressure downscaled test rig. Each multi-swirler combustor includes several elemental swirling nozzles with the equal Swirl Number and different array of port configurations. The dynamic pressure, dynamic heat release and critical flashback equivalence ratio are tested in these model combustors burning several kinds of simulated syngases with a similar low heat value. Firstly, the critical equivalence ratios of flashback are shown and compared with those in single-swirler combustors. Secondly, the paper presents the analysis of the temporal and spectral features of dynamic pressure oscillations using many data-processing methods. Thirdly, we describe the bifurcation and retardation phenomenon when the combustion transforms between stable and unstable operations. We also discuss how the equivalence ratio, the fuel composition and the combustor inlet velocity play important roles in determining the amplitudes, the frequencies, the bifurcation and retardation of the thermoacoustic oscillations. Finally, we use a wavelet transformation with a higher resolution in time domain than that with a PSD estimation by the AR model. The processes of amplitude “jump” and flashback are analyzed in details. The results in this paper could improve the current understanding of the nonlinear self-excited and combustion driven thermoacoustic oscillations in gas turbines and give us some references to the development of lean premixed syngas turbines for coal-based IGCC and co-generation systems.
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Chen Xiang and Zhou Haikun. "Coal Enterprises Safety Investment Efficiency Analysis Based on Data Envelopment." In 2010 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010). IEEE, 2010. http://dx.doi.org/10.1109/wkdd.2010.73.

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van Vuuren, Pieter A., H. C. Dorland, M. le Roux, W. C. Venter, P. Erasmus, M. I. Dorland, and Q. P. Campbell. "Using visual texture analysis to classify raw coal components." In 2015 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2015. http://dx.doi.org/10.1109/iwssip.2015.7314214.

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Zhang, Tie-ju, and Jie Sun. "Wavelet Analysis on Crack Image of Coal Mining Roof." In 2009 Second International Workshop on Knowledge Discovery and Data Mining (WKDD). IEEE, 2009. http://dx.doi.org/10.1109/wkdd.2009.194.

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Reports on the topic "Coal Analysis Data processing"

1

Labonté, M. Description of computer methods and computer programs for correspondence analysis and use of the dendograph analysis as means of coal data processing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1989. http://dx.doi.org/10.4095/126758.

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Fowler, Kimberly M., Alison H. A. Colotelo, Janelle L. Downs, Kenneth D. Ham, Jordan W. Henderson, Sadie A. Montgomery, Christopher R. Vernon, and Steven A. Parker. Simplified Processing Method for Meter Data Analysis. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1255411.

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Hodgkiss, W. S. Shallow Water Adaptive Array Processing and Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada306525.

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Boyd, Timothy J. Processing and Analysis of SCICEX-2000 CTD Data. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada628072.

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Boyd, Timothy. Processing and Analysis of SCICEX-2000 CTD Data. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada626128.

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Spina, John F. Integrated RF Sensor Signal/Data Processing Information Analysis Center (IAC). Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada401075.

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Konovalov, Mikhail. Analysis of Industrial Software Solutions for Data Processing and Storage. Intellectual Archive, March 2019. http://dx.doi.org/10.32370/iaj.2071.

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Cheng, Yi-Wen, and Christian L. Sargent. Data-reduction and analysis procedures used in NIST's thermomechanical processing research. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.3950.

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Davis, Dennis, and Robert T. Kroutil. Application of Novel Data Processing Techniques to the Analysis of Ion Mobility Spectrometry (IMS) Data. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada219976.

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Ranjan, Niloo, Jibonananda Sanyal, and Joshua Ryan New. In-Situ Statistical Analysis of Autotune Simulation Data using Graphical Processing Units. Office of Scientific and Technical Information (OSTI), August 2013. http://dx.doi.org/10.2172/1093099.

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