Search results for: and coal mining industry

Authors: Bitan Kumar Sarkar, Akashdeep Agarwal, Rajib Dey, Gopes Chandra Das

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The rapid depletion of high-grade iron ore (Fe₂O₃) has gained attention on the use of other sources of iron ore. Titaniferous magnetite ore (TMO) is a special type of magnetite ore having high titania content (23.23% TiO₂ present in this case). Due to high TiO₂ content and high density, TMO cannot be treated by the conventional smelting reduction. In this present work, the TMO has been collected from high-grade metamorphic terrain of the Precambrian Chotanagpur gneissic complex situated in the eastern part of India (Shaltora area, Bankura district, West Bengal) and the hematite ore has been collected from Visakhapatnam Steel Plant (VSP), Visakhapatnam. At VSP, iron ore is received from Bailadila mines, Chattisgarh of M/s. National Mineral Development Corporation. The preliminary characterization of TMO and hematite ore (HMO) has been investigated by WDXRF, XRD and FESEM analyses. Similarly, good quality of coal (mainly coking coal) is also getting depleted fast. The basic purpose of this work is to find how lean grade coal can be utilised along with TMO for smelting to produce pig iron. Lean grade coal has been characterised by using TG/DTA, proximate and ultimate analyses. The boiler grade coal has been found to contain 28.08% of fixed carbon and 28.31% of volatile matter. TMO fines (below 75 μm) and HMO fines (below 75 μm) have been separately agglomerated with lean grade coal fines (below 75 μm) in the form of briquettes using binders like bentonite and molasses. These green briquettes are dried first in oven at 423 K for 30 min and then reduced isothermally in tube furnace over the temperature range of 1323 K, 1373 K and 1423 K for 30 min & 60 min. After reduction, the reduced briquettes are characterized by XRD and FESEM analyses. The best reduced TMO and HMO samples are taken and blended in three different weight percentage ratios of 1:4, 1:8 and 1:12 of TMO:HMO. The chemical analysis of three blended samples is carried out and degree of metallisation of iron is found to contain 89.38%, 92.12% and 93.12%, respectively. These three blended samples are briquetted using binder like bentonite and lime. Thereafter these blended briquettes are separately smelted in raising hearth furnace at 1773 K for 30 min. The pig iron formed is characterized using XRD, microscopic analysis. It can be concluded that 90% yield of pig iron can be achieved when the blend ratio of TMO:HMO is 1:4.5. This means for 90% yield, the maximum TMO that could be used in the blend is about 18%.

Keywords: briquetting reduction, lean grade coal, smelting reduction, TMO

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Authors: Venkata Ramanaiah, Shabina Khanam

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Enormous potential for saving energy is available in coal-based sponge iron plants as these are associated with the high percentage of energy wastage per unit sponge iron production. An energy integration option is proposed, in the present paper, to a coal based sponge iron plant of 100 tonnes per day production capacity, being operated in India using SL/RN (Stelco-Lurgi/Republic Steel-National Lead) process. It consists of the rotary kiln, rotary cooler, dust settling chamber, after burning chamber, evaporating cooler, electrostatic precipitator (ESP), wet scrapper and chimney as important equipment. Principles of process integration are used in the proposed option. It accounts for preheating kiln inlet streams like kiln feed and slinger coal up to 170ᴼC using waste gas exiting ESP. Further, kiln outlet stream is cooled from 1020ᴼC to 110ᴼC using kiln air. The working areas in the plant where energy is being lost and can be conserved are identified. Detailed material and energy balances are carried out around the sponge iron plant, and a modified model is developed, to find coal requirement of proposed option, based on hot utility, heat of reactions, kiln feed and air preheating, radiation losses, dolomite decomposition, the heat required to vaporize the coal volatiles, etc. As coal is used as utility and process stream, an iterative approach is used in solution methodology to compute coal consumption. Further, water consumption, operating cost, capital investment, waste gas generation, profit, and payback period of the modification are computed. Along with these, operational aspects of the proposed design are also discussed. To recover and integrate waste heat available in the plant, three gas-solid heat exchangers and four insulated ducts with one FD fan for each are installed additionally. Thus, the proposed option requires total capital investment of $0.84 million. Preheating of kiln feed, slinger coal and kiln air streams reduce coal consumption by 24.63% which in turn reduces waste gas generation by 25.2% in comparison to the existing process. Moreover, 96% reduction in water is also observed, which is the added advantage of the modification. Consequently, total profit is found as $2.06 million/year with payback period of 4.97 months only. The energy efficient factor (EEF), which is the % of the maximum energy that can be saved through design, is found to be 56.7%. Results of the proposed option are also compared with literature and found in good agreement.

Keywords: coal consumption, energy conservation, process integration, sponge iron plant

Procedia PDF Downloads 123

Authors: Mustafa Col, Funda Gul Koc, Merve Yangaz, Eylem Subasi, Can Akbasoglu

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G38NiCrMo8-4-4 steel is widely used in mining industries, machine parts, gears due to its high strength and toughness properties. In this study, microstructure, wear and mechanical properties of G38NiCrMo8-4-4 steel modified with boron used in the mining industry were investigated. For this purpose, cast materials were alloyed by melting in an induction furnace to include boron with the rates of 0 ppm, 15 ppm, and 50 ppm (wt.) and were formed in the dimensions of 150x200x150 mm by casting into the sand mould. Homogenization heat treatment was applied to the specimens at 1150˚C for 7 hours. Then all specimens were austenitized at 930˚C for 1 hour, quenched in the polymer solution and tempered at 650˚C for 1 hour. Microstructures of the specimens were investigated by using light microscope and SEM to determine the effect of boron and heat treatment conditions. Changes in microstructure properties and material hardness were obtained due to increasing boron content and heat treatment conditions after microstructure investigations and hardness tests. Wear tests were carried out using a pin-on-disc tribometer under dry sliding conditions. Charpy V notch impact test was performed to determine the toughness properties of the specimens. Fracture and worn surfaces were investigated with scanning electron microscope (SEM). The results show that boron element has a positive effect on the hardness and wear properties of G38NiCrMo8-4-4 steel.

Keywords: G38NiCrMo8-4-4 steel, boron, heat treatment, microstructure, wear, mechanical properties

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Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

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Authors: Tafadzwa Makonese, Harold Annegarn, Patricia Forbes

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Residential coal combustion is one of the largest sources of carbonaceous aerosols in the Highveld region of South Africa, significantly affecting the local and regional climate. In this study, we investigated single coal burning particles emitted when using different fire-ignition techniques (top-lit up-draft vs bottom-lit up-draft) and air ventilation rates (defined by the number of air holes above and below the fire grate) in selected informal braziers. Aerosol samples were collected on nucleopore filters at the SeTAR Centre Laboratory, University of Johannesburg. Individual particles (~700) were investigated using a scanning electron microscope equipped with an energy-dispersive X-ray spectroscopy (EDS). Two distinct forms of spherical organic particles (SOPs) were identified, one less oxidized than the other. The particles were further classified into "electronically" dark and bright, according to China et al. [2014]. EDS analysis showed that 70% of the dark spherical organic particles balls had higher (~60%) relative oxygen content than in the bright SOPs. We quantify the morphology of spherical organic particles and classify them into four categories: ~50% are bare single particles; ~35% particles are aggregated and form diffusion accretion chains; 10% have inclusions; and 5% are deformed due to impaction on filter material during sampling. We conclude that there are two distinct kinds of coal burning spherical organic particles and that dark SOPs are less volatile than bright SOPs. We also show that these spherical organic particles are similar in nature and characteristics to tar balls observed in biomass combustion, and that they have the potential to absorb sunlight thereby affecting the earth’s radiative budget and climate. This study provides insights on the mixing states, morphology, and possible formation mechanisms of these organic particles from residential coal combustion in informal stoves.

Keywords: spherical organic particles, residential coal combustion, fixed-bed, aerosols, morphology, stoves

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Adarsh Shroff

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Big data is a collection of dataset so large and complex that it becomes difficult to process using data base management tools. To perform operations like search, analysis, visualization on big data by using data mining; which is the process of extraction of patterns or knowledge from large data set. In recent years, the data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. This project uses i2MapReduce, an incremental processing extension to Map Reduce, the most widely used framework for mining big data. I2MapReduce performs key-value pair level incremental processing rather than task level re-computation, supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. To optimize the mining results, evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics for efficient mining.

Keywords: big data, map reduce, incremental processing, iterative computation

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Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

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Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

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Authors: Suzan Wedyan

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Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases.

Keywords: associative classification, classification, data mining, learning, rule ranking, rule pruning, prediction

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Authors: M. Sai Veerendra

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Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project on developing a new data mining solution for AML Units in an international investment bank in Ireland, we survey recent data mining approaches for AML. In this paper, we present not only these approaches but also give an overview on the important factors in building data mining solutions for AML activities.

Keywords: data mining, clustering, money laundering, anti-money laundering solutions

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Authors: Andi Fallahi, Bona Ryan Situmeang

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The Construction Industry is notoriously risky. Much of the preparatory paperwork that precedes construction project can be viewed as the formulation of risk allocation between the owner and the Contractor. The Owner is taking the risk that his project will not get built on the schedule that it will not get built for what he has budgeted and that it will not be of the quality he expected. The Contractor Face a multitude of risk. One of them is an unforeseen condition at the construction site. The Owner usually has the upper hand here if the unforeseen condition occurred. Site data contained in Ground Investigation report is often of significant contractual importance in disputes related to the unforeseen ground condition. A ground investigation can never fully disclose all the details of the underground condition (Risk of an unknown ground condition can never be 100% eliminated). Adipala Coal Fired Steam Power Plant (CSFPP) 1 x 660 project is one of the large CSFPP project in Indonesia based on Engineering, Procurement, and Construction (EPC) Contract. Unforeseen Ground Condition it’s responsible by the Contractor has stipulated in the clausal of Contract. In the implementation, there’s indicated unforeseen ground condition at Circulating Water Pump House (CWPH) area which caused the Contractor should be changed the Method of Work that give big impact against Time of Completion and Cost Project. This paper tries to analyze the best way for allocating the risk between The Owner and The Contractor. All parties that allocating of sharing risk fairly can ultimately save time and money for all parties, and get the job done on schedule for the least overall cost.

Keywords: unforeseen ground condition, coal fired steam power plant, circulating water pump house, Indonesia

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Authors: Linus O. Asuquo

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Mining in Nigeria and development effort of metallurgical technologies at National Metallurgical Development Centre Jos has been addressed in this paper. The paper has looked at the history of mining in Nigeria, the impact of mining on social and industrial development, and the contribution of the mining sector to Nigeria’s Gross Domestic Product (GDP). The paper clearly stated that Nigeria’s mining sector only contributes 0.5% to the nation’s GDP unlike Botswana that the mining sector contributes 38% to the nation’s GDP. Nigeria Bureau of Statistics has it on record that Nigeria has about 44 solid minerals awaiting to be exploited. Clearly highlighted by this paper is the abundant potentials that exist in the mining sector for investment. The paper made an exposition on the extensive efforts made at National Metallurgical Development Center (NMDC) to develop metallurgical technologies in various areas of the metals sector; like mineral processing, foundry development, nonferrous metals extraction, materials testing, lime calcination, ANO (Trade name for powder lubricant) wire drawing lubricant, refractories and many others. The paper went ahead to draw a conclusion that there is a need to develop the mining sector in Nigeria and to give a sustainable support to the efforts currently made at NMDC to develop metallurgical technologies which are capable of transforming the metals sector in Nigeria, which will lead to industrialization. Finally the paper made some recommendations which traverse the topic for the best expectation.

Keywords: mining, minerals, technologies, value addition

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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Authors: Tibor Ajtai, Noémi Utry, Máté Pintér, Tomi Smausz, Zoltán Kónya, Béla Hopp, Gábor Szabó, Zoltán Bozóki

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Characterization of spectral responses of light absorbing carbonaceous particulate matter (LAC) is of great importance in both modelling its climate effect and interpreting remote sensing measurement data. The residential or domestic combustion of coal is one of the dominant LAC constituent. According to some related assessments the residential coal burning account for roughly half of anthropogenic BC emitted from fossil fuel burning. Despite of its significance in climate the comprehensive investigation of optical properties of residential coal aerosol is really limited in the literature. There are many reason of that starting from the difficulties associated with the controlled burning conditions of the fuel, through the lack of detailed supplementary proximate and ultimate chemical analysis enforced, the interpretation of the measured optical data, ending with many analytical and methodological difficulties regarding the in-situ measurement of coal aerosol spectral responses. Since the gas matrix of ambient can significantly mask the physicochemical characteristics of the generated coal aerosol the accurate and controlled generation of residential coal particulates is one of the most actual issues in this research area. Most of the laboratory imitation of residential coal combustion is simply based on coal burning in stove with ambient air support allowing one to measure only the apparent spectral feature of the particulates. However, the recently introduced methodology based on a laser ablation of solid coal target opens up novel possibilities to model the real combustion procedure under well controlled laboratory conditions and makes the investigation of the inherent optical properties also possible. Most of the methodology for spectral characterization of LAC is based on transmission measurement made of filter accumulated aerosol or deduced indirectly from parallel measurements of scattering and extinction coefficient using free floating sampling. In the former one the accuracy while in the latter one the sensitivity are liming the applicability of this approaches. Although the scientific community are at the common platform that aerosol-phase PhotoAcoustic Spectroscopy (PAS) is the only method for precise and accurate determination of light absorption by LAC, the PAS based instrumentation for spectral characterization of absorption has only been recently introduced. In this study, the investigation of the inherent, spectral features of laser generated and chemically characterized residential coal aerosols are demonstrated. The experimental set-up and its characteristic for residential coal aerosol generation are introduced here. The optical absorption and the scattering coefficients as well as their wavelength dependency are determined by our state-of-the-art multi wavelength PAS instrument (4λ-PAS) and multi wavelength cosinus sensor (Aurora 3000). The quantified wavelength dependency (AAE and SAE) are deduced from the measured data. Finally, some correlation between the proximate and ultimate chemical as well as the measured or deduced optical parameters are also revealed.

Keywords: absorption, scattering, residential coal, aerosol generation by laser ablation

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Authors: Şakir Bingöl, Bayram İslamoğlu, Ebubekir Furkan Tepeli, Fatih Mehmet Karakule, Fatih Küçük, Merve Sena Arpacık, Mustafa Taha Kabar, Muhammet Metin Molak, Osman Emre Turan, Ömer Faruk Yesir, Sıla İnanır

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This study introduces an IoT-based real-time safety system for mining, addressing global safety challenges. The wearable device, seamlessly integrated into miners' jackets, employs LoRa technology for communication and offers real-time monitoring of vital health and environmental data. Unique features include an LCD panel for immediate information display and sound-based location tracking for emergency response. The methodology involves sensor integration, data transmission, and ethical testing. Validation confirms the system's effectiveness in diverse mining scenarios. The study calls for ongoing research to adapt the system to different mining contexts, emphasizing its potential to significantly enhance safety standards in the industry.

Keywords: mining safety, internet of things, wearable technology, LoRa, RFID tracking, real-time safety system, safety alerts, safety measures

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Authors: Awatef Hicheur Cairns, Billel Gueni, Hind Hafdi, Christian Joubert, Nasser Khelifa

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Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to discover, analyze and provide a visual representation of complete educational processes. In this paper, we present our distributed computation platform which allows different education centers and institutions to load their data and access to advanced data mining and process mining services. To achieve this, we present also a comparative study of the different clustering techniques developed in the context of process mining to partition efficiently educational traces. Our goal is to find the best strategy for distributing heavy analysis computations on many processing nodes of our platform.

Keywords: educational process mining, distributed process mining, clustering, distributed platform, educational data mining, ProM

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Authors: Sahar Khedr, Dina Sayed, Ayman Hanafy

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Arabic is one of the most ancient and critical languages in the world. It has over than 250 million Arabic native speakers and more than twenty countries having Arabic as one of its official languages. In the past decade, we have witnessed a rapid evolution in smart devices, social network and technology sector which led to the need to provide tools and libraries that properly tackle the Arabic language in different domains. Stemming is one of the most crucial linguistic fundamentals. It is used in many applications especially in information extraction and text mining fields. The motivation behind this work is to enhance the Arabic light stemmer to serve the data mining industry and leverage it in an open source community. The presented implementation works on enhancing the Arabic light stemmer by utilizing and enhancing an algorithm that provides an extension for a new set of rules and patterns accompanied by adjusted procedure. This study has proven a significant enhancement for better search accuracy with an average 10% improvement in comparison with previous works.

Keywords: Arabic data mining, Arabic Information extraction, Arabic Light stemmer, Arabic stemmer

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Authors: Ketan Mahawer, Abeer Mutto, S. K. Gupta

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The mining wastewater contains inorganic metal salts, which makes it saline and additionally contributes to contaminating the surface and underground freshwater reserves that exist nearby mineral processing industries. Therefore, treatment of wastewater and water recovery is obligatory by any available technology before disposing it into the environment. Currently, reverse osmosis (RO) is the commercially acceptable conventional membrane process for saline wastewater treatment, but consumes an enormous amount of energy and makes the process expensive. To solve this industrial problem with minimum energy consumption, we tested the feasibility of forward osmosis-nanofiltration (FO-NF) hybrid process for the mining wastewater treatment. The FO-NF process experimental results for 0.029M concentration of saline wastewater treated by 0.42 M sodium-sulfate based draw solution shows that specific energy consumption of the FO-NF process compared with standalone NF was slightly above (between 0.5-1 kWh/m3) from conventional process. However, average freshwater recovery was 30% more from standalone NF with same feed and operating conditions. Hence, FO-NF process in place of RO/NF offers a huge possibility for treating mining industry wastewater and concentrates the metals as the by-products without consuming an excessive/large amount of energy and in addition, mitigates the fouling in long periods of treatment, which also decreases the maintenance and replacement cost of the separation process.

Keywords: forward osmosis, nanofiltration, mining, draw solution, divalent solute

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Authors: Ruzanna Muradyan

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Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

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Authors: Jianjun Wu, Fanhui Guo, Yixin Zhang

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In this study, gasification-coke were produced by blending the middlings (MC), and coking coal (CC) and a large proportion of long flame coal (Shenfu coal, SC), the effects of blending ratio were investigated. Mineral evolution and crystalline order obtained by XRD methods were reproduced within reasonable accuracy. Structure characteristics of partially gasification-coke such as surface area and porosity were determined using the N₂ adsorption and mercury porosimetry. Experimental data of gasification-coke was dominated by the TGA results provided trend, reactivity differences between gasification-cokes are discussed in terms of structure characteristic, crystallinity, and alkali index (AI). The first-order reaction equation was suitable for the gasification reaction kinetics of CO₂ atmosphere which was represented by the volumetric reaction model with linear correlation coefficient above 0.985. The differences in the microporous structure of gasification-coke and catalysis caused by the minerals in parent coals were supposed to be the main factors which affect its reactivity. The addition of MC made the samples enriched with a large amount of ash causing a higher surface area and a lower crystalline order to gasification-coke which was beneficial to gasification reaction. The higher SiO₂ and Al₂O₃ contents, causing a decreasing AI value and increasing activation energy, which reduced the gasification reaction activity. It was found that the increasing amount of MC got a better performance on the coke gasification reactivity by blending > 30% SC with this coking process.

Keywords: low-rank coal, middlings, structure characteristic, mineral evolution, alkali index, gasification-coke, gasification kinetics

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Authors: Seema Vaidya

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Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient.

Keywords: association rule, data mining, IWI mining, infrequent item set, frequent pattern growth

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Authors: Jacqui Robertson

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The sustainable governance of groundwater is of the utmost importance in an arid country like Australia. Groundwater has been relied on by our agricultural and pastoral communities since the State was settled by European colonialists. Nevertheless, the rapid establishment of a coal seam gas (CSG) industry in Queensland, Australia, has had extensive impacts on the pre-existing groundwater users. Managed aquifer recharge of important aquifers in Queensland, Australia, using treated coal seam gas produced water has been used to reduce the impacts of CSG development in Queensland Australia. However, the process has not been widely adopted. Negative environmental outcomes are now acknowledged as not only engineering, scientific or technical problems to be solved but also the result of governance failures. An analysis of the regulatory context for aquifer injection using treated CSG water in Queensland, Australia, using Ostrom’s Common Pool Resource (CPR) theory and a ‘heat map’ designed by the author, highlights the importance of governance arrangements. The analysis reveals the costs and benefits for relevant stakeholders of artificial recharge of groundwater resources in this context. The research also reveals missed opportunities to further active management of the aquifer and resolve existing conflicts between users. The research illustrates the importance of strategically and holistically evaluating innovations in technology that impact water resources to reveal incentives that impact resource user behaviors. The paper presents a proactive step that can be adapted to support integrated water resource management and sustainable groundwater development.

Keywords: managed aquifer recharge, groundwater regulation, common-pool resources, integrated water resource management, Australia

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Authors: Vladimir Messerle, Alexandr Ustimenko, Oleg Lavrichshev

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To enhance the efficiency of solid fuels’ use, to decrease the fuel oil rate in the thermal power plants fuel balance and to minimize harmful emissions, a plasma technology of coal ignition, gasification and incineration is successfully applied. This technology is plasma thermochemical preparation of fuel for burning (PTCPF). In the framework of this concept, some portion of pulverized solid fuel (PF) is separated from the main PF flow and undergone the activation by arc plasma in a specific chamber with plasma torch – PFS. The air plasma flame is a source of heat and additional oxidation, it provides a high-temperature medium enriched with radicals, where the fuel mixture is heated, volatile components of coal are extracted, and carbon is partially gasified. This active blended fuel can ignite the main PF flow supplied into the furnace. This technology provides the boiler start-up and stabilization of PF flame and eliminates the necessity for addition of highly reactive fuel. In the report, a model of PTCPF, implemented as a program PlasmaKinTherm for the PFS calculation is described. The model combines thermodynamic and kinetic methods for describing the process of PTCPF in PFS. The numerical investigation of operational parameters of PFS depending on the electric power of the plasma generator and steam coal ash content revealed the temperature and velocity of gas and coal particles, and concentrations of PTCPF products dependences on the PFS length. Main mechanisms of PTCPF were disclosed. It was found that in the range of electric power of plasma generator from 40 to 100 kW high ash bituminous coal, having consumption 1667 kg/h is ignited stably. High level of temperature (1740 K) and concentration of combustible components (44%) at the PFS exit is a confirmation of it. Augmentation in power of plasma generator results displacement maxima temperatures and speeds of PTCPF products upstream (in the direction of the plasma source). The maximum temperature and velocity vary in a narrow range of values and practically do not depend on the power of the plasma torch. The numerical study of indicators of the process of PTCPF depending on the ash content in the range of its values 20-70% demonstrated that at the exit of PFS concentration of combustible components decreases with an increase in coal ash, the temperature of the gaseous products is increasing, and coal carbon conversion rate is increased to a maximum value when the ash content of 60%, dramatically decreasing with further increase in the ash content.

Keywords: coal, efficiency, ignition, numerical modeling, plasma generator, plasma-fuel system

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Authors: Alma Khasenovna Zhakina, Arnt Oxana Vasilievna, Vasilets Evgeny Petrovich

Abstract:

The aim of this project is to develop methods for obtaining new molecularly imprinted polymers from coal waste to study their structure, structural and morphological features and properties. Recently, the development of molecularly imprinted polymers has become one of the hot topics for researchers. Modern research indicates the broad prospects of rapidly developing molecular imprinting technologies for creating a new generation of sorption materials. The attractiveness of this area of research lies in the fact that the use of imprinted polymers is not limited to scientific research; they are already being introduced in the chemical, pharmaceutical and biotechnological industries, primarily at the stages of purification of the final product. For the use of molecularly imprinted polymers in the development of sorption material, their ability to selectively remove pollutants, including trace concentrations, is of fundamental importance, and the exceptional stability of polymeric materials under harsh conditions makes it possible to simplify the process of water purification as a whole. The scientific and technical effect is associated with the development of technologies for the production of new molecularly imprinted polymers, the establishment of optimal conditions for their production and the creation of effective imprinted sorbents on their basis for wastewater treatment from heavy metals. The social effect is due to the fact that the use of coal waste as a feedstock for the production of imprinted sorbents will make it possible in the future to create new industries with additional jobs and obtain competitive multi-purpose products. The economic and multiplier effect is associated with the low cost of the final product due to the involvement of local coal waste in the production, reduction of transport, customs and other costs.

Keywords: imprinted polymers, coal waste, polymerization, template, customized sorbents

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Authors: Honggang Qu, Yong Xu, Rongmei Liu, Zhenji Gao, Bin Wang

Abstract:

Today's mining industry is advancing gradually toward digital and visual direction. The three-dimensional visualization geological modeling of mine is the digital characterization of mineral deposits and is one of the key technology of digital mining. Three-dimensional geological modeling is a technology that combines geological spatial information management, geological interpretation, geological spatial analysis and prediction, geostatistical analysis, entity content analysis and graphic visualization in a three-dimensional environment with computer technology and is used in geological analysis. In this paper, the three-dimensional geological modeling of an iron mine through the use of Surpac is constructed, and the weight difference of the estimation methods between the distance power inverse ratio method and ordinary kriging is studied, and the ore body volume and reserves are simulated and calculated by using these two methods. Compared with the actual mine reserves, its result is relatively accurate, so it provides scientific bases for mine resource assessment, reserve calculation, mining design and so on.

Keywords: three-dimensional geological modeling, geological database, geostatistics, block model

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Authors: J. Suneetha, Vijayalaxmi

Abstract:

Sequential Pattern Mining involves applying data mining methods to large data repositories to extract usage patterns. Sequential pattern mining methodologies used to analyze the data and identify patterns. The patterns have been used to implement efficient systems can recommend on previously observed patterns, in making predictions, improve usability of systems, detecting events, and in general help in making strategic product decisions. In this paper, identified performance of approximate sequential pattern mining defines as identifying patterns approximately shared with many sequences. Approximate sequential patterns can effectively summarize and represent the databases by identifying the underlying trends in the data. Conducting an extensive and systematic performance over synthetic and real data. The results demonstrate that ApproxMAP effective and scalable in mining large sequences databases with long patterns.

Keywords: multiple data, performance analysis, sequential pattern, sequence database scalability

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Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

Abstract:

Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

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Authors: Tanvi P. Patel, Warish D. Patel

Abstract:

Association rule mining forms the core of data mining and it is termed as one of the well-known methodologies of data mining. Objectives of mining is to find interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. For efficient targeted query processing, finding frequent patterns and itemset mining, there is an efficient way to generate an itemset tree structure named Memory Efficient Itemset Tree. Memory efficient IT is efficient for storing itemsets, but takes more time as compare to traditional IT. The proposed strategy generates maximal frequent itemsets from memory efficient itemset tree by using levelwise pruning. For that firstly pre-pruning of items based on minimum support count is carried out followed by itemset tree reconstruction. By having maximal frequent itemsets, less number of patterns are generated as well as tree size is also reduced as compared to MEIT. Therefore, an enhanced approach of memory efficient IT proposed here, helps to optimize main memory overhead as well as reduce processing time.

Keywords: association rule mining, itemset mining, itemset tree, meit, maximal frequent pattern

Procedia PDF Downloads 346

Authors: Muhammad Shahid, Kafait Ullah, Kashif Imran, Arshad Mahmood, Maarten Arentsen

Abstract:

Poor planning and governance in the power sector of Pakistan have generated several issues ranging from gradual reliance on thermal-based expensive energy mix, supply shortages, unrestricted demand, subsidization, inefficiencies at different levels of the value chain and resultantly, the circular debt. This situation in the power sector has also hampered the growth of allied economic sectors. This study uses the Long-range Energy Alternative Planning (LEAP) system for electricity modelling of Pakistan from the period of 2016 to 2040. The study has first time in Pakistan forecasted the electricity demand at the provincial level. At the supply side, five scenarios Business as Usual Scenario (BAUS), Coal Scenario (CS), Gas Scenario (GS), Nuclear Scenario (NS) and Renewable Scenario (RS) have been analyzed based on the techno-economic and environmental parameters. The study has also included environmental externality costs for evaluating the actual costs and benefits of different scenarios. Contrary to the expectations, RS has a lower output than even BAUS. The study has concluded that the generation from RS has five times lesser costs than BAUS, CS, and GS. NS can also be an alternative for the sustainable future of Pakistan. Generation from imported coal is not a good option, however, indigenous coal with clean coal technologies should be promoted. This paper proposes energy planners of the country to devise incentives for the utilization of indigenous energy resources including renewables on priority and then clean coal to reduce the energy crises of Pakistan.

Keywords: economic evaluation, externality cost, penetration of renewable energy, regional electricity supply-demand planning

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Authors: Fahad Hanash Alzahrani

Abstract:

This paper describes some recent advances in a quickly developing area of data storing and processing based on Data Warehouses and Data Mining techniques, which are associated with software, hardware, data mining algorithms and visualisation techniques having common features for any specific problems and tasks of their implementation.

Keywords: data warehouse, data mining, knowledge discovery in databases, on-line analytical processing

Procedia PDF Downloads 370