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Relevant bibliographies by topics / Selective agglomeration

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Academic literature on the topic 'Selective agglomeration'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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Journal articles on the topic "Selective agglomeration"

1

Abakay Temel, Halime, Volkan Bozkurt, and Arun Kumar Majumder. "Selective Oil Agglomeration of Lignite." Energy & Fuels 23, no. 2 (2009): 779–84. http://dx.doi.org/10.1021/ef8005096.

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Slaghuis, Johan H., and Leon C. Ferreira. "Selective spherical agglomeration of coal." Fuel 66, no. 10 (1987): 1427–30. http://dx.doi.org/10.1016/0016-2361(87)90191-8.

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JANIAK, J., W. PAWLAK, K. SZYMOCHA, B. IGNASIAK, and C. M. RODKIEWICZ. "Selective Agglomeration During Pipelining of Slurries." Coal Preparation 1, no. 2 (1985): 155–67. http://dx.doi.org/10.1080/07349348508945546.

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Hucko, R. E., R. P. Killmeyer, and P. S. Jacobsen. "Selective agglomeration: An interlaboratory test program." Mining, Metallurgy & Exploration 7, no. 2 (1990): 74–78. http://dx.doi.org/10.1007/bf03402903.

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Abakay, Halime, Fatma Deniz Ayhan, and Fikri Kahraman. "Selective oil agglomeration in Sırnak asphaltite beneficiation." Fuel 83, no. 14-15 (2004): 2081–86. http://dx.doi.org/10.1016/j.fuel.2004.05.001.

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Uwadiale, G. G. O. O. "Selective oil agglomeration of Lafia coal, Nigeria." Mining, Metallurgy & Exploration 13, no. 4 (1996): 179–80. http://dx.doi.org/10.1007/bf03402743.

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Uwadiale, G. G. O. O. "Selective oil agglomeration of Agbaja iron ore." Mining, Metallurgy & Exploration 7, no. 3 (1990): 132–35. http://dx.doi.org/10.1007/bf03403287.

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House, C. I., and C. J. Veal. "Selective recovery of chalcopyrite by spherical agglomeration." Minerals Engineering 2, no. 2 (1989): 171–84. http://dx.doi.org/10.1016/0892-6875(89)90038-1.

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Takase, Hitoshi. "Influence of interparticle forces on selective wet agglomeration." Advanced Powder Technology 20, no. 4 (2009): 327–34. http://dx.doi.org/10.1016/j.apt.2009.01.005.

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Skarvelakis, C., M. Hazi, and G. Antonini. "Investigations of Coal Purification by Selective Oil Agglomeration." Separation Science and Technology 30, no. 12 (1995): 2519–38. http://dx.doi.org/10.1080/01496399508021399.

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Dissertations / Theses on the topic "Selective agglomeration"

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Snyder, Bret Alan. "De-inking toner-printed paper by selective agglomeration /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/9886.

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Rahmani, Ali A. "Studies on selective flocculation and spherical oil agglomeration for coal de-mineralisation." Thesis, University of Manchester, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302923.

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Smith, Kara E. "Cleaning and Dewatering Fine Coal using Hydrophobic Displacement." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/33416.

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A new processing technique, known as hydrophobic displacement, was explored as a means of simultaneously removing both mineral matter and surface moisture from coal in a single process. Previous thermodynamic analysis suggests that coal moisture will be spontaneously displaced by any oil with a contact angle greater than ninety degrees in water. Based on these results, six methods of hydrophobic displacement were evaluated: hand shaking, screening, air classification, centrifugation, filtration, and displacement. In the first five methods hydrophobic displacement took place during the clean

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Douch, Mustapha. "Essays on firm performance, agglomeration and international trade." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/27620.

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Hasan, Syed M. "Three Essays on Export and Productivity-Impact of Financial Constraints and Technological Innovation." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1405352293.

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van, Netten Kim. "Maximising binder functionality in selective agglomeration." Thesis, 2016. http://hdl.handle.net/1959.13/1315761.

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Research Doctorate - Doctor of Philosophy (PhD)<br>This study was concerned with improving this process of selective agglomeration using an immiscible binding liquid by maximising the functionality of the binder. In the past, research into the area of selective agglomeration has focussed on the use of only pure oils as binders. Pure oils are highly suitable, in a technical sense, as they are hydrophobic, immiscible with water and will preferentially wet hydrophobic particles in a mixture of hydrophobic and hydrophilic particles. However, pure oils are equally highly unsuitable due to their cos

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Pagire, Sudhir K., Sachin A. Korde, Benjamin R. Whiteside, John Kendrick, and Anant R. Paradkar. "Spherical Crystallization of Carbamazepine/Saccharin Co-Crystals: Selective Agglomeration and Purification through Surface Interactions." 2013. http://hdl.handle.net/10454/9705.

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No<br>Spherical crystallization involves crystallization and simultaneous agglomeration of a crystalline particle using an immiscible phase, which has preferential affinity for the crystal surface. Here, we report application of a spherical crystallization technique to the field of co-crystallization. Carbamazepine/saccharin (CBZ/SAC) co-crystals were generated using reverse antisolvent addition and agglomerated using different bridging liquids. Two crystal forms of CBZ/SAC co-crystals were formed, depending on the levels of supersaturation achieved during processing. The selective agglomerati

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Books on the topic "Selective agglomeration"

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Allen, Raymond William. An investigation into the selective agglomeration of fine coal from liquid suspension. University of Birmingham, 1988.

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Behrens, Kristian. Survival of the fittest in cities: Agglomeration, selection, and polarisation. Centre for Economic Performance, London School of Economics and Political Science, 2008.

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Baldwin, Richard E. Heterogeneous firms, agglomeration and economic geography: Spatial selection and sorting. National Bureau of Economic Research, 2005.

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Poets, Desirée, and Max O. Stephenson Jr. Maré from the Inside: Art, Culture and Politics in Rio de Janeiro, Brazil. Edited by Nicholas Barnes. Virginia Tech Publishing, 2021. http://dx.doi.org/10.21061/mare.

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Complexo da Maré is a group of 16 contiguous favelas and housing projects in the northern zone of Rio de Janeiro. Home to an estimated 140,000 individuals, Maré is Brazil's largest agglomeration of favelas. Often depicted in a negative light, these favelas are in fact vibrant and diverse communities, as revealed in this remarkable book. Maré from the Inside: Art, Culture and Politics in Rio de Janeiro, BrazilMaré Brazil is a companion to the exhibition of the same name (Portuguese: Maré de DentroDentro), which was developed by an international team of Brazilian and US academics, activists and

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Szewczyk, Janusz. Rola zaburzeń w kształtowaniu struktury i dynamiki naturalnych lasów bukowo-jodłowo-świerkowych w Karpatach Zachodnich. Publishing House of the University of Agriculture in Krakow, 2018. http://dx.doi.org/10.15576/978-83-66602-35-9.

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The aim of the study was to determine the influence of different disturbances (both natural and anthropogenic) on species composition and stand structure of old-growth mixed mountain forests in the Western Carpathians. These stands are usually dominated by beech, fir and spruce, mixed in different proportions. The tree main species represent different growth strategies, and they compete against each other. The longevity of trees makes the factors influencing the stand structure difficult to identify, even during longitudinal studies conducted on permanent research plots. That is why dendroecol

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Book chapters on the topic "Selective agglomeration"

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Fang, Chuanglin, and Danlin Yu. "The Spatial Pattern of Selecting and Developing China’s Urban Agglomerations." In Springer Geography. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1551-4_2.

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Teliura, Natalia, Nataliia Tsapko, Hanna Khabarova, Olga Lomakina, Olena Pshenichnova, and Tetiana Klochko. "Selection Methodology of Ecological Safety Priorities of Sustainable Development Goals of Urban Agglomerations." In Integrated Computer Technologies in Mechanical Engineering - 2021. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94259-5_73.

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Abdalla, Hassan I. "A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_64.

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AbstractIn this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering using Euclidean distance. It is noted that performance of clustering algorithm is highly dependent on the similarity measure. Moreover, as the number of clusters gets reasonably increased, the clustering algorithms’ performance gets higher.

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Kawatra, S. Komar. "Selective Agglomeration." In Advanced Coal Preparation and Beyond. CRC Press, 2020. http://dx.doi.org/10.1201/9780429288326-10.

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"SELECTIVE OIL AGGLOMERATION IN FINE COAL BENEFICIATION." In Physical Cleaning of Coal. CRC Press, 2018. http://dx.doi.org/10.1201/9781351075589-14.

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Vega, J. M. G., M. R. Martinez-Tarazona, and A. B. Garcia. "Selective agglomeration of high rank coals with vegetable oils." In Coal Science, Proceedings of the Eighth International Conference on Coal Science. Elsevier, 1995. http://dx.doi.org/10.1016/s0167-9449(06)80108-4.

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Burke, F. P., R. A. Winschel, and G. A. Robbins. "COAL CLEANING FOR LIQUEFACTION BY SELECTIVE AGGLOMERATION WITH RECYCLE OILS." In 1991 International Conference on Coal Science Proceedings. Elsevier, 1991. http://dx.doi.org/10.1016/b978-0-7506-0387-4.50229-1.

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Zhang, Z. C., and B. C. Beard. "Agglomeration of Pt particles and potential commercial application for selective hydrodechlorination of CCl4." In Studies in Surface Science and Catalysis. Elsevier, 2000. http://dx.doi.org/10.1016/s0167-2991(00)81044-7.

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Biletskyi, V. S., B. N. Samilye, and A. Kheloufi. "Oil agglomeration in new combined technologies." In Mine Planning and Equipment Selection 1997. CRC Press, 2020. http://dx.doi.org/10.1201/9781003078166-145.

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Chauhan, Ritu, and Harleen Kaur. "Predictive Analytics and Data Mining." In Advances in Secure Computing, Internet Services, and Applications. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4940-8.ch004.

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High dimensional databases are proving to be a major concern among the researches to extract relevant information for futuristic decision making. Real world data is high dimensional in nature and comprises of irrelevant features, missing values, and redundancy, which requires serious concerns. Utilizing all such features can mislead the results for emergent prediction. Therefore, such databases are critical in nature to determine optimal solutions. To deal with such issues, the authors have developed and implemented a Cluster Analysis Study Behavior of School Children from Large Databases (CABS) framework to retrieve effective and efficient clusters from high dimensional human behavior datasets for school children in US. They have applied feature selection technique and hierarchical agglomerative clustering technique to discover clusters of vivid shape and size to retrieve knowledge from large databases. This study was conducted for Health Behavior in School-Aged Children (HBSC) using Correlation-Based Feature Selection (CFS) technique to reduce the inconsistent data records and select relevant features that will eventually extract the appropriate data to merge similar data and retrieve clusters. However, predictive analytics can facilitate a more thorough extraction of knowledge to facilitate better quality and faster decisions. The authors have implemented the current framework in R language where the clustering was emphasized using pvclust package. The proposed framework is highly efficient in discovering hidden and implicit knowledge from large databases due to its accessibility to handling and discovering clusters of variant shapes.

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Conference papers on the topic "Selective agglomeration"

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Tului, M., F. Arezzo, and L. Pawlowski. "Optical Properties of Plasma Sprayed Semiconducting Oxides." In ITSC2004, edited by Basil R. Marple and Christian Moreau. ASM International, 2004. http://dx.doi.org/10.31399/asm.cp.itsc2004p0195.

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Abstract Optical selective surfaces, i.e., surfaces with optical properties varying according to the frequency of the impinging radiation, have been exploited in several technical fields. These surfaces consist generally of doped semiconducting films, such as mixed oxides of Indium and Tin as well as Aluminium and Zinc. Thay are currently obtained by physical vapour deposition or sol-gel techniques. The present work aimed at demonstrating that coatings retaining optical selectivity can be obtained also by plasma spraying. Powders of Indium Tin Oxide (ITO) were prepared by an agglomeration tech

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Roy, Nilabh, Anil Yuksel, and Michael Cullinan. "Design and Modeling of a Microscale Selective Laser Sintering System." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8569.

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The development of micro and nanoscale additive manufacturing methods in metals and ceramics is important for many applications in the aerospace, medical device, and electronics industries. Unfortunately, most commercially available metal additive manufacturing tools have feature-size resolutions of greater than 100 μm, which is too large to precisely control the microstructure of the parts they produce. A few research-grade metal additive manufacturing tools do exist, but their build rate is generally too slow for commercial applications. Therefore, this paper presents a new microscale select

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Hu, Yingbin, Hui Wang, and Weilong Cong. "Laser Deposition-Additive Manufacturing of Graphene Oxide Reinforced IN718 Alloys: Effects on Surface Quality, Microstructure, and Mechanical Properties." In ASME 2019 14th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/msec2019-2985.

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Abstract Owing to its high stiffness and strength, low density, and excellent flexibility, nano-sized graphene oxide (GO) is considered as a competitive material to reinforce metallic materials. Conventional manufacturing methods for GO reinforced metal matrix fabrication include casting and powder metallurgy, both of which demonstrate disadvantages of high reinforcement agglomeration, high cost, and difficulty in fabrication of complex structures. To reduce these problems, it is important to investigate a finely-controlled, cost-saving, and near-net-shaping process for GO reinforced metal mat

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Wang, Yachao, Jing Shi, Xinnan Wang, and Yun Wang. "In Situ AFM Characterization of Deformation Behavior of Nano TiC Reinforced Inconel 718 Superalloy Prepared by Selective Laser Melting." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52426.

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Laser assisted additive manufacturing (LAAM) is regarded as a complementary method to traditional manufacturing processes and it has drawn significant attention from both industry and academia. Improving the performance of components fabricated by LAAM has been an important research task in recent years due to the inevitable negative effects introduced by in the LAAM process, such as porosity, inhomogeneity, and tensile residual stress. In this study, to obtain high-performance metal components by LAAM, nano-TiC particles are adopted to reinforce Inconel 718 and the mixed powders of TiC and In

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Jalan, Ankit, and Purushottam Kar. "Accelerating Extreme Classification via Adaptive Feature Agglomeration." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/361.

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Extreme classification seeks to assign each data point, the most relevant labels from a universe of a million or more labels. This task is faced with the dual challenge of high precision and scalability, with millisecond level prediction times being a benchmark. We propose DEFRAG, an adaptive feature agglomeration technique to accelerate extreme classification algorithms. Despite past works on feature clustering and selection, DEFRAG distinguishes itself in being able to scale to millions of features, and is especially beneficial when feature sets are sparse, which is typical of recommendation

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Zuo, Shuhui, and Fang Sun. "The Impact of Industrial Agglomeration on the FDI Location Selection." In First International Conference Economic and Business Management 2016. Atlantis Press, 2016. http://dx.doi.org/10.2991/febm-16.2016.61.

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Strofylas, Giorgos A., Georgios N. Lygidakis, and Ioannis K. Nikolos. "Accelerating RBF-Based Mesh Deformation by Implementing an Agglomeration Strategy." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50902.

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During the past decade Radial Basis Functions-based mesh deformation techniques have been emerged as an indispensable tool of numerical simulations entailing grid transformation. Despite their important contribution to such problems, they suffer from a significant drawback; they call for relatively excessive memory and computation time requirements. A remedy to this deficiency seems to be the selection of a reduced set of surface nodes, to be used as RBF’s centers; an equations’ system with decreased dimensions is obtained in that way. In this paper a new methodology for such a reduced surface

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ZHANG, Yajie, Yaqiong LV, Lei TU, and Yueqiu HOU. "Intelligent Logistics Supplier Selection Based On Improved Agglomerative Hierarchical Clustering Algorithm." In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). IEEE, 2019. http://dx.doi.org/10.1109/indin41052.2019.8972146.

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Magri, Luca, Filippo Leveni, and Giacomo Boracchi. "MultiLink: Multi-class Structure Recovery via Agglomerative Clustering and Model Selection." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.00189.

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Stu¨bing, S., M. Dietzel, and M. Sommerfeld. "Modelling Agglomeration and the Fluid Dynamic Behaviour of Agglomerates." In ASME-JSME-KSME 2011 Joint Fluids Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajk2011-12025.

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For modeling agglomeration processes in the frame of the Lagrangian approach, where the particles are treated as point masses, an extended structure model was developed. This model provides not only information on the number of primary particles in the agglomerate, but also on the geometrical distension of the agglomerates. These are for example the interception diameter, the radius of gyration, the fractal dimension and the porosity of the agglomerate using the convex hull. The question however arises now, which is the proper agglomerate cross-section for the calculation of the drag force. In

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Reports on the topic "Selective agglomeration"

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Wheelock, T. D., J. Drzymala, R. W. Allen, et al. Mechanisms for selective agglomeration of coals. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6472396.

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Wheelock, T. D., J. Drzymala, R. W. Allen, et al. Mechanisms for selective agglomeration of coals. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6502136.

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Wheelock, T., J. Drzymala, R. Allen, et al. Mechanisms for selective agglomeration of coals. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/6679420.

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Wheelock, T. D., J. Drzymala, R. W. Allen, et al. Basic principles and mechanisms of selective oil agglomeration. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6410567.

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Wheelock, T. D., J. Drzymala, R. W. Allen, Y. C. Hu, D. Tyson, and Qiu, Lessa, A. Ziaoping. Basic principles and mechanisms of selective oil agglomeration. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6286763.

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Author, Not Given. Engineering development of selective agglomeration: Task 5, Bench- scale process testing. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/5805503.

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Good, R. J., and M. N. Badgujar. The use of starch to enhance sulfur and ash removal from coal by selective oil agglomeration. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/5064647.

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Wheelock, T. D. Basic principles and mechanisms of selective oil agglomeration. Fossil energy interim report, October 1, 1983--September 30, 1992. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10161801.

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Moro, N. `. Jha, M. C. Engineering Development of Advanced Physical Fine Coal Cleaning for Premium Fuel Applications: Task 9 - Selective agglomeration Module Testing and Evaluation. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/643510.

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Moro, N., and M. C. Jha. Engineering development of advanced physical fine coal cleaning for premium fuel applications. Task 6 -- Selective agglomeration laboratory research and engineering development for premium fuels. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/656669.

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