Among all comminution steps, grinding in tumbling mills is known to be energy inefficient. The most commonly used tumbling mill in the industry is the ball mill

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EFFECT OF OPERACIO NAL VARIABLES ON BALL MILLING EFFECT OF OPERATIONAL VARIABLES ON BALL MILLING Daniel Mendon“a Francioli Projeto de Gradua“‰o apresentado ao Curso de Engenharia de Materiais da Escola Polit”cnica, Universidade Federal do Rio de Janei ro, como parte dos requisitos necess⁄rios ‹ obten“‰o do t™tulo de Engenheiro de Materiais. Orientador: Prof. Lu™s Marcelo Marques Tavares Coorientador: Prof. Rodrigo Magalh‰es de Carvalho Rio de Janeiro Fevereiro de 2015

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iii Francioli, Daniel Mendon“a Effect of operational variables on ball milling/ Daniel Mendon“a Francioli. Ð Rio de Janeiro: UFRJ/ Escola Polit”cnica, 2015. XVIII, 72, p.: il.; 29,7 cm. Orientador: Lu™s Marcelo Marques Tavares Coorientador: Rodrigo Magalh‰es de Carvalho Projet o de Gradua“‰o Ð UFRJ/ Escola Polit”cnica/ Curso de Engenharia de Materiais, 2015. Refer’ncias Bibliogr⁄ficas: p. 68 -71. 1. Comminution. 2. Energy efficiency. 3. Ball milling. I. Tavares, Lu™s Marcelo Marques e Carvalho, Rodrigo Magalh‰es de. II. Universi dade Federal do Rio de Janeiro, Escola Polit”cnica, Curso de Engenharia de Materiais. III. Effect of operational variables on ball milling.

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vi Acknowledgments I would like to thank the follow ing persons for helping me during my undergraduate degree. ¥ My family, Marco, Rachel, Andr”, Brenno, Ignez and Nylson, for their encouragement, advice and friendship throughout all my life. ¥ My girlfriend Rita, for her support and patience during these last months and also for her loving care. ¥ My group of friends, Friends Metalmat , for affording great unforgettable laughs along our undergraduate study years. ¥ My advisors, Professors Luis Marcelo Tavares and Rodrigo Carvalho, for their constant support, advice and suggestions. I am extremely grateful to consider them not only as advisors but also as great friends. ¥ LTM undergraduate and postgraduate students, for their support and incredible knowledge exchange. ¥ LTM staff, for their crucial support during experim ental work. ¥ Professor Malcolm Powell and Research Fellow Dr. Mohsen Yahyaei, from JKMRC/UQ, for their enduring advice and invaluable encouragement. ¥ Pedra Sul Minera“‰o Ltda, for providing the samples for the experimental work. ¥ Funda“‰o Coppetec and Thysse nKrupp Steel Europe , for the financial support. ¥ CNPq (Brazilian Research Agency), for providing financial support during the Science without Borders Program. ¥ And every other person without whom this project would not be possible.

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viii moinho . Ainda assim , para que os resultados simulados atinjam total confiabilidade ainda ” necess⁄rio um profundo entendimento sobre qual ” a real contribui“‰o de finos de min”rio tanto no movime nto da carga quanto na pot’ncia. O modelo mecanicista da UFRJ mostrou excelente concord›ncia com dados experimenta is relacionados ‹ quebra de part™culas grossas de min”rio quando corpos moedores de 40 mm foram utilizados. Contudo, o prŠprio modelo ou os par›metros espec™ficos relacionados ao min”rio ainda necessitam de ajustes para que seja poss™vel fazer predi“łes da cominui“‰o de finos. Palavras -chave: cominui“‰o, efici’ncia energ”tica, moagem

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ix Abstract of Undergraduate Project presented to POLI/UFRJ as a partial fulfillment of the requirements for degree of Materials Engineer. EFFE CT OF OPERATIONAL VA RIABLES ON BALL MILLING Daniel Mendon“a Francioli February/ 2015 Advisors: Lu™s Marcelo Marques Tavares Rodrigo Magalh‰es de Carvalho Course: Materials Engineering (BEng) Ball mills have large appli cability in the mining industry. At the same tim e, ball mills are considered as low efficient equipment. Laboratory tests using tumbling mills for batch grinding have been crucial to a better understanding of the variables that affect their development. These tests, when allied to adequate analysis too ls, are able to elucidate all effects from operational variables on ball milling and also provide information for their operation optimization. The combined analyses of experimental data with computational simulations using the discrete element method (DEM ) forms a challenge basis for the validation and calibration of the mechanistic model developed at the LaboratŠrio de Tecnologia Mineral (LTM) from COPPE/UFRJ. This work consisted on experimental batch grinding tests with a 30 x 30 cm ball mill in which op erational variables were altered. The change of these parameters resulted in direct variation on the final product size as well as on the average power consumption . Therefore, it was possible to verify enhanced process efficiency for bigger grinding media and intermediate degree of both mil filling and percentage of solids. The use of DEM through the software EDEM ¨ provided an outstanding tool for analyzing charge movement inside ball mills. However, in order to achieve absolute trust in the results from th e simulations, it is still necessary a sophisticated understanding of the actual contribution of the fine ore both on the charge movement and on the power consu med during the milling process.

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x The UFRJ mechanistic model showed excellent agreement with exper imental data regarding the breakage of coarse particles when steel balls of 40 mm were used. Nonetheless, either the model itself or the specific parameters used, which are related to the ore, still needs adjustments, which aim at improving the prediction on the breakage of intermediate and fine particles. Keywords : Comminution, energy efficiency, ball milling.

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xi Table of Contents Acknowledgments . vi List of Figures .. xiii List of Tables xvii Nomenclature .. xviii 1. Introduction .. 1 2. Objective 4 3. Review of the literature .. 5 3.1. Comminution .. 5 3.2. Comminution laws 6 3.2.1. Size specific energy (SSE) as a measure of energy efficiency . 9 3.3. Particle breakage mechanisms .. 10 3.4. Grinding .. 12 3.4.1. Power in ball mills .. 14 3.5. Comminution modeling 18 3.5.1. Discrete element method (DEM) 20 3.5.2. UFR J mechanistic model overview .. 22 4. Materials and methods 25 4.1. Batch grinding .. 25 4.1.1. Measurements . 28 Different mill design .. 30 4.1.2. Size analyses .. 30 4.1.3. Experimental method 34 4.1.4. Experimental repeatability . 36 4.2. Simulation software (EDEM ¨ & LTM Ana lyst) . 37 5. Results and discussion 41 5.1. Batch grinding .. 41 5.1.1. Power .. 41 5.1.2. Particle size distribution .. 46 5.1.3. Fines generated and grindability 50 5.1.4. Effect of mill internal design .. 55 5.2. Simulation .. 56 5.2.1. Power calculated from DEM simulations 56

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