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Computational methods and strategies for geometallurgy [Elektronisk resurs]

Koch, Pierre-Henri, 1988- (författare)
Rosenkranz, Jan (preses)
Pirard, Eric (opponent)
Luleå tekniska universitet Institutionen för samhällsbyggnad och naturresurser (utgivare)
ISBN 9789177903475
Publicerad: Luleå : Luleå tekniska universitet, 2019
Serie: Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, 1402-1544 1402-1544
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  • E-bokAvhandling(Diss. (sammanfattning), 2019)
Sammanfattning Ämnesord
  • At the interface of geology and mineral processing, geometallurgy is a powerful tool for enhancingresource efficiency. A spatial model that represents the ore body in terms ofmineralogyand physical properties is combined with a process model that describes the concentrationprocess. The performance of a given ore in the process is computed in terms of gradeand recovery of the mineral of interest in the concentrate, but also the presence of potentialpenalty elements and energy costs. The inclusion of ore performance indicators in a blockmodel yields a geometallurgical model that considers the variations in an ore body.Progress has been made in recent years to list and study different processing options interms of data requirements and implementation costs. While providing useful data, littleadvance was made to guide decision-making and to handle uncertainty. The objective has,therefore, been to develop, choose and validate computational methods that suggest optimaldecisions in the scope of geometallurgical strategies for an iron ore and a porphyry copperdeposit.The selected approach is based on an analysis of structure and regularity fromthe ore blockdown to the mineral grains. By selecting the appropriate mathematical tool for each scale,the dimension of the data is reduced and the different scales are then taken into account inmaking decisions. Methods introduced for dimension reduction include machine learningmodels, statistical models and spectral descriptors. The decision models rely on stochasticmulti-armed bandits which are a form of reinforcement learning. The presentation of thedifferent models proceeds by zooming in from coarse scale to fine scale then taking a stepback and analyze the implications. Data that was collected during sampling campaigns andindustrial plant surveys is used to design and verify the proposedmodels.iWith regard to the dimension reduction problem, results showed the method’s ability toclassify mineral textures and identify mineral phases with more than 90 percent accuracy onthe selected data sets of optical images and incorporate different physical properties into ageometallurgical ore type classification. Decision results showed that strategies in the case ofa feed grade control or when different ore types were identified, resulted in a twofold increaseof a reward function which is either Boolean (the product fulfills quality requirements ornot), or continuous (an economic objective). The cumulative value of the reward functionmeasured the optimality of a processing strategy. Quantitative methods were introduced toevaluate ore classification as well as geometallurgical strategies.The achieved results suggest the introduction of these computationalmethods in the practiceof geometallurgy. The increased knowledge of different ore type performances and appropriatemodels lead to optimal decisions for improved resource efficiency along the ore valuechain. This is achieved by bothmaximizing profit and decreasing environmental impact, forexample by choosing processing routes that minimize energy consumption. 


Engineering and Technology  (hsv)
Environmental Engineering  (hsv)
Mineral and Mine Engineering  (hsv)
Teknik och teknologier  (hsv)
Naturresursteknik  (hsv)
Mineral- och gruvteknik  (hsv)
Engineering and Technology  (hsv)
Materials Engineering  (hsv)
Metallurgy and Metallic Materials  (hsv)
Teknik och teknologier  (hsv)
Materialteknik  (hsv)
Metallurgi och metalliska material  (hsv)
Mineral Processing  (ltu)
Mineralteknik  (ltu)


government publication  (marcgt)

Indexterm och SAB-rubrik

process simulation
machine learning
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