Simulation of the comminution process in a high-speed rotor mill based on the feed’s macroscopic material data

Marijnissen, Michał J.; Graczykowski, Cezary; Rojek, Jerzy

doi:10.1016/j.mineng.2020.106746

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…Asghari et al [69] did not generate an integrated model of the production dynamics of an SAG mill, but they studied the effects of ore characteristics and operating parameters on mill performance using mathematical models, such as t 10 or the Bond work index, in addition to the particle shape of the mill product to monitor breakage events as a function of ore strength. Lvov et al [70] and Marijnissen et al [71] developed a model of a wet SAG mill based on the discrete element method and computational fluid dynamics (CFD), which allows for determining the energy grossness ratios of the grinding process (SAG) under certain conditions [70], or to obtain the velocities and collision angles of a representative group of particles in the mill [71]. Focused on optimizing SAG grinding performance, Behnamfard et al [72] applied the properties of feed ore, including hardness and particle size distribution, to improve the operation of an AG mill, developing a model for the prediction of mill energy consumption, a variant of the MINNOVEX model [73].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

et al. 2023

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Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process’s productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale.

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Section: Introductionmentioning

confidence: 99%

Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

et al. 2023

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“…In addition, to some areas, the scale of logistics enterprises is small, and they often need to stay in the distribution center for a short time before distribution, which leads to an extended delivery time. Most express companies have not developed a set of perfect reward and punishment mechanisms in the process of terminal distribution, which leads to problems of lax work attitudes among their staff, product loss, damage, and delayed delivery, resulting in low customer satisfaction [5][6].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Logistics Frequent Path Data Mining Based on Statistical Density

Hou

2023

IJACSA

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Sharp increases and rapid development followed the effects of a novel coronavirus outbreak on online sales and the real economy. The e-commerce mode on the Internet has attracted much attention, and users' purchases on the Internet has never been done before. However, among the many express companies, as the ones closest to consumers, they can still provide high-quality products in the face of huge market demand. Urban terminal logistics refers to the purpose of express services to meet the needs of terminal customers under the requirements of logistics centralization and customer diversification. However, the geographical distribution of logistics services in China is comprehensive, and customers' requirements are also complex. Practical problems in logistics enterprises in China significantly restrict the quality of logistics services. The final kilometer of distribution is composed of many links, and it is a very cumbersome enlace; it contains the determination of distribution scope, loading goods, arrangement of distribution sequence, arrangement of vehicles or personnel scheduling, and planning of distribution routes. A Genetic Algorithm (GA) with local search method fusion is proposed for fast logistics data modeling and mining simulation analysis. Practical examples and literature data prove the method's accuracy.

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A simulation of the comminution process of homogenized lithium-ion battery models

et al. 2022

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Simulation of the comminution process in a high-speed rotor mill based on the feed’s macroscopic material data

Cited by 3 publications

References 36 publications

Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry

Simulation of Logistics Frequent Path Data Mining Based on Statistical Density

A simulation of the comminution process of homogenized lithium-ion battery models

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