Inferential measurement of sag mill parameters IV: Inferential model validation

Apelt, T.A.; Thornhill, Nina F.

doi:10.1016/j.mineng.2009.01.001

Cited by 10 publications

(6 citation statements)

References 9 publications

(15 reference statements)

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“…In general, the number of real-time measurements available is significantly lower than the size of the state vector that needs to be measured [43]. In [4]- [6], the problem of inferential modeling, state estimation, and model validation of an SAG mill is addressed using the extended Kalman filter. However, the results are obtained based on a set of measurements that are not physically measurable in the process.…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

Cedeño,

Coronel,

Orellana

et al. 2024

IEEE Access

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In this paper, we have proposed a new paradigm for modeling of SAG mills. Typically, important parameters found in the modeling of such processes are described as state-space system model rather than unknown parameters. Here, we propose to estimate the system model using the maximum likelihood approach. Additionally, we propose using a new measurement that has not been considered in other modeling approaches. The benefits of our proposal are illustrated via numerical simulations. The results demonstrate that incorporating this new measurement within the framework of maximum likelihood estimation improves the accuracy of estimating the unknown parameters.

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Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

Cedeño,

Coronel,

Orellana

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Checking the above, in the literature review, it is possible to find a lots of applications of ML to the study of mineral processing dynamics, highlighting the following applications to the grinding phase: analysis of the mechanisms and measurement methods for the load of a mill based on mechanical vibrations and acoustic signals [80]; the impact of blast fragmentation control on increased mill production [81]; the development of a dynamic model of operation of an SAG mill using equations based on the conventional non-stationary population balance approach [82]; the identification of the best operating conditions with which to identify the cut size of optimal grinding to reduce metal losses in flotation circuits using a gradient recovery model [83]; case studies of grinding circuit modeling using time series analysis or the adjustment of vector machine algorithms of support [10] in order to analyze descriptor variables, such as power or temperature; predicting breakage and the evolution of rock size and shape distributions in AG/SAG mills [84]; models of power and specific energy consumption based on the distribution of the size of the mineral feed [64]; inferential measurement of SAG mill parameters [85][86][87][88][89]; multicomponent phenomenological modeling, which represents the performance of an SAG mill as a function of the distribution and components of the mineral feed [21]; and modeling of energy consumption prediction [90,91], among others.…”

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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“…After ore is mined, it first needs to be crushed and ground to suitable particle size. Semi-autogenous grinding (SAG) mills are widely used, large-scale grinding machines that generate a grinding effect through the impact and abrasion of a small amount of grinding medium and ore clasts of different sizes [1]. This process can complete the traditional secondary crushing, fine crushing, and coarse grinding processes, thereby simplifying the crushing and grinding process and equipment required [2].…”

Section: Introductionmentioning

confidence: 99%

Differences in Properties between Pebbles and Raw Ore from a SAG Mill at a Zinc, Tin-Bearing Mine

Sun

Yang

et al. 2022

Minerals

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Semi-autogenous (SAG) mills are widely used grinding equipment, but some ore with critical particle sizes cannot be effectively processed by SAG mills and turned into pebbles. This research aims to analyze and compare the properties of raw ore and pebbles from a zinc- and tin-bearing ore. The results show that the contents of sphalerite, cassiterite, biotite, antigorite, pyroxferroite, ferroactinolite, and ilvaite in the raw ore are higher than those in the pebbles, and that the pebbles have higher contents of hedenbergite, chlorite, epidote, actinolite, etc. Meanwhile, the abrasion and impact resistance of pebbles is greater than that of the raw ore. In addition, the sphalerite is evenly embedded, and the grinding process is regular. Fine cassiterite associated with harder minerals is difficult to dissociate; it is often found in softer or brittle minerals which may be easily ground into ore mud. The cassiterite in the pebbles is associated with hard and brittle hedenbergite and soft chlorite, making it difficult to recover. This research provides a good foundation for evaluating the recovery value of pebbles and improving the productivity of the SAG process.

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Inferential measurement of sag mill parameters IV: Inferential model validation

Cited by 10 publications

References 9 publications

Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

Maximum Likelihood Estimation for an SAG Mill Model Utilizing Physical Available Measurements

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

Differences in Properties between Pebbles and Raw Ore from a SAG Mill at a Zinc, Tin-Bearing Mine

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