Exploration of mill power modelled as function of load behaviour

Nierop, M.A. van; Moys, M.H.

doi:10.1016/s0892-6875(01)00143-1

Cited by 13 publications

(4 citation statements)

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“…Compensation for mixer loading deviations: Various power models for grinding mills have been used to prepare powders in metallurgy, mining, cement or other industries and are now proposed to study the fundamental effect of load behavior on mill power (van Nierop and Moys, 2001;Bazin et al, 2005;Dong and Moys, 2003). This development has practical implications for the operations and control of grinding mills, given that load parameters may drastically alter the optimal set-point typically chosen to draw maximum grinding mill power (van Nierop and Moys, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Characterization of the granular-to-fluid state process during mixing by power evolution in a planetary concrete mixer

Cazacliu

Legrand

2008

Chemical Engineering Science

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Section: Introductionmentioning

confidence: 99%

“…This development has practical implications for the operations and control of grinding mills, given that load parameters may drastically alter the optimal set-point typically chosen to draw maximum grinding mill power (van Nierop and Moys, 2001).…”

Section: Introductionmentioning

confidence: 99%

Characterization of the granular-to-fluid state process during mixing by power evolution in a planetary concrete mixer

Cazacliu

Legrand

2008

Chemical Engineering Science

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“…Morrel [62], on the other hand, relates the feed mineral, mill geometry, and operating conditions with the specific energy of the circuit. In the same M2M line, it has that the feed size distribution directly affecting the SAG mill load, which directly affects the power of the mill, and in turn impacts its efficiency [63].…”

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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“…In recent years, with collecting of the high sensitivity and strong anti-interference signals, the feature extractive methods of the ball mill load based on external response signal have increasingly attracted much focus from both academic and industrial fields [2]. Synergy Interval Partial Least-Squares Regression (SiPLS) [3] is an effective method to select the feature of external response signal [4].…”

Section: Introductionmentioning

confidence: 99%

Experimental Platform for Feature Selection of Signal of Ball Mill

Feng

Zhao

Zhang

2012

AMM

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Ball mill load monitoring and rational parameters setting are important to ensure the ball mill long-term stable operation. Although vibration and acoustic signal of shell contain plenty of information about mill load, it is difficult to select the feature of them in time domain. Due to the high dimensionality and colinearity, models based on frequency spectrum are complex and with a low generalization and the irrelevant spectral variables deteriorate their quality. This paper use Synergy Interval Partial Least-Squares Regression(SiPLS) to select the feature frequency bands of vibration and acoustical, which are directly relevant to the parameters of ball mill load, and build effective prediction models. The experimental platform combines the strengths of MATLAB with the benefits of C#.net to implement the functions of frequency feature selection, feature modeling and load parameters prediction. Test results show that the platform selects the frequency spectrum feature effectively, and the generalization of mill load models are improved.

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Exploration of mill power modelled as function of load behaviour

Cited by 13 publications

References 1 publication

Characterization of the granular-to-fluid state process during mixing by power evolution in a planetary concrete mixer

Characterization of the granular-to-fluid state process during mixing by power evolution in a planetary concrete mixer

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

Experimental Platform for Feature Selection of Signal of Ball Mill

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