Modelling the power draw of tumbling mills: A comprehensive review

Golpayegani, Mohammad Hasan; Rezai, Bahram

doi:10.37190/ppmp/151600

Cited by 6 publications

(9 citation statements)

References 52 publications

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“…Stephen Morell invented a power draw model in 1992. The mathematical model is highly recommendable for ball milling innovations that operate via the semi-autogenous grinding methodology, therefore, accurately assessing the comminution performance at various mill charge capacity as depicted by Figure 3 [7], [9], [23], [26], [27], [28]. Mill power calculations can be followed through some knowledge on mechanical energy interaction of components during grinding activities and the integration of the lever arm model of Figure 3a, the Stephen Morrell power model was developed [1].…”

Section: Stephen Morrell Power Draw Model Prediction During Ball Mill...mentioning

confidence: 99%

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Gaesenngwe,

RAGHUPATRUNI,

MAMVURA

et al. 2024

Preprint

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The application of Semi Autogenous Grinding strategy in mineral beneficiation engineering is well defined and established unit operation that is qualified for liberation of valuable final products from various rock ores of various kinds. The TENCAN GQM series Ball Mill equipment was deployed throughout our investigation to learn on optimum setting factors that are crucial in achieving effective product quality outcome during coal beneficiation. Therefore, a Semi-autogenous grinding methodology was deployed for grinding five (5) different coal samples into a product stream ranging in particle size from below [−600μm, +38μm] through an optimized procedure that syndicate three(3) control parameters being the {A − powder filling (fc), B − ball loading (JB) and C − residence time or grinding duration (t)}. Moreover, to analyse the grinding kinetics during interaction to produce coal mass recovery at desirable particle size specification relevant for specific manufacturing industry. However, initially the coal sample material was acquired and characterized via three spectroscopy techniques {x – ray diffraction (XRD), x – ray fluorescence (XRF), scanning electron microscopy (SEM)}, to establish contrast of each sample material in composition, morphology, hardness, and the relative abundance of occurring species entrenched within the coal structure (inorganic matter). Hence using the Proximate testing, Ultimate testing, and petrography analysis the examination was possible for each sample type collected. The coal samples that were initially collected were assorted in particle sizes classification and were grouped as run-of-mine coals (−200mm), Cobbles (−75mm, +40mm), Nuts (−40mm, +25mm), Peas (−32mm, +14mm) and fines (−14mm) according to our laboratory sieve specifications. However, the acquired coal species were initially subjugated to a hardness testing using the Hardgrove grindability Index device that reported a variable coal grindability index value with material hardness increased from Nuts (60.37), Cobbles (64.58), ROM (66.84), Fines (66.99) to Peas (67.37) which are relatively soft compared to other coal samples. Process engineers, researchers and students in different organizations studying metallurgy and coal beneficiation etc., must be informed about factors affecting coal material preparation innovations and the health-safety risk encounter by the mineral engineer personal during handling and processing. Moreover, production efficacy is significantly improved through adopting optimized model functions that accurately increase the product recovery at lower power rating for the cumulative production path hence relevantly reducing the cost(s) of exacerbated by tedious and unnecessary methods.

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Section: Stephen Morrell Power Draw Model Prediction During Ball Mill...mentioning

confidence: 99%

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Gaesenngwe,

RAGHUPATRUNI,

MAMVURA

et al. 2024

Preprint

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“…To increase mills' efficinecy, equimpment designers have attempted to improve all ball mill components, including bearigns, gear drivers, lubrication systems, and the electric motor. However, one of the main significant problems leading to the low energy efficinecy of mills is that about 30% of grinding bodies remain in a dead zone and are not involved in the dynamic processes (Goralczyk et al, 2020;Golpayegani and Rezai, 2022). Accordingly, new technologies such as the Vertimill (Metso) and the horizontal Isamill (Glencore Technology) have been introduced, which use gravitation forces or rotating discs to maximaze the live area of the mill (Mogroup, 2022;Glencoretechnology;2022).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, to prevent the transmission of grinding media in the dead zone, researchers have focused on using electromagnets embedded in the mill shells (Wołosiewicz-Głab et al, 2016;Wołosiewicz-Głab et al, 2017;Krawczykowski et al, 2018;Foszcz et al, 2019). However, traditionall designed ball mills are still the mian part of mineral processing plants in the world (Golpayegani and Rezai, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, provided models can be classified under two generalized categories: empirical and fundamental. Moreover, the fundamental models can be classified under four subsets: energy balance, friction approach, torque-arm, and discrete element method (DEM) (Morrell, 2019;Tavares, 2017;Golpayegani and Rezai, 2022). Davis (1919) established the first relationship between charge motion in a ball mill and its power draw using White's description of the charge motion (White, 1905).…”

Section: Introductionmentioning

confidence: 99%

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Improvement of the accuracy of Hogg and Fuerstenau's model in predicting the power draw of ball mills based on determining the grinding media’s dynamic voidage

Golpayegani¹,

Rezai²

2022

Physicochem. Probl. Miner. Process.

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In the development of tumbling mills' power models, the voidage of grinding media is assumed to be static and equal to 40%. While the grinding media's voidage is dynamic; and hence is varied by changing the operating parameters. In this paper, to improve the Hogg and Fuerstenau model's accuracy in predicting the ball mills' power draw, the grinding media's static and dynamic voidage was studied for Bond's proposed ball size distributions (BSD) for the ball mills' first filling. To this end, by scaling down balls to one-tenth of actual size, developing a novel method to measure the dynamic voidage, and employing the three-level factorial method, a separate empirical model was developed for determining the dynamic voidage of each Bond's BSD with respect to mill's fractional filling and rotating speed. Moreover, using the multiple regression method, a general empirical model was derived to determine the dynamic voidage of each supposed BSD based on calculating the mean absolute deviation of balls diameter (MAD). Results indicated that grinding media's dynamic voidage increases with an increase in rotating speed and a decrease in fractional filling and balls diameter's MAD. The maximum and minimum static and dynamic voidage occurred for the seventh and first Bond's BSDs. By employing an industrial database and analyzing the mean absolute percentage error (MAPE) of predicted ball mills' power draw, it was found that the Hogg and Fuerstenau model's accuracy enhances by calculating the load's bulk density based on the grinding media's dynamic voidage.

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“…Accordingly, the developed models can be classified under two basic categories: empirical and fundamental. Besides, the fundamental models are classified under four subsets: energy balance, friction method, torque-arm approach, and discrete element method (Tavares 2017; Morrell 2019; Golpayegani and Rezai 2022). Davis (1919) established the first relationship between charge motion in a ball mill and its power draw using White's description of the charge motion (White 1905).…”

Section: Introductionmentioning

confidence: 99%

Improving the Morrell C-model's accuracy in predicting the ball mills’ power draw based on calculating the dynamic voidage of grinding media

Golpayegani

Rezai

2022

Mineral Processing and Extractive Metallurgy

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This paper investigates grinding media's dynamic voidage to improve the Morrell-C model's accuracy in predicting the ball mills' power draw. Using a three-level factorial design, we provided an empirical model to determine the voidage of each ball size distribution proposed by Bond for ball mills' first filling in various ranges of fractional mill filling and mill rotating speed. Moreover, by employing the multiple regression method, a general model was developed to predict the balls' dynamic voidage by calculating the mean absolute deviation (MAD) of the balls' diameter. Results revealed that the balls' dynamic voidage increases with increasing the rotating speed and decreasing the fractional filling and MAD of the balls' diameter. The evaluation of the grinding media's voidage prediction models' performance in improving the Morrell-C model's accuracy indicated an increase in the model's predictive power by calculating the ball mills' load bulk density based on the dynamic voidage of grinding media.

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Modelling the power draw of tumbling mills: A comprehensive review

Cited by 6 publications

References 52 publications

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Improvement of the accuracy of Hogg and Fuerstenau's model in predicting the power draw of ball mills based on determining the grinding media’s dynamic voidage

Improving the Morrell C-model's accuracy in predicting the ball mills’ power draw based on calculating the dynamic voidage of grinding media

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