Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings

Abstract

Reprocessing of iron ore tailings (IOTs) and extracting recoverable valuable iron oxides will become increasingly financially attractive for mining companies and also may reduce environmental problems. Using databases built based on long term monitoring of units installed on plants to control the operational conditions to generate artificial intelligence models can decrease the cost of reprocessing operations Although some investigations have been focused on the reprocessing of IOTs, several challenges still remain which need to be addressed, especially for fine particles. SLon®, has developed a pulsating high gradient magnetic separator for the processing of fine iron oxides. However, there has been no systematic optimisation and variable assessments for SLon® operating variables to examine their effects on metallurgical responses (separation efficiency) on the industrial scale. This study addressed these drawbacks by linear (Pearson correlation) and non-linear (random forest) variable importance measurements (VIM) through an industrial SLon® installation.

Keywords

Iron ore tailings random forest Pearson correlation reprocessing

1 Introduction

Due to mining of lower grade iron ores, the volume of the gangue minerals associated with iron oxides has markedly increased. To achieve the required liberation degree for processing these type of ores, the rate of fine particle production has significantly increased (Xiong et al. 1998; Hearn and Dobbins 2000; Svoboda 2001; Dobbins et al. 2007, 2009; Liu et al. 2013; Ren et al. 2015; Makhula et al. 2016; Tohry et al. 2017; Li et al. 2018). Processing these large volumes of low-grade iron ores has led to the production of huge amounts of waste materials. Iron ore tailings (IOTs) the most common solid waste in the world (Tang et al. 2019). These IOTs contain a massive amount of fine iron oxide particles; therefore, there is a considerable value in their reprocessing. For successfully reprocessing IOTs by conventional methods, several treatment steps need to be implemented. Although some studies have been undertaken to examine different methods for reprocessing of fine iron oxide particles from IOTs, several gaps in the understanding of process mechanisms still remain (Yi et al. 2009; Yakubailik et al. 2017; Panda et al. 2018).

Magnetic separation has been historically used for processing coarse iron ore particles. Over the last decade, magnetic separation has significantly improved in operability and performance. The relative magnetic field strength (MFS) for separation classifies magnetic equipment into three types: low-intensity magnetic separators (LIMS), medium intensity magnetic separators (MIMS), and wet high-intensity magnetic separators (WHIMS). Several factors need to be considered in selecting the appropriate magnetic equipment, such as particle size, degree of association of minerals in an ore (liberation degree), magnetic susceptibility of the targeted mineral and its gangue phases, as well as the capabilities of the magnetic equipment (Xiong et al. 1998; Hearn and Dobbins 2000; Svoboda 2001; Dobbins et al. 2007, 2009; Liu et al. 2013; Ren et al. 2015; Makhula et al. 2016; Li et al. 2018). LIMS is generally used for the scalping of simple ferromagnetic minerals such as magnetite. MIMS traditionally has been employed for separation of fine weak ferromagnetic minerals, and WHIMS mostly has been considered for separation of paramagnetic minerals such as hematite. Although WHIMS has several advantages, it is not suitable for the processing of fine and ultrafine particles. Moreover, another drawback through using WHIMS is that non-magnetic minerals may become entrapped among the magnetic minerals (Hearn and Dobbins 2000; Dahe 2002, 2003; Zeng and Dahe 2003; Chen et al. 2012).

In the present work, for processing these fine particles, a SLon® pulsation high gradient magnetic separator was modifed to fill these gaps by adding a vertical ring, a rod matrix (to create more usable surface area for magnetics collection) and a pulsation mechanism into the WHIMS system. Adding these mechanisms to the WHIMS machine minimises the entrapment of fine particles by giving them a loose state (Dobbins et al. 2009). However, there are few feasibility studies that have focused on the application of SLon® technology at the industrial scale for the processing of IOTs. Previous studies have been done mostly on the batch scale and freshly mined ore (Table 1). Moreover, since optimisation within the industrial equipment is difficult, typical statistical optimisation methods used for laboratory equipment can not usually be considered for such a scale. There is no study which has statistically explored the importance of SLon® operating variables (ring speed, field intensity and pulsation) for the separation efficiency on an industrial plant and then ranked them based on their effectiveness for various stages of processing (rougher and cleaner).

Table 1
Applications of SLon technology for mineral beneficiation.

Feed characteristics*	Parameters	Results	Ref
Minerals: Fe₂O₃, Fe₃O₄, and SiO₂	SLon-1500® and WHIMS-2000 are examined in industrial scale tests for tailings stream of LIMS.	SLon-1500® is better than WHIMS-2000. Concentrate grade, tail grade, and recovery improved about +3.8%, −3.2%, and +19.3% respectively.	Dahe (2003)
Fe Total: 29.1%, SiO₂: 56.1%, P: 0.040%, S: 0.08%
Feed size: < 200 µm (90%)
Minerals: ZrSiO₄, and FeTiO₃	Field intensity varied between 0.3 and 0.9 T.	The best field intensity was 0.7–0.9 T. Zircon grade increased (70–75%) and Zircon recovery decreased from 95% to 92%.	Dobbins et al. (2009)
ZrSiO₄: 59.0%, FeTiO₃: 8.0%
Feed size: <100 µm (50%)
Minerals: Kaolinite, and Fe₂O₃	Rotation speed changed between 0 and 3 r/min; Magnetic induction: 0.97 T; Vibrating amplitude 2–4 mm; Vibrating frequency 0–1450 min^–1.	An increase in the magnetic induction and rotation speed of ring reduced the mass but increased the iron removal rate of the non-magnetic product, and the increase in the vibration amplitude and frequency of ring caused an increase in the mass but a decrease in the removal rate.	Chen et al. (2012)
Fe₂O₃: 0.7%
Feed size: <150 µm (100%)
Minerals: Fe₂O₃, Fe₃O₄, and SiO₂	SLon-1500® and Slon-1750® used in the tailing stream of LIMS on industrial scale (Qi Dashan Mineral Processing Plant, China).	The SLon outperformed its competitor through higher grade and recovery, at higher capacities, all the while the matrix remained clean and did not plug. Additional testing proved a reverse flotation addition would produce an even better quality final product and enable eliminating the roasting step.	Hearn and Dobbins (2000)
Fe Total: 14.2%
Feed size: <50 µm (50%)
Minerals: Fe₂O₃, Fe₃O₄, and SiO₂	SLon-2000® used in the tailing stream of LIMS on the industrial scale (Hai Nan Iron and Steel Company, China).	Feed with a grade of 51.0% Fe yielded a concentrate of 65.0% Fe at a recovery of 88.0% Fe.
Fe Total: 50.2%
Feed size: <75 µm (95%)
Minerals: Fe₂O₃, Fe₃O₄, and SiO₂	SLon® used in the tailing stream of LIMS on the industrial scale (Mikhailovsky Mine, Russia); Magnetic intensity changed between 0.3 and 0.5 T; Pulsation changed between 220 and 250%.	The target was to upgrade the iron at a high recovery rate. The material was treated on a SLon after LIMS to remove and recover magnetite. The final concentrate of 66.0% Fe could be made but recovery was > 80.0% and the concentrate of ∼53% Fe could be cleaned by flotation.
Fe Total: 40.7%
Feed size: <120 µm (80%)
Minerals: Fe₂O₃, Fe₃O₄, FeS₂ and SiO₂	SLon-1500® used in the tailings stream of LIMS on the industrial scale (Mishan Mineral Processing Plant, China).	SLon-1500® magnetic separators have been applied to reduce phosphorus to upgrade the concentrate quality.	Dahe (2002)
Fe Total: 42.5%, SiO₂: 7.0%, P: 0.614%, and S: 0.68%
Feed size: <75 µm (73%)
Minerals: Fe₂O₃, SiO₂, and Al₂O₃	Pulp density was prepared at 20–30% Magnetic field strengths of 1000, 5500 and 10000 G were tested; Pulsation frequency changed between 6 and 25.2 Hz.	The increase in pulp density from 20% to 25% and 30% had a negative effect on the mass yield of concentrate. The magnetic intensity and pulsation mechanism had a direct effect on the mass yield. The optimum conditions for iron ore could be achieved at 20%, 2800 G and pulsation of 25.2 Hz.	Makhula et al. (2016)
Fe Total: 43.5%, SiO₂: 14.5%, and Al₂O₃: 13.7%
Feed size: <75 µm (100%)
Minerals: Fe₂O₃, Fe₃O₄, and FeO(OH).nH₂O	SLon-1750® used in the tailings stream of LIMS on the industrial scale (Qidashan Mineral Processing Plant).	SLon-1750® was better than Roast LIMS-2000®. Concentrate grade, tailings grade, and recovery improved about +3.6%, −2.6%, and +5.6%, respectively.	Zeng and Dahe (2003)
Fe Total: 15.8%
Feed Size: <75 µm (60%)
Minerals: Fe₂O₃, Fe₃O₄, FeO(OH).nH₂O, FeCO₃, FeS₂, kaolinite, and SiO₂	SLon-1500® used in the final tailing stream of old flowsheet (Gushan iron ore, gravity and magnetic plant). Parameters studied: magnetic intensity 0–1 T; pulsation frequency 0–300 per min; pulp density 10–45%.	Concentrate grade, tailings grade and recovery improved about +3.0%, −8.2%, and +13.8%, respectively.	Xiong et al. (1998)
Fe Total: 35.9%, SiO₂: 37.5%, Al₂O₃: 6.0%, P: 0.250%, and S: 0.05%
Feed size: <75 µm (87%)
Minerals: Fe₂O₃, Fe₃O₄, FeO(OH).nH₂O, FeCO₃, FeS₂, kaolinite, and SiO₂	SLon-1500® used for processing of the LIMS tailing stream (Meishan iron ore) for desulphurization and dephosphorization flowsheets).	The iron concentrate passed the metallurgical requirements: recovery> 94%; S < 0.35%; P < 0.250%; and Fe > 55%).
Fe Total: 52.8%, SiO₂: 5.7%, Al₂O₃: 0.9%, P: 0.399%, and S: 0.44%
Feed Size: <75 µm (73%)
Minerals: Fe₂O₃, Fe₃O₄, FeO(OH).nH₂O, FeCO₃, γ-Fe₂O₃, and iron bearing Silicate	SLon-2000® (magnetic intensity: 0.22 T) was used for processing of the MIMS tailing stream (Gong Changling iron ore, beneficiation flowsheets).	Concentrate grade, tailings grade and recovery improved about +7.1%, −5.4%, and +7.4%, respectively.
Fe: 46.4%
Feed size: <75 µm (60%)

^*All assays are % by weight.

This investigation employed the Pearson correlation and random forest (RF) as a machine learning tool. In industrial-scale investigations, ranking operating variables based on variable importance measurement (VIM) can be considered as an essential key for efficient operation. The VIM methods that can determine the most effective variables on processing responses have been receiving significant attention. RF can eliminate the imprecision and uncertainty of complex linear and non-linear relationships for VIM. Although RF for VIM has been used in several mining area investigations (Matin et al. 2016; Matin and Chelgani 2016; Chelgani et al. 2016a, 2016b; Shahbazi et al. 2017; Chelgani and Matin 2018; Matin et al. 2018; Nazari et al. 2019; Tohry et al. 2019; Jafari et al. 2019a, 2019b), it is not yet widely used in mineral processing. Therefore, this worked used RF for ranking variables of a SLon® unit (rougher and cleaner stage) based on their effec on the separation efficiency (SE) for reprocessing IOTs in an industrial plant.

2 Materials and Methods

2.1 Process

The circuit for tailing reprocessing from the Chadormalu plant, Yazd, Iran, was used in this study. The SLon® circuit for hematite reprocessing includes four rougher units in parallel and two cleaner units, also in parallel (Figure 1). The LGS 2000 SLon® model was installed in roughing and cleaning circuits. The machines were manufactured by the LONGI Company. Their nominal capacity is 150–180 t/h slurry. The feed of the SLon unit was provided by the final tail of the medium magnetic separation (3500 G) unit in. The d₈₀ of the input feed for the SLon flowsheet was below 30 µm and the FeO content was below 3%.

Figure 1

Schematic of the SLon circuit of the Chadormalu tailing recovery plant.

Field intensity (gauss), ring speed (%) and pulsation (%) were considered as the three main operational parameters of the unit. In order to optimise the performance of the circuit, these parameters were changed at 3 levels, and their representative separation efficiency (SE) was considered as a model response. For this purpose, 30 industrial tests were designed (Table 2). Each test was carried out over a period of 24 h. For comparison purposes, and because of the variations which may be generated by different feed grades, a uniform and large stockpile (around 100, 000 t) was prepared by extracting material from the tailings dams. In order to calculate SE, Fe grades of feed, concentrate and tailings were measured by a titration method, and then Fe recovery was calculated.

Table 2

Operating variables and their metallurgical response.

Rougher parameters			Cleaner parameters			Metallurgical response
Field intensity (Gauss)	Pulsation (%)	Ring speed (%)	Field intensity (Gauss)	Pulsation (%)	Ring speed (%)	SE* (%)	Upgrading ratio
10, 000	50	50	7000	50	50	32.66	1.53
10, 000	50	50	5000	50	50	36.71	1.69
10, 000	50	50	5000	50	50	28.14	1.64
10, 000	50	50	5000	50	50	22.99	1.70
7000	50	40	5000	50	40	14.29	1.73
9000	50	20	7000	50	40	16.48	1.66
10, 000	50	40	7000	50	60	31.50	1.67
7000	50	50	5000	50	80	21.06	1.71
7000	50	50	5000	50	60	27.30	1.68
10, 000	50	50	5000	80	50	20.22	1.87
10, 000	50	50	4500	90	50	25.84	1.85
9000	60	50	4000	85	50	14.97	1.71
9000	60	50	3500	85	75	13.16	1.69
9000	60	50	4000	75	75	19.14	1.81
9000	60	50	5000	75	75	24.57	1.80
10, 000	50	50	4500	60	50	19.80	1.68
10, 000	50	50	4500	80	50	21.60	1.76
10, 000	50	50	4500	70	50	27.37	1.71
10, 000	50	50	5000	90	50	22.33	1.67
9000	50	50	5000	90	50	22.83	1.71
9000	50	50	5000	90	85	26.51	1.76
10, 000	50	80	5000	70	50	12.05	1.72
10, 000	50	80	4800	70	50	25.06	1.62
9000	50	40	4800	70	50	18.24	1.73
10, 000	50	50	4500	70	50	16.20	1.75
7000	50	50	5000	90	60	17.99	1.71
9000	60	50	5000	75	50	20.61	1.70
9000	60	50	4500	50	50	22.02	1.70
9000	60	50	4500	90	50	18.99	1.73
9000	60	50	4500	50	40	19.69	1.63

^*SE: separation efficiency.

2.2 Variable importance measurement

2.2.1 Pearson correlation

Pearson correlation r is a statistical index for assessing linear correlations between dependent and independent variables. r ranks the inter-correlations of variables between −1 and +1. The absolute value of r shows the strength of correlation between two variables (a larger absolute value indicating stronger interdependence), while its sign indicates their relationship magnitude. In general, |r| > 0.5 shows that there is a significant linear correlation between two variables, which is calculated based on the following equation (Chelgani et al. 2016b; Matin et al. 2018; Jafari et al. 2019b).

(1)
2.2.2 Random forest

For ranking operating variables by RF, the permutation accuracy importance measurement (PAIM), which is an advanced non-linear tool for VIM, is used. The PAIM for VIM compares the differences between the prediction accuracy of a tree before and after random permutation of the target variable (Matin et al. 2016; Matin and Chelgani 2016; Chelgani et al. 2016a, 2016b; Shahbazi et al. 2017; Chelgani and Matin 2018; Matin et al. 2018; Nazari et al. 2019; Tohry et al. 2019; Jafari et al. 2019a, 2019b). The final ranking of variables is based on the computed average of differences over all trees. The empirical PAIM (mp) of the variable X_j is defined as follows

where represents a replicate of learning set ℒ (the training set (ℒ) of size N) by model in the values of X_j which have been randomly permuted, and , … , . denote the indices of the trees (M) that have been built from bootstrap replicates that do not include (x_i, y_i) (for i = 1, … , N) (Matin et al. 2016; Matin and Chelgani 2016; Chelgani et al. 2016a, 2016b; Shahbazi et al. 2017; Chelgani and Matin 2018; Matin et al. 2018; Nazari et al. 2019; Tohry et al. 2019; Jafari et al. 2019a, 2019b). High values of the PAIM show a strong non-linear relationship between the predictor and the output. Values around zero (or even negative values) indicate insignificant non-linear relationships between variables. The PAIM can evaluate the impact of each variable individually as well as in multivariate interactions with other variables on the interested target, and guarantee unbiased VIMs (Matin et al. 2016; Matin and Chelgani 2016; Chelgani et al. 2016a; Chelgani et al. 2016b; Shahbazi et al. 2017; Chelgani and Matin 2018; Matin et al. 2018; Nazari et al. 2019; Tohry et al. 2019; Jafari et al. 2019a, 2019b).

3 Results and Discussion

The results show (Table 2) that the designed flowsheet can recover and upgrade IOTs of the plant (Figure 2). For improving the metallurgical responses within the examined operating conditions, VIM assessments are conducted. Assessing linear relationships r between operating conditions and their representative SEs demonstrates that the field intensity in the cleaner stages has the highest correlation (r = 0.45) with SE (Table 3). Pearson correlation assessments show that by increasing the field intensity (both in rougher and cleaner stages), the SE is increased. In both rougher and cleaner stages, increasing the pulsation percentage has a negative effect on the SE (Table 3). Linear assessments show that the ring speed in both rougher and cleaner stage, within the considered conditions, has an insignificant effect on S.E (Table 3). Based on the linear correlations, the absolute impact (importance) of operating variables on SE for both rougher and cleaner stages has the following order: field intensity > pulsating > ring speed. However, non-linear VIM by RF (Figure 3) illustrates that the pulsation in the cleaner stage has the highest effectiveness on SE. This could be because pulsation in the cleaner stage has the highest impact on the final grade of products (Table 3). These results could be iused by the plant operators to maintain the process in its steady-state efficiently.

Figure 2

Metallurgical responses of the SLon unit.

Figure 3

Non-linear ranking of operating variables by variable importance measurement (VIM) and the random forest method (RF).

Table 3

Pearson correlation between operating variables and metallurgical responses.

	Rougher			Cleaner
Variables	Field intensity	Pulsation	Ring speed	Field intensity	Pulsation	Ring speed
S.E	0.25	–0.22	–0.014	0.45	–0.21	0.12
Upgrading ratio	–0.03	0.10	–0.030	–0.43	0.51	0.29
Recovery	0.25	–0.25	–0.001	0.54	–0.31	0.06

According to the metallurgical results, the highest SE for each operating variable potentially can be obtained when in the rougher stage; field intensity, pulsation and ring speed were 10, 000 Gauss, 50 and 50%, and in the cleaner step they were 5000 Gauss, 50 and 50%, respectively (Figure 4). Using these optimum points (rougher: field intensity 10, 000 Gauss, pulsation 50% and ring speed 50%, and cleaner: field intensity 5000 Gauss, pulsation 50%, and ring speed 50%) could achieve 36.7% SE and 1.7 upgrading ratio from IOTs.

Figure 4

Relationship between various SLon operating variables and their representative separation efficiency (SE).

Angadi et al. (2012) explored the relationship between various parameters of magnetic separation and their representative recoveries and concluded that the significance of variables has the following order: particle particle size > magnetic field > wash water rate. They reported that increasing the magnetic field leads to a higher probability of capturing magnetic particles. Makhula et al. (2016) suggested the following order of variable importance: field intensity > pulsation frequency > pulp density. Increasing the magnetic field strength generates a high force on the magnetic particles, improves their loading on the matrix, and promotes their recovery. However, this improvement leads to the entrapment of finely sized non-magnetic particles which dilutes the product grade (Makhula et al. 2016). Thus, continuously increasing the field intensity cannot improve the SE, and interactions of different factors have to be considered for an optimum separation (Dobbins et al. 2007, 2009). It was indicated that, by increasing the magnetic field intensity, the recovery was also increased while this increase has a negative effect on the product quality (Makhula et al. 2016).

Pulsation action reverses the slurry direction and decreases the entrapping and clogging of the matrix by non-magnetic particles. Thus, the capacity of the matrix can be improved, and it can be covered with an increased quantity of magnetic minerals from a certain volume of a slurry through a shorter time (Xiong et al. 1998; Hearn and Dobbins 2000; Dobbins et al. 2009). Moreover, it was documented that increasing the pulsation velocity has a positive effect on the quality of SLon products (Makhula et al. 2016); however, it can reduce the recovery (a negative relationship between pulsation and recovery after a certain level) (Xiong et al. 1998). The ring speed directly affects the retention time of the magnetic matrix, which can have a critical impact on SLon performance. Increasing the ring speed would decrease the residence time; thus, a huge volume of the slurry may be unprocessed. Moreover, a significant decrease in the speed of the ring would affect the feed rate and overall processing (Dobbins et al. 2009; Chen et al. 2012). Therefore, ranking variables and providing a balance between these variables (optimisation) can improve SLon efficiency (Makhula et al. 2016). These reported conclusions from various studies confirmed the reliability of the provided results (Table 3 and Figure 3) of this investigation.

4 Conclusions

A study of relationships between operational variables (field intensity, ring speed, and pulsation) of an industrial SLon unit (rougher and cleaner stages) and their representative separation efficiencies for reprocessing of iron ore tailings was investigated. Pearson correlation and the random forest methods, being powerful statistical tools, were used for assessing the intercorrelations and for ranking the parameters. In general, statistical assessments indicated that field intensity had a positive correlation with the process recovery while it had a negative correlation with the upgrading ratio whereas pulsation had a positive relationship with the upgrading ratio but a negative correlation with the recovery. Variable importance measurement by random forest indicated that pulsation in the cleaner stage has the highest effect on the unit efficiency. The highest separation efficiency (36.7%) was achieved when in the rougher stage (field intensity, pulsation, and ring speed were 10000 Gauss, 50% and 50%, respectively) and in the cleaner stage (field intensity 5000 Gauss, pulsation 50% and ring speed 50%).

Footnotes

Acknowledgment

The first author would like to thank the Rahbar Farayand Arya Co (RFACo) for technically supporting this work.

Disclosure statement

No potential conflict of interest was reported by the authors.

References

Angadi

, Jeon

H-S

, Mohanthy

, Prakash

, Das

2012. Analysis of wet high-intensity magnetic separation of low-grade Indian iron ore using statistical technique. Sep Sci Technol. 47(8):1129–1138. doi: 10.1080/01496395.2011.644020

Chelgani

, Matin

SS.

2018. Study the relationship between coal properties with Gieseler plasticity parameters by random forest. Int J Oil, Gas Coal Technol. 17(1):113–127. doi: 10.1504/IJOGCT.2018.089345

Chelgani

, Matin

, Hower

JC.

2016a. Explaining relationships between coke quality index and coal properties by Random Forest method. Fuel. 182:754–760. doi: 10.1016/j.fuel.2016.06.034

Chelgani

, Matin

, Makaremi

2016b. Modeling of free swelling index based on variable importance measurements of parent coal properties by random forest method. Measurement. 94:416–422. doi: 10.1016/j.measurement.2016.07.070

Chen

, Liao

, Qian

, Chen

2012. Vibrating high gradient magnetic separation for purification of iron impurities under dry condition. Int J Miner Process. 102–103:136–140. doi: 10.1016/j.minpro.2011.11.012

Dahe

2002. A large scale application of SLon magnetic separator in Meishan Iron Ore Mine. Phys Separ Sci Eng. 11(1–2):1–8.

Dahe

2003. SLon magnetic separator applied to upgrading the iron concentrate. Phys Separ Sci Eng. 12(2):63–69. doi: 10.1080/14786470310001603487

Dobbins

, Domenico

, Dunn

2007. A discussion of magnetic separation techniques for concentrating ilmenite and chromite ores. International Heavy Minerals Conference Southern African Mining and Metallurgy. p. 197–204.

Dobbins

, Dunn

, Sherrell

2009. Recent advances in magnetic separator designs and applications. The 7th International Heavy Minerals Conference “What next”, The Southern African Institute of Mining and Metallurgy, p. 63–70.

10.

Hearn

, Dobbins

2000. SLon magnetic separator: A new approach for recovering and concentrating iron ore fines. Montreal Energy Mines Montreal.

11.

Jafari

, Chelgani

, Shafaie

, Abdollahi

, Hadavandi

2019a. Study effects of conventional flotation reagents on bioleaching of zinc sulfide. J Ind Eng Chem. 78:364–371. doi: 10.1016/j.jiec.2019.05.033

12.

Jafari

, Golzadeh

, Shafaei

, Abdollahi

, Gharabaghi

, Chehreh Chelgani

2019b. Effects of conventional flotation frothers on the population of Mesophilic microorganisms in different cultures. Processes. 7(10):653. doi: 10.3390/pr7100653

13.

, Han

, Xu

, Gong

2018. A preliminary investigation into separating performance and magnetic field characteristic analysis based on a novel matrix. Minerals. 8(3):94. doi: 10.3390/min8030094

14.

Liu

, Peng

, Hu

2013. Removing iron by magnetic separation from a potash feldspar ore. Journal of Wuhan University of Technology-Mater Sci Ed. 28(2):362–366. doi: 10.1007/s11595-013-0696-3

15.

Makhula

, Falcon

, Bergmann

, Bada

2016. Statistical analysis and concentration of iron ore using Longi LGS 500 WHIMS. Int J Min Sci Technol. 26(5):769–775. doi: 10.1016/j.ijmst.2016.05.052

16.

Matin

, Chelgani

SC.

2016. Estimation of coal gross calorific value based on various analyses by random forest method. Fuel. 177:274–278. doi: 10.1016/j.fuel.2016.03.031

17.

Matin

, Farahzadi

, Makaremi

, Chelgani

, Sattari

2018. Variable selection and prediction of uniaxial compressive strength and modulus of elasticity by random forest. Appl Soft Comput. 70:980–987. doi: 10.1016/j.asoc.2017.06.030

18.

Matin

, Hower

, Farahzadi

, Chelgani

SC.

2016. Explaining relationships among various coal analyses with coal grindability index by Random Forest. Int J Miner Process. 155:140–146. doi: 10.1016/j.minpro.2016.08.015

19.

Nazari

, Chelgani

, Shafaei

, Shahbazi

, Matin

, Gharabaghi

2019. Flotation of coarse particles by hydrodynamic cavitation generated in the presence of conventional reagents. Sep Purif Technol. 220:61–68. doi: 10.1016/j.seppur.2019.03.033

20.

Panda

, Biswal

, Venugopal

, Mandre

2018. Recovery of ultra-fine iron ore from iron ore tailings. Trans Indian Inst Met. 71(2):463–468. doi: 10.1007/s12666-017-1177-8

21.

Ren

, Zeng

, Zhang

2015. Magnetic field characteristics analysis of a single assembled magnetic medium using ANSYS software. Int J Min Sci Technol. 25(3):479–487. doi: 10.1016/j.ijmst.2015.03.024

22.

Shahbazi

, Chelgani

, Matin

SS.

2017. Prediction of froth flotation responses based on various conditioning parameters by Random Forest method. Colloids Surf, A. 529:936–941. doi: 10.1016/j.colsurfa.2017.07.013

23.

Svoboda

2001. A realistic description of the process of high-gradient magnetic separation. Miner Eng. 14(11):1493–1503. doi: 10.1016/S0892-6875(01)00162-5

24.

Tang

, Li

, Ni

, Fan

2019. Recovering iron from iron ore tailings and preparing concrete composite admixtures. Minerals. 9(4):232. doi: 10.3390/min9040232

25.

Tohry

, Chelgani

, Matin

, Noormohammadi

2019. Power-draw prediction by random forest based on operating parameters for an industrial ball mill. Adv Powder Technol. 31(3):967–972. doi: 10.1016/j.apt.2019.12.012

26.

Tohry

, Dehghani

, Hosseini-Nasab

2017. Removal of fine gangue minerals from Chador-malu iron concentrate using hydroseparator. Physicochem Probl Miner Process. 53:250–263.

27.

Xiong

, Liu

, Chen

1998. New technology of pulsating high gradient magnetic separation. Int J Miner Process. 54(2):111–127. doi: 10.1016/S0301-7516(98)00009-X

28.

Yakubailik

, Balaev

, Ganzhenko

2017. Evaluation of Additional Iron Recovery from Iron Ore Tailings. J Min Sci. 53(3):559–564. doi: 10.1134/S1062739117032511

29.

Z-l

, Sun

H-h

, Wei

X-q

, Li

2009. Iron ore tailings used for the preparation of cementitious material by compound thermal activation. Int J Miner Metall Mater. 16(3):355–358. doi: 10.1016/S1674-4799(09)60064-9

30.

Zeng

, Dahe

2003. The latest application of SLon vertical ring and pulsating high-gradient magnetic separator. Miner Eng. 16(6):563–565. doi: 10.1016/S0892-6875(03)00102-X

Variable importance assessments of an innovative industrial-scale magnetic separator for processing of iron ore tailings

Abstract

Keywords

1 Introduction

Table 1 Applications of SLon technology for mineral beneficiation.

2.1 Process

2.2.1 Pearson correlation

(1) 2.2.2 Random forest

3 Results and Discussion

Footnotes

Acknowledgment

References

Table 1
Applications of SLon technology for mineral beneficiation.

(1)
2.2.2 Random forest