地質冶金學建模在剛果（金）SICOMINES難處理銅鈷礦中的應用

王玲; 杜宇航; 趙戰鋒; 張文娟; 馬保中; 王成彥

doi:10.13374/j.issn2095-9389.2023.01.02.001

地質冶金學建模在剛果（金）SICOMINES難處理銅鈷礦中的應用

doi: 10.13374/j.issn2095-9389.2023.01.02.001

王玲^{1, 2},
杜宇航¹,
趙戰鋒³,
張文娟^{1, 2},
馬保中^{1, 2},
王成彥^{1, 2, ,}

1.
北京科技大學鋼鐵冶金新技術國家重點實驗室，北京 100083
2.
北京科技大學冶金與生態工程學院，北京 100083
3.
中國恩菲工程技術有限公司，北京 100038

基金項目: 國家重點研發計劃資助項目（2022YFC2904505）

詳細信息

通訊作者:
E-mail: wchy3207@sina.com

中圖分類號: TG142.71
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出版歷程
- 收稿日期: 2023-01-02
- 網絡出版日期: 2023-04-07

Application of geometallurgical modeling in SICOMINES refractory copper–cobalt deposit in Congo (Kinshasa)

WANG Ling^{1, 2},
DU Yuhang¹,
ZHAO Zhanfeng³,
ZHANG Wenjuan^{1, 2},
MA Baozhong^{1, 2},
WANG Chengyan^{1, 2
, ,}

1.
State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China
2.
School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China
3.
China ENFI Engineering Co., Ltd., Beijing 100038, China

More Information

Corresponding author: E-mail: wchy3207@sina.com

摘要

摘要: SICOMINES銅鈷礦位于剛果（金）科盧韋齊南西側，是中部非洲加丹加銅礦帶的典型礦床. 由于礦床成因復雜，礦石中形成了十多種銅、鈷礦物，尤其各種鈷礦物選冶性質差異大，一直存在選冶工藝復雜，生產不穩定，回收率低的問題. 為此，本文首次采用Datamine和Leapfrog軟件構建了鈷的冶金地質學模型，首先，收集歷史勘查資料，建立礦區地層和礦化域模型，初步獲得鈷在空間的品位變化規律；其次，進行采樣設計，采集代表鈷在地層和礦體中品位分布規律的工藝礦物學樣品；再次，采用工藝礦物學綜合手段，獲得各樣品中礦物含量和鈷賦存狀態的定量數據，并采用單一域賦值法和距離冪次反比法等插值手段寫入模型；然后，根據鈷礦物選冶類型的空間分布規律劃分了5個空間選冶域，分別為適于浮選域（TYPE1）、適于磁選域（TYPE2）、適于浮選-磁選聯合分選域（TYPE3）、適于浸出域（TYPE4）和難以分選域（TYPE5），構建初步地質冶金學模型；最后，通過對5個選冶域中綜合樣分別進行選礦實驗來驗證模型. 實驗結果顯示，采用礦山現行的浮選–磁選聯合工藝流程，5個選冶域中鈷的回收率和精礦品位差距明顯，現有工藝流程只適用于空間域TYPE1、TYPE2和TYPE3，模型中選冶域的劃分合理. 根據模型中鈷的賦存狀態和有效鈷品位進行配礦，可以起到穩定現行生產工藝，提高鈷回收率的作用，同時，構建的地質冶金學模型為今后實現SICOMINES礦區鈷的分采分選提供指導.
- 地質冶金學 /
- SICOMINES /
- 難處理銅鈷礦 /
- 工藝礦物學 /
- 選冶空間域 /
- 礦石可選性
Abstract: The SICOMINES Cu–Co ore deposit is located in southwest Kolwezi, Congo (Kinshasa), and is a typical deposit in the Katanga Copper Belt in central Africa. Dozens of Cu and Co minerals exist in the deposit as a result of the superposition and transformation of three complex ore-forming stages, including the sediment-hosted, hydrothermal, and oxidation periods; some of these minerals include heterogenite, carrollite, chalcocite, malachite, Co-containing malachite, spherocobaltite, Cu/Co-containing psilomelane, and Co-containing limonite. The mineralogy and processability properties among Co minerals differ considerably. The variability in Co minerals poses substantial challenges in establishing a universal beneficiation or extraction process that can accommodate all geometallurgical variations. The current Co-recovery process integrates flotation and magnetic separation techniques. However, the lack of fundamental knowledge about the spatial distribution of Co minerals and the poor adaptability of current Co-recovery processes to adapt to variable ores contribute to considerable Co losses in mine tailings. The recovery efficiency for Co is generally low, and the operational stability of the process is unstable. To address the issues, this study devised a geometallurgical model of Co in an ore body using Datamine and Leapfrog software for the first time. Initially, historical exploration data were collected, strata and mineralized domain models were developed, and the spatial variation in Co grade was preliminarily obtained. Subsequently, a sampling design was implemented to collect samples for process mineralogical research, effectively representing the Co-grade distribution within the strata and ore bodies. Furthermore, quantitative data of the mineral content and Co-occurrence state for each sample were obtained using a process mineralogical method, and these data were incorporated into the model using interpolation methods such as single-domain assignment and the distance inverse power ratio. As a result, five spatial beneficiation zones were obtained based on the spatial distribution of Co minerals with varying processability properties. These zones were classified as suitable for flotation (TYPE1), suitable for magnetic separation (TYPE2), suitable for combined magnetic separation and flotation (TYPE3), suitable for leaching (TYPE4), and difficult to recover (TYPE5); this classification resulted in the formation of a preliminary geometallurgical model. Finally, comprehensive samples were collected from the five beneficiation zones for the beneficiation experiments. The results revealed that the integrated magnetic separation and flotation process employed in the mine achieved varying Co-recovery efficiencies across the five beneficiation zones. This process proves applicable solely to the spatial domains of TYPE1, TYPE2, and TYPE3. The results also indicated that the classification of beneficiation zones in the geometallurgical model was within reason. Reasonable ore blending, based on the occurrence state of Co and the effective Co grade in the model, contributes to stabilizing current production and enhancing Co recovery. The developed geometallurgy model can be continuously optimized by adding sampling points or mineralogy parameters such as Co mineral particle size, mineral liberation degree, and Co-associated relationship with other minerals. The developed geometallurgy model serves as a valuable guide for the realization of classified mining and separation of Co ores in the SICOMINES mining region and for appropriate management.
- geometallurgy /
- SICOMINES mine /
- refractory Cu–Co ore /
- process mineralogy /
- spatial beneficiation zones /
- processability properties of mineral

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圖 1 礦區地質圖. (a) 地質圖；(b) 區域地質圖；(c) 地層柱狀圖

Figure 1. Geological map of the mining area: (a) geological map; (b) regional geological map; (c) stratigraphic histogram

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圖 2 地層模型. (a) 平面圖；(b) AB位置剖面圖

Figure 2. 3D stratigraphic model: (a) plane view; (b) section view

Notes: 1—Q, quaternary sediments; 2—RGS, dolomitic siltstone, mudstone; 3—CMN, dolomite, dolomitic sandstone, siltstone and shale; 4—SDS, dolomitic shale; 5—BOMZ, dolomitic quartz sandstone; 6—SDB, dolomitic shale; 7—RSC, massive and honeycomb dolomite; 8—RSF, stratified silicified dolomite and sandstone; 9—RAT, sandstone, conglomerate, siltstone and mudstone; 10—K granite basement.

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圖 3 顯示鈷品位在地層和礦體空間分布規律的礦體模型

Figure 3. 3D orebody model representing the grade distribution of Co in the strata and ore bodies

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圖 4 樣品采集代表性統計. (a) 樣品的鈷品位分布頻率直方圖；(b) 樣品地層分布箱線圖

Figure 4. Representative statistics of sampling: (a) distribution frequency of Co grade; (b) distribution in strata

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圖 5 礦體模型中樣品空間分布

Figure 5. Spatial distribution of samples in the orebody model

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圖 6 樣品中典型鈷礦物的SEM圖及能譜分析. (a) 水鈷礦; (b) 鈷孔雀石; (c) 含鈷白云石; (d) 硫銅鈷礦; (e) 含鈷輝銅礦; (f) 含鈷綠泥石

Figure 6. SEM images and EDS analysis of cobalt minerals in the samples: (a) cobaltite; (b) Co-bearing malachite; (c) Co-bearing dolomite; (d) carrollite4; (e) cobalt-bearing chalcocite; (f) cobalt-bearing chlorite

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圖 7 重要礦物空間分布模型

Figure 7. Spatial distribution model of important minerals,

Notes: M1—heterogenite; M2—carollite; M7—spherocobaltite; M8—Cu/Co-containing psilomelane

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圖 8 鈷礦石選冶類型空間分布

Figure 8. Co spatial beneficiation zones according to geometallury and processability properties

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圖 9 不同選冶域的綜合樣選礦實驗結果對比圖

Figure 9. Co recovery index of comprehensive samples from five beneficiation zones using combined magnetic separation and flotation process

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表 1 建模過程礦物種類及編號

Table 1. Mineral and code in modeling

No.	Mineral	No.	Mineral	No.	Mineral
M1	Heterogenite	M10	Co-containing dolomite	M19	Quartz
M2	Carollite	M11	Dolomite	M20	Feldspar
M3	Chalcocite	M12	Calcite	M21	Apatite
M4	Chalcopyrite, Pyrite	M13	Chrysocolla	M22	Diopsidite
M5	Co-containing malachite	M14	Pseudomalachite	M23	Kaolinite
M6	Malachite	M15	Mica	M24	Zircon
M7	Spherocobaltite	M16	Chlorite	M25	Monazite
M8	Cu/Co-containing psilomelane	M17	Zigueline, Melaconite
M9	Co-containing limonite	M18	Rutile, Ilmenite

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表 2 鈷空間選冶域劃分標準及選冶域中鈷金屬量及鈷礦物含量

Table 2. Division standard of Co spatial beneficiation zone and content of Co metal and Co minerals in the zone

Type	Division standard	Co grade/%	Mass fraction of Co mineral/%
Type	β_Co(n)	Co grade/%	Co1	Co2	Co3	Co4
TYPE1	β_Co(1) ≥ 70	0.38	79.54	12.79	4.30	3.37
TYPE2	β_Co(2) ≥ 70	0.30	12.40	83.89	2.33	1.37
TYPE3	β_Co(1) + β_Co(2) ≥ 70	0.39	46.33	40.46	6.96	6.25
TYPE4	β_Co(1) + β_Co(2) < 70 β_Co(3) ≥ 60	0.24	27.05	23.93	44.60	4.42
TYPE5	β_Co(1) + β_Co(2) < 70 β_Co(3) < 60	0.39	52.19	11.21	21.53	15.07

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