Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Fault detection in flotation processes based on deep learning and support vector machine

Effective fault detection techniques can help flotation plant reduce reagents consumption,increase mineral recovery,and reduce labor intensity.Traditional,online fault detection methods during flotation processes have concentrated on extracting a specific froth feature for segmentation,like color,shape,size and texture,always leading to undesirable accuracy and efficiency since the same segmentation algorithm could not be applied to every case.In this work,a new integrated method based on convolution neural network(CNN)combined with transfer learning approach and support vector machine(SVM)is proposed to automatically recognize the flotation condition.To be more specific,CNN function as a trainable feature extractor to process the froth images and SVM is used as a recognizer to implement fault detection.As compared with the existed recognition methods,it turns out that the CNN-SVM model can automatically retrieve features from the raw froth images and perform fault detection with high accuracy.Hence,a CNN-SVM based,real-time flotation monitoring system is proposed for application in an antimony flotation plant in China.

Process mineralogy of copper-nickel sulphide flotation by a cyclonic-static micro-bubble flotation column

In our study we investigated a refractory copper-nickel sulfide ore separation by using a cyclonic-static micro-bubble flotation column (FCSMC). The process mineralogy of the main products was studied. Using a scanning electron microscope-energy dispersive system (SEM-EDS) and an X-ray spectrometer the mineral category and content of samples were analyzed. By using a mineral liberation analyzer (MLA) the mineral liberation characteristics were revealed. It is shown that in roughing feed the monomers liberation degree of nickel pyrite and chalcopyrite take up 84.11% and 88.82%, respectively. In tailings, the lost nickel pyrite and chalcopyrite are mainly monomers. Therefore, strengthening the micro-fine particle recovery capacity is the key to increase recovery.

Effect of mineral processing wastewater on flotation of sulfide minerals

The effects of mineral processing wastewater on sulfide minerals were investigated by flotation,infrared spectrometry and electrochemistry test. The results show that lead-concentrate water can improve the flotation of galena,while the sulfur-concentrate water has negative effect on flotation of galena compared with distilled water. The flotation behavior of pyrite is contrary to that of galena in three kinds of water. Infrared spectra indicate that the residual collector in the lead-concentrate water is beneficial to the formation of lead xanthate on the surface of galena. Electrochemistry results indicate that electrochemistry reaction on galena surface has apparent change. The anode polarization is improved and cathode polarization is depressed.

Oil droplet movement and micro-flow characteristics during interaction process between gas bubble and oil droplet in flotation

Flotation is an efficient pre-treatment technology for oily water.In this work,the interaction process between the moving oil droplet and the gas bubble was studied by high-speed camera and Bassset-Boussinesq-Oseen(BBO)theoretical model,and the experimental and simulation results of the oil droplet trajectory were compared.Moreover,the micro-particle image velocimetry system was utilized to observe the flow inside and outside of the moving oil droplet.The results show that the BBO model with the mobile bubble’s surface can reflect the velocity change trend of the oil droplet during the interaction process between the moving oil droplet and the gas bubble,but there are some significant differences between the experimental and simulation results.While the oil droplet is moving on the bubble’s surface,the velocity of the area near the contact point of oil droplet–gas bubble is less than that of the other areas inside the oil droplet.Meanwhile,the flow of water above the oil drop is more biased towards the gas bubble.

Correlation Analysis of Control Parameters of Flotation Process

The dosage of gold-antimony flotation process of 5 main drugs,including Copper Sulfate,Lead Nitrate,Yellow Medicine,No.2 Oil,Black Medicine,with corresponding visual features of foam images,including Stability,Gray Scale,Mean R,Mean G,Mean B,Mean Average,Dimension and Degree Variance,were recorded.Parameter correlation analysis showed that the correlation among Copper Sulfate,Yellow Medicine,Black Medicine,as well as the correlation among Gray Scale,Mean R,Mean G,Mean B,is strong,and the correlation among Dimension,Gray Scale,Mean R,Mean G,Mean B,as well as the correlation between Stability and each dosing parameter,is week.It also indicated a feasible way to decrease the complexity of flotation control system by reducing some parameters.

Modeling of fine coal flotation separation based on particle characteristics and hydrodynamic conditions

Flotation is a complex multifaceted process that is widely used for the separation of finely ground minerals. The theory of froth flotation is complex and is not completely understood. This fact has been brought many monitoring challenges in a coal processing plant. To solve those challenges, it is important to understand the effect of different parameters on the fine particle separation, and control flotation performance for a particular system. This study is going to indicate the effect of various parameters （particle Characteristics and hydrodynamic conditions） on coal flotation responses （flotation rate constant and recovery） by different modeling techniques. A comprehensive coal flotation database was prepared for the statistical and soft computing methods. Statistical factors were used for variable selections. Results were in a good agreement with recent theoretical flotation investigations. Computational models accurately can estimate flotation rate constant and coal recovery （correlation coefficient 0.85, and 0.99, respectively）. According to the results, it can be concluded that the soft computing models can overcome the complexity of process and be used as an expert system to control, and optimize parameters of coal flotation process.

Flotation Separation on Rare Earth Minerals and Gangues

The flotation process of native rare earth minerals such as bastnasite, monazite, mixed minerals of bastnasite and monazite, using the new effective collector Dh was studied, respectively, and the flotation properties were described. The good qualities of the new collector Dh were revealed through comparing with other collector of rare earth minerals. The test results of different ore samples showed that at moderate pulp pH (8.5～9.5), rare earth minerals could be effectively separated from barium, calcium and silicon bearing intergrowth minerals (barite, calcite and silicate minerals) and high quality rare earth concentrates could be obtained successfully by the new collector Dh, acid silica gel, turpentine and reagents fitting together rationally. In order to determine optimum technical conditions, the effect of pulp pH, pulp temperature, pulp density and the effect of dosage of reagents (Dh and acid silica gel) on the flotation were investigated in the test. Simultaneously, the mechanism of the flotation of rare earth minerals from intergrowth minerals was explored. The infrared spectra for Dh and rare earth cation by analysis in theory showed that Dh formed chelate complex with rare earth cation and were adsorbed on the surfaces of rare earth minerals. The mechanism of the intergrowth minerals depressed by acid silica gel can be explained as gummy colloid hydrolyzed from acid silica gel which were selectively absorbed on the gangue minerals, making them hydrophilic and depressed, with pulp pH value of alkalescent.

Upgrading graphite by flotation at Bogala Mines in Sri Lanka

Graphite is naturally floatable due to its hydrophobic pro pe rty and also soft and smears on other gangue particles, rendering the gangue mor e or less floatable too. Due to this reason it is important to concentrate on ar eas such as suitable flotation reagents, depression agents, pH modifiers, and pa rticle size to be fed during the process. The paper surveys and analyses the sui table particle size to be fed to achieve high-grade concentrate. According to t h e work carried out the author suggested the ideal cost effective flotation f low sheet for improved results at Bogala Mines in Sri Lanka.

A dynamic size-based time series feature and application in identification of zinc flotation working conditions

Conventional feature description methods have large errors in froth features due to the fact that the image during the zinc flotation process of froth flotation is dynamic,and the existing image features rarely have time series information.Based on the conventional froth size distribution characteristics,this paper proposes a size trend core feature(STCF)considering the froth size distribution,i.e.,a feature centered on the time series of the froth size distribution.The core features of the trend are extracted,the inter-frame change factor and the inter-frame stability factor are given and two calculation methods of the feature factors are proposed.Meanwhile,the STCF feature algorithm was established based on the core features by adding the inter-frame change factor and the inter-frame stability factor.Finally,a flotation condition recognition model based on BP neural network was established.The experiments show that the recognition model has achieved excellent results,proving that the method proposed effectively overcomes the limitation of the lack of dynamic information in the existing traditional size distribution features and the introduction of the two factors can improve the classification accuracy to varying degrees.

A honeycomb-tube packing medium and its application to column flotation

We address problems in the development of large-scale flotation columns that use short cylinders. As a starting point, we investigated the packing medium to identify a highly efficient internal packing for the flotation column. The chosen packing was a honeycomb structure with an aperture diameter of 80 mm, a web thickness of 0.80 mm, a film height of 1000 mm, packed into a 400 mm diameter space, which completely filled the vessel at optimal cost. The column consisted of a modular ring of single-hole hexagonal honeycomb tube packing made from atactic polyproplene (PP-R). The packing was tested in a cyclonic, static micro-bubble flotation column. Computational fluid dynamic modeling was used to analyze the flotation fluid in a honeycomb tube packed flotation column. Our results show that the fluid axial movement was maximized and that the transverse fluid velocities were zero in the vicinity of axial flow. Using the honeycomb tube packing for copper sulfide flotation we observed that the average concentration in the product was increased to 25.41%, from an average feed concentration of 0.729%, with an average recovery of 92.92%. The demands of on-site industrial production were met.

Study on the Axial Dispersion of Liquid in Column Flotation

An experimental study on the axial dispersion of liquid was carried out in a 0.382-m-ID flotation column packed with different structured packings or free of packings. The correlations of axial Peclet numbers with the liquid and gas superficial Reynolds numbers were developed for various packings. Among the packings tested, it is found that in the column packed with 250Y or 350Y packings the axial dispersion is the lowest. The addition of frother can decrease the axial dispersion. By the simulation analysis of the one-dimension dispersion model of packed flotation column, it is found that small axial dispersion, high collection rate constant and low axial liquid velocity can increase the collection zone recovery.

Research on Effect of Magnetized Pulp on Coal Slime Flotation

Effect of magnetization on oxygen concentration, pH, surface zeta potential, and wet heat of flotation pulp were researched. The result shows that magnetization treatment can improve the floatablility of coal and increase the difference in wet heat among coal, refuse, and pyrite, which is favorable for slime flotation and for removing sulfur and ash from coal.

The Effects of Water Recycling on Flotation at a North American Concentrator—Part 1

Water chemistry and its impact on mineral processing operations are not well understood and often not adequately monitored. CanmetMINING, as part of its water management research program, has been involved in a project initiated to identify opportunities for improving water recovery, water treatment, and recycling in the mining and mineral processing operations. One of the main objectives of this work is to evaluate and assess water chemistry and identify factors that impact mineral recovery, concentrate grade, and metal extraction efficiencies in order to understand and mitigate negative impacts of water recycling and improve process efficiency. In collaboration with a North American concentrator, CanmetMINING has been involved in assessing the water chemistry in the mill and evaluating water recycling options for select process streams to reduce fresh water intake and maximize recycling. The overall goal of the project is to investigate options for water recycling (increase the thickener overflow recirculation from thickener overflow tank) without affecting nickel and copper metallurgy. The results of the sampling campaigns showed that the water chemistry of the streams was fairly consistent throughout the year with no significant seasonal variations. The laboratory tests illustrated that when higher quantities of thickener overflow from thickener overflow were used, the nickel + copper grade versus nickel recovery curves shifted towards lower values. These observations were observed for the plant water samples obtained in April, June and August 2019.

不同矿浆温度下Ca^(2+)、Mg^(2+)对黄铁矿可浮性的影响

这是一篇矿物加工工程领域的论文。通过单矿物浮选实验、浮选溶液化学、Zeta电位、XPS,研究了不同矿浆温度下Ca^(2+)、Mg^(2+)对黄铁矿浮选效果的影响机理,结果表明:低温能够明显抑制黄铁矿的浮选,且温度降低能够明显弱化Ca^(2+)、Mg^(2+)对黄铁矿浮选的抑制作用。当矿浆温度从20℃降至5℃时,矿浆中带电粒子运动速度减慢,Zeta电位增大,生成氢氧化钙镁沉淀的临界pH值增大,纯水矿浆中黄铁矿表面的FeO/OH的比例减小,Ca^(2+)、Mg^(2+)矿浆中黄铁矿表面FeO/OH含量的降低幅度减小,减少了黄铁矿表面的氧化程度和活性吸附位点,减少了Ca^(2+)、Ca(OH)+,Mg^(2+)和Mg(OH)+等离子在黄铁矿表面的吸附;但低温并不改变Ca^(2+)、Mg^(2+)在黄铁矿表面的存在形式和吸附状态,pH值为9时,钙镁均以Ca^(2+)、Ca(OH)+、Mg^(2+)、Mg(OH)+的形式存在并吸附在黄铁矿表面。

基于浮选泡沫图像预测精矿品位的研究进展

随着人工智能技术在矿业生产的广泛应用,利用计算机视觉技术提高精矿品位预测的准确性和效率已成为必然趋势。在综述了传统图像处理算法和深度学习算法在精矿品位预测中的应用与发展历程基础上,并探讨了未来的发展趋势和挑战。传统图像处理技术通过提取泡沫图像的尺寸、颜色、纹理和流速等特征,结合分水岭分割、颜色矩、灰度共生矩阵和局部点特征匹配等算法进行特征提取。这些特征在计算资源有限的场景中具有一定的应用价值,但在应对精矿品位预测任务时精度较低。深度学习技术通过构建合适的模型架构并利用大量数据进行训练,能够提取高层语义特征,具有较高的预测精度,与图形处理单元(GPU)等高效运算设备配合使用,可实现高性能和高效率的统一。介绍了支持向量机(SVM)、极限学习机(ELM)等机器学习算法以及多层感知器(MLP)、全连接层和多尺度特征融合等深度学习算法在特征映射和品位预测中的应用,以及深度学习模型的发展历程。最后综述了工业界视觉检测系统的应用现状,并从数据驱动模型、多模态数据融合、算法实时性和数据集规模等方面分析了该领域所面临的挑战和未来发展趋势。

硫精矿中有价金属铜金银的综合回收利用实验

这是一篇矿物加工工程领域的论文。对某含铜金银硫精矿进行了选矿综合回收实验研究,实验结果表明,采用异丁基黄药+乙硫氮组合捕收剂,氧化钙做抑制剂,2^(#)油做起泡剂,经过一次粗选-三次精选-二次扫选的浮选工艺流程,最终可得到铜品位为16.66%、回收率为86.96%,金品位为278.95 g/t,回收率为75.56%;银品位为1848.74 g/t,回收率为78.55%的精矿产品,有效地实现了硫精矿中铜的综合回收,同时使金、银很好地富集在铜精矿中,便于回收利用,选矿指标理想。

湖南某铍矿综合回收实验

这是一篇矿物加工工程领域的论文。针对湖南某铍矿含BeO 0.47%、CaF_(2)44.86%,含铍矿物为金绿宝石,主要脉石矿物为绿泥石、云母、方解石、白云石等,铍矿物嵌布粒度较细,与绿泥石、方解石等脉石矿物共生密切,分选难度较大。基于金绿宝石与萤石以及与主要脉石矿物的可浮性差异,实验采用原矿浮选脱除硫化矿后—优先浮选萤石—浮选尾矿脱泥后反浮选脉石矿物的工艺流程处理该矿。在磨矿细度为-0.074 mm80%的条件下,采用组合捕收剂丁基黄药+丁铵黑药+乙硫氮浮选脱除硫化矿,然后利用组合捕收剂油酸钠+氧化石蜡皂浮选萤石得到含CaF_(2)96.32%、回收率为70.34%的萤石精矿,浮选尾矿脱泥后反浮选脉石矿物,可获得含BeO 1.57%、回收率为67.95%的铍精矿。铍精矿后续可采用冶金方法提取获得氧化铍产品。该工艺实现了对金绿宝石型铍矿和萤石矿物的综合回收,为同类型铍矿的开发利用提供参考。

风化矿选矿技术现状与进展

矿石风化后其理化性质发生了复杂变化,导致后续选矿难度大、分选效率低。详述了风化金属矿和风化非金属矿选矿技术现状,归纳总结了风化矿分选的主要方法。针对含泥量高、粒度细、嵌布复杂的风化金属矿,可采用预处理强化以及加强分级选矿等方法进行多段选别,提高选矿效果。针对风化非金属矿氧化程度高、杂质含量多的特点,利用辅助手段剥去矿物表面氧化层,联合浮选工艺以及分级选矿的方法可获得较高品位的精矿。通过对风化后的矿物进行工艺矿物学研究,可掌握目的矿物和伴生矿物风化后的矿物特征,为制定分选工艺、选择分选设备、确定选矿药剂提供技术指导。进行矿石工艺矿物学研究是风化矿分选的前提,开发先进分选工艺设备、研制高效选矿药剂是风化矿选矿未来研究的重点。