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Transactions of Nonferrous Metals Society of China 2019年第12期29卷 2615-2626页

作者： omid salmani nuri Mehdi IRANNAJAD Akbar MEHDILO Department of Mining and Metallurgical Engineering Amirkabir University of TechnologyTehranIran Department of Engineering University of Mohaghegh ArdabiliArdabiliIran

研究表面酸溶解对钛铁矿(IL)及其常见伴生脉石矿物(包括橄榄石-辉石(Ol-Px)、透闪石-斜绿泥石(Tr-Cch)和石英)浮选动力学的影响。结果表明,通过表面溶解,钛铁矿的吸附速率常数从5.272增大到8.441 mol/(g·min),而Ol-Px、Tr-Cch和石英的... 详细信息

研究表面酸溶解对钛铁矿(IL)及其常见伴生脉石矿物(包括橄榄石-辉石(Ol-Px)、透闪石-斜绿泥石(Tr-Cch)和石英)浮选动力学的影响。结果表明,通过表面溶解,钛铁矿的吸附速率常数从5.272增大到8.441 mol/(g·min),而Ol-Px、Tr-Cch和石英的吸附速率常数分别从6.332、7.309和7.774mol/(g·min)减小为5.034、6.223和7.371mol/(g·min)。二元混合矿物的浮选实验结果表明,经表面溶解后,从Ol-Px、Tr-Cch和石英中浮选钛铁矿的修正速率常数值分别从36.15、36.52和47.86min^-1增大到41.72、45.78和56.24min^-1,导致从脉石矿物中分离预处理过的钛铁矿过程的动力学选择性指数(SI)增大。ICP-MS分析证实,脉石矿物动力学参数的降低可能是由于其表面Fe^2+、Ca^2+和Mg^2+的去除,导致矿物表面缺乏足够的活性位点与捕集剂组分发生作用。接触角测量结果证实,表面溶解可防止矿物表面形成稳定的疏水层,从而可在矿物和气泡之间发生稳定的附着。一般来说,钛铁矿浮选动力学参数的提高与伴生脉石矿物中的铁含量呈负相关关系。

关键词：表面溶解浮选动力学参数修正速率常数选择性指数捕集剂吸附

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Optimization of Ilmenite Dissolution by Synergistic Effect of Oxalic Acid and Hydrochloric Acid for Preparing Synthetic Rutile

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International Journal of Nonferrous Metallurgy 2018年第3期7卷 25-38页

作者： Mohammad Hasan omidi omid salmani nuri Hassan Tavakoli Department of Chemistry Faculty of Science University of Guilan Guilan Iran Department of Mining and Metallurgical Engineering Amirkabir University of Technology Tehran Iran Department of Physics and Chemistry Faculty of Basic Sciences Imam Ali University Tehran Iran

This study was carried out to investigate the interaction of oxalic acid with hydrochloric acid to attain the better performance for iron dissolution in comparison to absence of oxalic acid. The effects of oxalic acid on ratio of hydrochloric acid, acid concentration, ratio of liquid to solid, temperature and dissolution time are investigated for the dissolution of Fe and Ti from ilmenite to produce synthetic rutile. The DX7 software basing on an experimental design method with the central composite of response surface design is applied to specify the effects of the parameters and to optimize the leaching process. The optimum condition was determined by analysis of variance (ANOVA), indicating that the ratio of oxalic acid to hydrochloric acid for Fe dissolution and acid concentration for Ti dissolution were the most effective parameters. The results showed that the dissolution of Fe and Ti in 30% (w/w) hydrochloric acid solution was only 48.65% and 5.14%, respectively, while at the same condition and in the presence of oxalic acid with twice the ratio, these values are increased to 78.65% and 12.06%, respectively. The optimum values of parameters were as follows: oxalic acid to hydrochloric acid ratio (2:1), acid concentration (30%), ratio of liquid to solid (10), temperature (160 °C), and dissolution time (3 h). By applying the optimized parameters, Fe and Ti dissolution of 97.15% and 2.8% were predicted by the software with a desirability of 0.745. The results of leaching tests indicated that the Fe and Ti dissolution of 97.58% and 2.43%, were achieved, respectively, which are very close to the predicted value.

关键词： Ilmenite Oxalic Acid Hydrochloric Acid Synthetic Rutile Leaching

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Estimation of Copper and Molybdenum Grades and Recoveries in the Industrial Flotation Plant Using the Artificial Neural Network

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International Journal of Nonferrous Metallurgy 2016年第3期5卷 23-32页

作者： Ebrahim Allahkarami omid salmani nuri Aliakbar Abdollahzadeh Bahram Rezai Mostafa Chegini Amirkabir University of Technology Tehran Iran University of Kashan Kashan Iran

In this paper, prediction of copper and molybdenum grades and their recoveries of an industrial flotation plant are investigated using the Artificial Neural Networks (ANN) model. Process modeling has done based on 92 datasets collected at different operational conditions and feed characteristics. The prominent parameters investigated in this network were pH, collector, frother and F-Oil concentration, size percentage of feed passing 75 microns, moisture content in feed, solid percentage, and grade of copper, molybdenum, and iron in feed. A multilayer perceptron neural network, with 10:10:10:4 structure (two hidden layers), was used to estimate metallurgical performance. To obtain the optimal hidden layers and nodes in a layer, a trial and error procedure was done. In training and testing phases, it achieved quite correlations of 0.98 and 0.93 for Copper grade, of 0.99 and 0.92 for Copper recovery, of 0.99 and 0.92 for Molybdenum grade and of 0.99 and 0.94 for Molybdenum recovery prediction, respectively. The proposed neural network model can be applied to determine the most beneficial operational conditions for the expected Copper and Molybdenum grades and their recovery in final concentration of the industrial copper flotation process.

关键词： Prediction of Grade and Recovery Artificial Neural Network Copper Flotation Copper Concentrator Plant

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