The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
D A Rice - One of the best experts on this subject based on the ideXlab platform.
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Selective flocculation of zinc concentrate to reduce silica contamination
Mining Metallurgy & Exploration, 1994Co-Authors: W. C. Hirt, D A RiceAbstract:Selective flocculation was used to produce a superclean zinc concentrate by minimizing silica contamination. Research was carried out on a flotation concentrate from the Jersey Miniere Zinc Company (JMZ) in Gordonsville, TN. The material was first dispersed with sodium silicate and then causticized tapioca starch was used to selectively flocculate the sphalerite, leaving the silica in suspension. This technique has been applied commercially to iron Ore Beneficiation but never to zinc processing.
Marco Aurélio Soares Martins - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Simulation for Verification of Gains with the Implementation of Advanced Process Control for a Nickel Sulphide Ore Beneficiation Plant
IFAC Proceedings Volumes, 2016Co-Authors: Andre Nogueira, Mônica Guimarães Vieira, Marco Aurélio Soares MartinsAbstract:Abstract A dynamic simulation tool was used to quantify the benefits from the implementation of an advanced control system for a low grade Nickel sulphide Ore Beneficiation plant. In order to obtain a consistency between the simulation and the process a database that represented the natural variability of the Ore was used. The information consisted of laboratory analyzes every 2 hours for the concentrations of nickel in the feed, product and tailings from the plant. In addition, variability in the feed rate was included in order to simulate the performance of the circuit. Empirical and phenomenological mathematical models, of each unit operation of the processing circuit, were calibrated in the simulator. The representativeness of the tool was proven by the confrontation of the results obtained against the data from the laboratory. The variables of the models were changed during the dynamic simulation to reproduce the usual control done by the operators. To provide comparison data another scenario was simulated with advanced process control. This was possible due to the fact that the simulator has a data manager performed by an artificial intelligence that allows configuring process control strategies, such as an expert system. After the analysis of simulation results, it was possible to estimate the improvement obtained with the implementation of an expert system through evidence of increased Nickel metallurgical recovery. There was also reduction of the variability of product quality. The above mentioned results shows that techniques for advanced process control are among the most effective methods in cost and time to improve plant performance.
Luo Xian-ping - One of the best experts on this subject based on the ideXlab platform.
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Huili Zinc Concentrator of cleaner production technology research
2009Co-Authors: Luo Xian-pingAbstract:Through the development of clean and highly efficient new lead-zinc sulfide Ore Beneficiation process,tailings in the recovery of zinc oxide,mineral processing waste water recycling,copper Ore recovery,technical means increased the comprehensive utilization rate of non-renewable resources.Mineral processing to achieve the reuse of all waste water dressing eliminates the environmental impact of wastewater concentrator to achieve a cleaner production.
K A Natarajan - One of the best experts on this subject based on the ideXlab platform.
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Developments in Biotechnology for Environmentally Benign Iron Ore Beneficiation
Transactions of the Indian Institute of Metals, 2013Co-Authors: K A NatarajanAbstract:Biomineralization and biogenesis of iron Ore deposits are illustrated in relation to indigenous microorganisms inhabiting iron Ore mines. Aerobic and anaerobic microorganisms indigenous to iron oxide mineralization are analyzed. Microbially-induced flotation and flocculation of iron Ore minerals such as hematite, alumina, calcite and quartz are discussed with respect to use of four types of microorganisms, namely, Paenibacillus polymyxa, Bacillus subtilis, Saccharomyces cerevisiae and Desulfovibrio desulfuricans. The role of the above organisms in the removal of silica, alumina, clays and apatite from hematite is illustrated with respect to mineral-specific biOreagents, surface chemical changes and microbe–mineral interaction mechanisms. Silica and alumina removal from real iron Ores through bioBeneficiation is outlined. Environmental benefits of bioBeneficiation are demonstrated with respect to biodegradation of toxic reagents and environmentally-safe waste disposal and processing.
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Biotechnological innovations in iron Ore Beneficiation
2010Co-Authors: K A NatarajanAbstract:Many types of microorganisms occur intimately associated with iron Ore deposits contributing to biogenic formation and redox conversion of iron oxides and associated minerals. Bacteria such as Paenibacillus polymyxa and sulfate reducing bacteria are capable of altering the surface chemical behavior of iron Ore minerals such as hematite, alumina, calcite and silica. Bacterial cells and biOreagents such as bioproteins can be utilized to induce iron Ore flotation or flocculation. Mineral-specific biOreagents such as proteins and exopolysaccharides are generated when bacteria are grown in the presence of the above minerals.
Andrey Kupin - One of the best experts on this subject based on the ideXlab platform.
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Development of Classification Model Based on Neural Networks for the Process of Iron Ore Beneficiation
Technology audit and production reserves, 2019Co-Authors: Anton Senko, Andrey Kupin, Bohdan MyskoAbstract:The object of research is the processes of Beneficiation of iron Ore in the conditions of a mining and processing plant. Iron Ore Beneficiation factory near parallel to existing production lines or concentration sections. One of the key characteristics that determine the operating mode of the grinding apparatus is the crushing of Ore, directly related to its strength. But unlike other parameters, the problem is with constant monitoring of the strength value. The determination of this parameter requires a laboratory study of the technological Ore sample from the conveyor of the Beneficiation section. The specifics of the working conditions of the Beneficiation section complicate the monitoring of the strength parameter by installing a hardware sensor directly on the conveyor. TherefOre, it is proposed to determine it by fOrecasting. Based on Big Data information technologies, using the accumulated statistical data, it is possible to fOrecast data between the technological samples. The technological process of Ore Beneficiation in the conditions of a mining and processing plant is systematically analyzed. The generalized structure of the classification model is presented, which, based on the accumulated statistical data of the Beneficiation section based on the current parameters of the section, is able to determine the parameters of incoming raw materials. The unknown parameter is determined using the counterpropagation neural network, which combines the following algorithms: a self-organizing Kohonen map and a Grossberg star. Their combination leads to an increase in the generalizing properties of the network. The training sample is formed as a result of clustering the statistical data of the Beneficiation section and selecting the cluster to which the current status of the section works. The presented fOrecasting algorithm, based on a combination of clustering methods and the use of a predictive neural network, allows the specialist to mOre quickly receive recommendations for making decisions regarding the behavior of the object compared to obtaining laboratory test data.
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Neural Identification of Technological Process of Iron Ore Beneficiation
2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2007Co-Authors: Andrey KupinAbstract:This document presents results to identification of technological process (TP) of Beneficiation of iron Ore on bases of models NNARX (Neural Network based AutOregressive exogenous signal), NNARXMAX (Neural Network based AutOregressive, Moving Average, exogenous signal), NNOE (Neural Network Output Error). Computer modeling has been carried out with application of Neuro Solution software package. As internal modeling structure bases on the basis of multilayered perceptron (MLP) and networks of the radial basis functions (RBF) network have been analyzed. The sample of parameters of the Southern mining complex (Krivoy Rog city, Ukraine) has been used for training and verification. It is proved efficiency of application of neural networks for identification of parameters of concentrating technology.