Pyrometric temperature and size measurements of chalcopyrite particles during flash oxidation in a laminar flow reactor

Laurila, Toni; Hernberg, Rolf; Oikari, Risto; Joutsenoja, Timo; Mikkola, Petteri; Ranki-Kilpinen, Tiina; Taskinen, Pekka

doi:10.1007/s11663-005-0021-4

Cited by 13 publications

(9 citation statements)

References 15 publications

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“…It can also be seen that percentual errors range between 0.8% and 7.9%, which agree with results reported in literature where the two-wavelength method is used. Please note that temperature values are also in agreement with the theory, laboratory experiments and operation of flash smelting furnaces [ 26 , 27 , 28 , 29 ]. Additionally, by applying the preceding procedure, the estimated combustion temperatures of industrial signals depicted in Figure 4 are 1803.0 K ( ▬ signal) and 1995.0 K (▬ signal) respectively.…”

Section: Temperature and Emissivity Estimationsupporting

confidence: 76%

Flash Smelting Copper Concentrates Spectral Emission Measurements

Arias

Torres

Toro

et al. 2018

Sensors

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In this paper, we report on spectral features emitted by a reaction shaft occurring in flash smelting of copper concentrates containing sulfide copper minerals such as chalcopyrite (CuFeS2), bornite (Cu5FeS4) and pyrite (FeS2). Different combustion conditions are addressed, such as sulfur-copper ratio and oxygen excess. Temperature and spectral emissivity features are estimated for each case by using the two wavelength method and radiometric models. The most relevant results have shown an increasing intensity behavior for higher sulfur-copper ratios and oxygen contents, where emissivity is almost constant along the visible spectrum range for all cases, which validates the gray body assumption. CuO and FeO emission line features along the visible spectrum appear to be a sensing alternative for describing the combustion reactions.

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Section: Temperature and Emissivity Estimationsupporting

confidence: 76%

Flash Smelting Copper Concentrates Spectral Emission Measurements

Arias

Torres

Toro

et al. 2018

Sensors

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“…This has enabled, for example, the estimation of reaction temperatures and the observation of the main species of the reaction and its concentrations. This concept has been successfully applied to the combustion of liquids or gases [2] and pyrotechnic mixtures [3][4][5]. In these applications, the information provided by online spectra has allowed investigation of morphological structures of flames, types of reactions in mixtures pyrotechnics, the movement and burning of individual particles, the processes of igniting powders, propagation of gas explosions, and in situ observations of reactions in closed combustion chambers through optical fibers with optomechanics adapted for these processes.…”

Section: Introductionmentioning

confidence: 99%

Sensors and Process Control in Copper Smelters: A Review of Current Systems and Some Opportunities

et al. 2020

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Despite the widespread and extended use of sensor systems in different industries, there is an important gap to reach equivalent conditions in pyrometallurgical processes for primary production. In the specific case of copper pyrometallurgy, the situation is particularly challenging to incorporate the Industry 4.0 concept for the optimization of their operations. Currently, only two instruments can be identified at the commercial level: the Noranda pyrometer and the Online Production Control (OPC) system. The iron-making and steelmaking industries, however, present an advanced level of control based on monitoring and sensing networks throughout the entire process. This reality has served as a basis for developing a series of solutions based on radiometric sensors for copper pyrometallurgy. We present two types of sensing concept. The first one is applied to smelting and converting reactors based on the measurements of the radiation of the oxidation of different copper and iron sulfides. The second one considers hyperspectral imaging of molten phases flow during operations. The idea of this proposal is to transfer some commercial sensing technologies already in use in the steelmaking industry. In this article, the fundamentals of the sensor design, proofs of concept, and the initial industrial validations are reviewed. Finally, a discussion on the contribution of this knowledge and development opportunities within the framework of Industry 4.0 are addressed.

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“…The species involved during the combustion absorb and emit energy with specific characteristics, which can be used to retrieve information about the process condition [7]. In particular, optical information at specific wavelengths has been used to describe the oxidation of the main sulfide minerals such as chalcopyrite (CuFeS 2 ) and pyrite (FeS 2 ) [4,6], to estimate ignition temperatures [8][9][10], single particle temperature [4,6], and to model particle size distributions [11].…”

Section: Introductionmentioning

confidence: 99%

Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend

Díaz

Toro

Balladares

et al. 2019

Metals

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The pyrometallurgical processes for primary copper production have only off-line and time-demanding analytical techniques to characterize the in and out streams of the smelting and converting steps. Since these processes are highly exothermic, relevant process information could potentially be obtained from the visible and near-infrared radiation emitted to the environment. In this work, we apply spectral sensing and multivariate data analysis methodologies to identify and classify copper and iron sulfide minerals present in the blend from spectra measured during their combustion in a laboratory drop-tube setup, in which chemical reactions that take place in flash smelting furnaces can be reproduced. Controlled combustion experiments were conducted with two industrial concentrates and with high-grade mineral species as well, with a focus on pyrite and chalcopyrite. Exploratory analysis by means of Principal Component Analysis (PCA) applied on the spectral data depicted high correlation features among species with similar elemental compositions. Classification algorithms were tested on the spectral data, and a classification accuracy of 95.3% with a support vector machine (SVM) algorithm with a Gaussian kernel was achieved. The results obtained by the described procedures are shown to be very promising as a first step in the development of a predictive and analytical tool in search of fitting the current need for real-time control of pyrometallurgical processes.

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Pyrometric temperature and size measurements of chalcopyrite particles during flash oxidation in a laminar flow reactor

Cited by 13 publications

References 15 publications

Flash Smelting Copper Concentrates Spectral Emission Measurements

Flash Smelting Copper Concentrates Spectral Emission Measurements

Sensors and Process Control in Copper Smelters: A Review of Current Systems and Some Opportunities

Spectral Characterization of Copper and Iron Sulfide Combustion: A Multivariate Data Analysis Approach for Mineral Identification on the Blend

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