An integrated thermochemical-systems approach to the prediction of matte composition dynamics in an Ausmelt® nickel–copper matte converter

Georgalli, G.A.; Eksteen, Jacques; Reuter, M.A.

doi:10.1016/s0892-6875(02)00119-x

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…(1) Use reconciled data for TSL, [61] submerged arc (Si, FeSi, FeCr, FeMn, CaC 2 ), [59,62] salt slag aluminum recycling, [63] chemical waste incineration, and blast furnaces (among others) to create dynamic models linked to thermodynamics (captured in neural net functions) to predict metal recovery, heat loss, matte Fig. 10-Linking process design and large system simulation to environmental impact analysis.…”

Section: B Calibration Of Models Data Driven Modeling-big-data and mentioning

confidence: 99%

“…Fig. 11-Big-data analysis of ill-defined metallurgical systems (clockwise from top left): (i) top left dynamic modeling linking data driven approaches with thermodynamics; [61] (ii) top right intelligent control of submerged arc furnaces (SAFs) highlighting the central role of a database with reconciled data that link mine, ore, and reductant characterization numbers (geometallurgy based), mineral to metal product; [59] (iii) bottom left dynamic modeling of FeCr production and product quality; [62] and (iv) bottom right optimizing silicon metal recovery in a silicon SAF on the basis of various independent operational parameters. [59] …”

Section: Metallurgical Reactors-closers Of the Metal And Materials mentioning

confidence: 99%

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Digitalizing the Circular Economy

Reuter

2016

Metall Mater Trans B

104

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Metallurgy is a key enabler of a circular economy (CE), its digitalization is the metallurgical Internet of Things (m-IoT). In short: Metallurgy is at the heart of a CE, as metals all have strong intrinsic recycling potentials. Process metallurgy, as a key enabler for a CE, will help much to deliver its goals. The first-principles models of process engineering help quantify the resource efficiency (RE) of the CE system, connecting all stakeholders via digitalization. This provides well-argued and first-principles environmental information to empower a tax paying consumer society, policy, legislators, and environmentalists. It provides the details of capital expenditure and operational expenditure estimates. Through this path, the opportunities and limits of a CE, recycling, and its technology can be estimated. The true boundaries of sustainability can be determined in addition to the techno-economic evaluation of RE. The integration of metallurgical reactor technology and systems digitally, not only on one site but linking different sites globally via hardware, is the basis for describing CE systems as dynamic feedback control loops, i.e., the m-IoT. It is the linkage of the global carrier metallurgical processing system infrastructure that maximizes the recovery of all minor and technology elements in its associated refining metallurgical infrastructure. This will be illustrated through the following: (1) System optimization models for multimetal metallurgical processing. These map large-scale m-IoT systems linked to computer-aided design tools of the original equipment manufacturers and then establish a recycling index through the quantification of RE. (2) Reactor optimization and industrial system solutions to realize the ''CE (within a) Corporation-CEC,'' realizing the CE of society. (3) Real-time measurement of ore and scrap properties in intelligent plant structures, linked to the modeling, simulation, and optimization of industrial extractive process metallurgical reactors and plants for both primary and secondary materials processing. (4) Big-data analysis and process control of industrial metallurgical systems, processes, and reactors by the application of, among others, artificial intelligence techniques and computer-aided engineering. (5) Minerals processing and process metallurgical theory, technology, simulation, and analytical

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Section: B Calibration Of Models Data Driven Modeling-big-data and mentioning

confidence: 99%

Section: Metallurgical Reactors-closers Of the Metal And Materials mentioning

confidence: 99%

Digitalizing the Circular Economy

Reuter

2016

Metall Mater Trans B

104

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“…A decided advantage of batch processing is the ability to flexibly direct the converting to a definite composition and temperature, specifically tailored for the needs of downstream refining to PGM. Equally though, this requires a combination of feed-forward predictive (Georgalli et al 2002), on-line converter flame endpoint detection and in-bath sampling and rapid matte analysis (Bustos, Ip et al 1988;Viviers and Hines 2005) feedback controls.…”

Section: Batch End-point Controlmentioning

confidence: 99%

“…Non-equilibrium between a highly oxidised slag and matte. A view presented in thermodynamic equilibrium modelling of a TSL is of the bath being well-mixed (Georgalli et al 2002). This, however, .…”

Section: Converter Slag Foamingmentioning

confidence: 99%

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Converter processing of platinum group metals

Nelson

Georgalli

Hines

et al. 2018

Mineral Processing and Extractive Metallurgy

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The history and variety of commercial Platinum Group Metal (PGM) matte converting processes are explored. Aspects unique to pneumatic converting of high iron, lower grade mattes (typically up to 30% NiCuCo), often containing chrome (0.5-2% Cr 2 O 3 ), to PGM-enriched (1500-25000 g/t PGM) converter mattes, to endpoints specific to promote downstream refining to final metal are described. The range of converter vessels, involving batch, to staged, to continuous converting processes are presented, along with some of the operating requirements and challenges.

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