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Optimizing the operation of straight-grate iron-ore pellet induration systems using process models

Optimizing the operation of straight-grate iron-ore pellet induration systems using process models

Cross, Mark and Blot, Philippe (1999) Optimizing the operation of straight-grate iron-ore pellet induration systems using process models. Metallurgical and Materials Transactions B, 30 (4). pp. 803-813. ISSN 1073-5615 (Print), 1543-1916 (Online) (doi:10.1007/s11663-999-0042-5)

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Abstract

Mathematical models of straight-grate pellet induration processes have been developed and carefully validated by a number of workers over the past two decades. However, the subsequent exploitation of these models in process optimization is less clear, but obviously requires a sound understanding of how the key factors control the operation. In this article, we show how a thermokinetic model of pellet induration, validated against operating data from one of the Iron Ore Company of Canada (IOCC) lines in Canada, can be exploited in process optimization from the perspective of fuel efficiency, production rate, and product quality. Most existing processes are restricted in the options available for process optimization. Here, we review the role of each of the drying (D), preheating (PH), firing (F), after-firing (AF), and cooling (C) phases of the induration process. We then use the induration process model to evaluate whether the first drying zone is best to use on the up- or down-draft gas-flow stream, and we optimize the on-gas temperature profile in the hood of the PH, F, and AF zones, to reduce the burner fuel by at least 10 pct over the long term. Finally, we consider how efficient and flexible the process could be if some of the structural constraints were removed (i.e., addressed at the design stage). The analysis suggests it should be possible to reduce the burner fuel lead by 35 pct, easily increase production by 5+ pct, and improve pellet quality.

Item Type: Article
Uncontrolled Keywords: straight-grate iron-ore pellet induration systems, process models
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QD Chemistry
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis
School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group
Related URLs:
Last Modified: 14 Oct 2016 08:59
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/187

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