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Mathematical modelling tools for optimisation of direct smelting processes

Mathematical modelling tools for optimisation of direct smelting processes

Davis, Mark, Pericleous, Koulis ORCID: 0000-0002-7426-9999, Cross, Mark and Schwartz, Philip (1998) Mathematical modelling tools for optimisation of direct smelting processes. Applied Mathematical Modelling, 22 (11). pp. 921-940. ISSN 0307-904X (doi:10.1016/S0307-904X(98)10037-9)

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Direct smelting operations involve the strong interaction of a wide range of complex physico-chemical processes. Moreover, for such processes to be efficient, these interactions have to be optimised to yield the desired set of chemical reactions and exchanges of heat and mass amongst the variety of gaseous, liquid and solid phases. This paper focuses upon a consideration of Computational Fluid Dynamics (CFD) based models developed to represent the HIsmeltĀ® direct smelting process. The models are extremely sophisticated and at every stage have challenged the limits of CFD technology, as well as the adequacy of constitutive sub-models to represent the chemical reaction/combustion phenomena. From an unprecedented synergy amongst process metallurgists, experimental scientists and CFD modellers, sophisticated, comprehensive and well-validated models of the process have evolved. The paper highlights some of the key state-of-the-art CFD techniques developed for the models and the role of specially designed experiments in parameter estimation and validation against plant measurements.

Item Type: Article
Uncontrolled Keywords: drect smelting, numerical simulation, multi-phase, post combustion
Subjects: Q Science > QA Mathematics
Q Science > QD Chemistry
T Technology > TN Mining engineering. Metallurgy
Pre-2014 Departments: School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group
School of Computing & Mathematical Sciences
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Last Modified: 14 Oct 2016 09:15
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None

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