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Operation of a 25 KWth Calcium Looping Pilot-plant with High Oxygen Concentrations in the Calciner
Published on: October 25, 2017
Field-Resolved Three-Phase Dephosphorisation in Molten Steel: Euler-Euler-DPM Modelling of Bottom-Blown
Hongyang Wang1,2, Wenxuan Mo1,3, Kai Dong2
1Beijing Zhiruzhenyi Superconducting Technology Co., Ltd., Beijing 102200, China.
Abstract:
Dephosphorisation in oxygen steelmaking depends on more than the equilibrium phosphorus partition ratio. It also depends on where gas, slag, metal and injected lime powder coexist while the bath is stirred. We develop a gas-slag-metal-particle reaction model for bottom-blown oxygen-CaO powder injection by coupling Euler-Euler transport of liquid steel, mixed slag, and gas with a discrete phase model (DPM) for CaO particles. The local source terms include oxygen dissolution, FeO/Fe2O3 conversion, CO/CO2 buffering, competitive C/Si/P oxidation, P2O5 formation, C2S-C3P fixation, reaction heat, and phase-wise mass conservation. Bubble swarms, dispersed slag, and emulsified metal-slag contact are represented through mean-field interfacial area densities tied to local phase fractions and mixing. Two simulated composition states have the same initial phosphorus content but different C, Si, and dissolved O levels; they are therefore compared as Case H and Case L rather than as a carbon-only test. Under the selected closures, Case H shows stronger decarburisation and CO-supported plume motion, whereas Case L retains more FeOx and dissolved oxygen near the slag-metal interface. In both states, calculated P removal is confined mainly to locations where FeOx supply, CaO availability, P2O5 generation, and C2S-C3P fixation overlap. These observations are conditional on the reported parameters, a production mesh accompanied only by a two-grid qualitative sensitivity check, one time step, and the early transient considered here. Quantitative validation, systematic grid/time-step studies, closure-sensitivity tests, and controlled-composition simulations are required before the framework is used for process prediction.

