Abstract
A real-life optimization of dynamic processes, such as Cao-Mg-base reagent co-injecting for hot metal desulfuration processes, is a matter of multiple objectives and constraints. This paper is concerned with on-line and off-line hot metal desulfuration process model optimization under uncertainty. In such cases a result value taken from a single on-line process model optimization may mismatch a target value, but it can be used as experience data to match some subsequent expected target values. Therefore, in order to obtain the expected target value, a set-value or a pre-specified input of desulfuration processes is updated off-line either within a pot or from pot-to-pot desulfuration processes. The resulting optimizing model of hot metal desulfuration processes is applied to desulfuration productions. 500 real-life data of desulfuration productions indicate the efficiency of the proposed model optimization for desulfuration processes.
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