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MODEL OPTIMIZATION OF HOT METAL DESULFURATION PROCESSES

机译:热金属脱硫过程的模型优化

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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.
机译:动态过程的实际优化,例如CaO-Mg基因试剂共注入热金属脱硫过程,是多重目标和约束的问题。本文在不确定性下涉及在线和离线热金属脱硫过程模型优化。在这种情况下,从单线过程模型优化采取的结果值可能不匹配目标值,但它可以用作匹配一些后续预期目标值的体验数据。因此,为了获得预期的目标值,在罐中或从锅盆脱硫过程中脱绕脱硫过程的设定值或预先指定的脱硫化过程。得到的热金属脱硫过程的优化模型应用于脱硫制作。 500脱硫制作的实际数据表明脱硫过程所提出的模型优化的效率。

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