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Modeling and optimization of slopping prevention and batch time reducation in basic oxygen steelmaking

机译：碱性氧气炼钢中防止喷溅和间歇时间的建模与优化

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摘要

Because of increasingly stricter environmental regulations, steel plants are at-udtempting to reduce the occurrence of (heavy) slopping, which can be accompa-udnied by large ejections of dust. They are also aiming to increase their produc-udtion capacity by e.g. investments in additional equipment and by improvingudlogistics. Reduction of the batch time in basic oxygen steelmaking might con-udtribute to the desired increase in production capacity if the converters are theudbottleneck in production.udCurrently the desired temperature and steel composition are met by appli-udcation of a ¯rst principles static model, which determines the required rawudmaterial input. This model is sometimes perceived as complicated. The set-udpoints of the control variables such as the addition rates, the lance height andudthe oxygen blowing rate are based on standard operating procedures, whichudhave been developed during many years of practical experience. Operatorsudonly deviate from these standard operating procedures when it is necessary,udfor instance, when slopping occurs. It may be expected, that both the batchudtime and the occurrence of slopping can signi¯cantly be reduced by optimizingudoperating settings.udThe objective of this thesis is to develop a dynamic control strategy for basicudoxygen steelmaking which both reduces the occurrence of slopping and in-udcreases the production capacity by reducing the batch time. The developmentudof this strategy would greatly bene¯t from the continuous measurement of im-udportant process variables. However, due to the high temperatures and dustyudenvironment involved, measuring of important process variables is di±cult. Itudis therefore necessary to develop a dynamic process model that predicts im-udportant process variables. Dynamic modeling of the process enables dynamic optimization. The feasibility of measurements, modeling of the process anduddynamic optimization are studied subsequently in this thesis.
机译：由于日益严格的环境法规，钢铁厂正在努力减少（大量）倾斜的发生，这种倾斜可能伴随着大量的灰尘喷射而出现。他们还旨在将生产能力提高例如。通过改进 udistics在附加设备上进行投资。如果转炉是生产中的瓶颈，则基本氧气炼钢中批量时间的减少可能有助于所需的生产能力的提高。目前，通过应用第一种方法可以满足所需的温度和钢成分。原理静态模型，它确定所需的原材料原材料输入。这种模型有时被认为是复杂的。控制变量的设定值，例如添加速率，喷枪高度和氧气吹入速率，是基于多年的实践经验开发的标准操作程序。如果有必要，例如在发生倾斜时，操作员仅会偏离这些标准操作程序。可以预期，通过优化过穿孔设置，可以显着减少批处理停工时间和喷溅的发生。通过减少批处理时间，可以增加产量并增加产能。这种策略的发展将极大地受益于对重要过程变量的连续测量。但是，由于所涉及的高温和多尘环境，很难对重要的过程变量进行测量。因此，有必要开发一个动态过程模型来预测不重要的过程变量。过程的动态建模可实现动态优化。随后研究了测量的可行性，过程建模和动态优化。

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    Kattenbelt Carolien;

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  • 年度 2008
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  • 正文语种 {"code":"en","name":"English","id":9}
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