A MULTILEVEL RESOURCE-SAVING BLAST FURNACE PROCESS CONTROL

T. Barbasova
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引用次数: 1

Abstract

A multilevel resource-saving blast furnace process control is considered. The resource-saving control is provided for operating, adaptation, technical and economic control in the automated systems of blast-furnace processes. It is proposed to form optimal operation modes of blast furnace heating, metal charge structures, natural gas and oxygen consumption. Decisions are made using Kohonen neural networks taking into account current and planned parameters of coke quality, iron ore, raw materials and blast. At the level of operating control, the work suggests a model predictive control to improve the resource conservation indicators. The method is based on decomposition of the general problem of the process dynamics identification on particular problems: dynamic synchronization and identification of process transfer functions. At the level of adaptive control, optimal operating modes of blast furnaces are expedient to be developed with respect to blast furnace heating, structure of metal charge, natural gas and oxygen rate considering the current and planned parameters of coke, blasting. The blast furnace operating modes are suggested to be determined based on Kohonen neural networks. In evaluating the efficiency of introducing the model predictive control, the existing actual statistics of scatter of BF mode parameters should be based upon. The fact is that the introduction of model predictive control assumes no radical change of the BF melt technology. Like in all the control systems, the BF process is considered as the set control object with all its characteristics. Changing process settings, raw material content does not introduce any cardinal variation in the scatter of process characteristics. However, in this case a transient process occurs which is necessary for the control system to identify the changing conditions. The transient process is inherent to all the control systems and the blast furnace process is not an exclusion. As a result of transient process, the control system is set to the optimal mode.
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多级资源节约型高炉过程控制
考虑多级资源节约型高炉过程控制。为高炉工艺自动化系统的运行、调试和技术经济控制提供了资源节约型控制。提出形成高炉加热、金属炉料结构、天然气和氧气消耗的优化运行模式。利用Kohonen神经网络综合焦炭质量、铁矿石、原料和高炉等现有参数和计划参数进行决策。在运行控制层面,提出了模型预测控制,以提高资源节约指标。该方法是将过程动力学辨识的一般问题分解为具体问题:动态同步和过程传递函数辨识。在自适应控制层面,考虑焦炭、爆破的现有参数和规划参数,适宜从高炉加热、金属炉料结构、天然气和含氧量等方面制定高炉的最优运行模式。提出了基于Kohonen神经网络确定高炉运行模式的方法。在评价引入模型预测控制的有效性时,应以已有的BF模式参数散射的实际统计量为依据。事实上,模型预测控制的引入并没有彻底改变高炉熔体技术。与所有控制系统一样,高炉过程被视为具有其全部特性的设定控制对象。改变工艺设置,原料含量不会导致工艺特性分散的任何主要变化。然而,在这种情况下,发生了一个瞬态过程,这是控制系统识别变化条件所必需的。瞬态过程是所有控制系统所固有的,高炉过程也不例外。由于暂态过程的存在,控制系统被设定为最优模式。
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