【作者】 涂春林；

【导师】 毕学工；

【作者基本信息】 武汉科技大学 ， 钢铁冶金， 2004， 硕士

【摘要】 煤气流的分布关系到炉内温度分布、软熔带结构、炉况顺行和煤气的利用状况，最终影响到高炉冶炼指标。高炉操作也主要是围绕获得合理、适宜的煤气流分布来进行的，另一方面，煤气流分布也是高炉操作者判断炉况的重要依据。 由于高炉是个封闭式系统，炉内煤气流的变化是不可见的，只有通过传感器数据对炉况进行判断。传统的判断方法是基于纯机理性的数学模型来研究高炉内煤气流分布，但这种方法通常过于复杂，受在线实时的制约，难以进行实时在线分析和控制，因此不能及时反馈信息，调整布料制度。人工神经网络模型具有很强的容错性、学习性、自适应性和非线性的映射能力，特别适合于解决因果关系复杂的非确定性推理、判断、识别和分类等问题，它由网络拓扑结构、神经元特性函数和学习方法确定。高炉生产过程是大型的分布参数系统，可用大量传感器得到其高度和半径方向上的各种检测值，然后用人工神经网络来识别它们的特征分布模式，进行炉况诊断和控制。 鉴于实际中煤气流分布并没有固定的分布模式，本文应用了一种自组织神经网络来进行煤气流模式识别。自组织神经网络是一类无导师学习网络，它可以自动地向环境学习，可以对任意多和任意复杂的二维模式进行自组织、自稳定和大规模并行处理，在无监督的情况下从输入数据中找出有意义的规律来。我们应用这种网络方法从我国宝钢1号高炉大量十字测温历史数据中自动整理出5×5=25种分布模式来，这25种分布模式将映射在一个二维网络图上，并且相近的模式在图上的坐标位置也是靠近的，即该方法有归类的作用。借助于该模型，高炉操作者将可更直接更方便地判断煤气流的分布情况，从而更好地指导高炉操作。该模型方便地表达和描述了实际气流分布状况，并方便了建立十字测温数据与布料模式及料面形状、矿焦比等之间的关系。该模型是宝钢1号高炉总布料推定模型的一个子模型，已在线运行。更多还原

【Abstract】 Gas flow distribution is correlative with the temperature distribution in blast furnace, the shape of the cohesive zone, the smooth state of blast furnace and using status of gas flow , finally influence the smelting index of blast furnace. The target of blast furnace operation mainly is to achieve proper and optimum gas flow distribution. At the same time, the gas flow distribution also is the important foundation for blast furnace operators to estimate the state of blast furnace.The blast furnace is a closed system, and the change of gas flow distribution is invisible, so the only method of estimate the status of blast furnace is to utilize records of all kinds of sensors. The conventional ways of identifying gas flow distribution is mathematic model based absolute mechanism. But this method is too complex , be subjected to on line operation and is difficult to on line analyzing and control , so can not to feedback information in time and to adjust burden rule. The artificial neural network model has strong fault - tolerant performance, learning performance, self-adaptive performance and non - linearity map ability, and it is adaptive to solve some problems like non - determinacy inference of complex causal relation , judgment , recognition , classification and so on. The artificial neural network model is defined by network topological structure, neuron characteristic function and learning method. The production process of blast furnace is a large - scale distribution parameter system. We can obtain all kinds of detection value in altitude - direction and radius - direction by many sensors. After that, to recognize their character distribution patterns by artificial neural network, and using these to diagnose and control the condition of blast furnace.In fact, gas flow has not immovable distribution patterns, so this paper recognize gas flow distribution pattern by a self-organization neural network. The self organization neural network is a kind of unsupervised learning network, it can learns automatically from surrounding, can self -organize, self - stabilize and large-scale parallel - process random multi and complex two - dimensional patterns, and find some significative rules from inputted data in the condition of unsupervised data. We arranged 25 gas flow distribution patterns (5X5) from multiple production data of the Ne 1 blast furnace of Bao Steel, the 25 patterns are mapped on a two - dimensional net diagram , but also the coordinates of those similar patterns is also closer in the diagram, namely, the method has classified function. Using this model, the operators can recognize expediently gas flow’distribution. The model describes expediently actual gas flow distribution, and establishes expediently the relation between temperature data, burden mode, charge shape, the ore - to - coke ratio, and so on. The model is a sub - model of distribution model of Ne 1 blast furnace of Bao Steel, and is working on line.更多还原