【作者】 李启会；

【导师】 刘祥官；

【作者基本信息】 浙江大学 ， 运筹学与控制论， 2005， 博士

【摘要】 高炉冶炼过程是一个高度复杂的过程，其运行机制往往具有非线性、大时滞、大噪声、分布参数等特征。同时，高炉本身是一个集传热与化学反应耦合的开放体。在智能控制论范畴内解析高炉冶炼过程，并最终实现高炉冶炼过程的智能控制，是冶金科技发展的前沿课题。 本文选取具有代表性的2座中型高炉(莱钢1号750m~3高炉和临钢6号380m~3高炉)为研究对象。 论文前3章是在炼铁专家知识和模糊数学理论的基础上，从模糊数学角度详细分析了高炉冶炼过程的复杂性，并且由此得出结论：高炉冶炼过程不仅存在着许多非常规问题(如，炉况预报，异常炉况诊断、高炉设备诊断等)，而且在对其进行分析判断的过程(炉温[Si]辨识、预测及控制等)中存在着大量的模糊信息。这些非常规问题和模糊信息无法用简单的方式作出确切的描述，而必须采用模糊数学中的隶属函数对其中的不确定知识进行量化处理。文中对许多变量建立了隶属函数，采用了多种形式的隶属函数方法，例如，利用3维隶属函数法确定了铁水温度及其可信系数间的相互关系。3维隶属函数法有助于减少规则数目；利用非参数相似度法对[Si]进行模糊聚类分析，建立相应的模糊相似关系矩阵，并用模糊熵评价聚类效果。 随着智能控制理论研究的不断深入，建立功能更强的异常炉况预报与诊断模型来改善和提高模型的预测准确度和诊断效果，成为新的期望目标。本文第4章从炼铁工艺实际数据出发，深入分析了影响炉况的几个关键参数，按照这些参数的不同波动情况进行了定量的模糊分类。在此基础上，建立了异常炉况模糊预报模型和异常炉况模糊诊断模型。这2个模型不仅可以对中间过程进行逻辑推理，而且能定量计算高炉异常炉况的符合程度以及发展趋势。模拟结果表明，异常炉况模糊预报和诊断模型具有较好的可操作性。 高炉炼铁过程的炉温预测与控制的数学模型研究，既是炼铁自动化中的难题，也是实际炼铁生产中工长和厂长都十分关注的重要课题。炉温的准确预报，将有助于工长提高操作水平，从而达到提高利用系数和降低焦比的目的。论文第5章结合莱钢1号高炉和临钢6号在线采集的生产数据，针对高炉冶炼过程存在的时滞，应用广义函数分析法，计算了主要检测参数的时滞。基于模糊系统和神经网络的逼近特性，将模糊数学理论、随机系统理论以及神经网络方法结合在一起，建立了一整套的高炉炉温[Si]的智能预报模型，并进行横向和纵向的对比研究。仿真结果表明，该套模型不仅能够很好的预测炉温[Si]，而且能指导高炉工长调控炉温，使之处于最佳状态。 高炉冶炼过程控制是高炉主控室指挥下的多目标、多工序的复合控制。炉温控制是高炉冶炼过程控制的核心和基础。“炉温控制”不同于传统意义上的“温度控制”，其区别在于：“炉温控制”不仅与提供的能量有关，而且与物理化学反应过程、流体相态有关。论文第6章在逆系统理论的基础上，将喷煤量作为主控量，建立高炉炉温[Si]模糊预测控制模型和炉温[Si]的聚类模糊控制模型，在模糊控制论范畴内，探索高炉炉温[Si]控制规律。更多还原

【Abstract】 Blast Furnace (BF) ironmaking process is highly complicated, whose operating mechanism is characteristic of nonlinearity, time lag, high dimension, big noise and distribution parameter etc. What’s more, BF is an open system with heat transport and chemical reaction coupling. Study on BF ironmaking process with intelligent cybernetics and realization of intelligent control to it are the frontier in the field of metallurgic development.In present dissertation, No.1 BF (750m~3) at Laiwu Iron and Steel Group Co. and No.6 BF(380m~3) at Linfen Iron and Steel Co., which are the representation of medium-sized BF in China, were selected out as the studying objective.The complexity of BF ironmaking process was analyzed in detail in the first three chapters of this article, based on Fuzzy Mathematics and knowledge of ironmaking experts. The following conclusions were drawn: Not only many unconventionality problems (including prediction and diagnosis of abnormal BF state, diagnosis of BF equipment etc) exist in BF ironmaking process, but much fuzzy information is encountered in analysis BF ironmaking process (for example, identification, prediction and control of [Si]), which makes Simple-inference disabled, Membership Functions in Fuzzy Mathematics must be used to solved those problems. Many kinds of Membership Functions were constructed, for example, the relationship between hot metal temperature and it’s confidence coefficients was confirmed with the method of three-dimensional membership function, and the amount of rules can be reduced through three-dimensional membership function. Nonparametric similar degree method was used in the fuzzy cluster of [Si], and the fuzzy similarity relation matrix of [Si] was presented, with the fuzzy entropy as the evaluation criterion.With the development of intelligent cybernetics, new and more efficient prediction and diagnosis models of abnormal BF state should be established on intelligent cybernetics, and it is the object of ironmaking process. Those models were presented in the fourth chapter of this paper. Some key parameters of BF state were analyzed in detail, based on fuzzy mathematics. And then, Fuzzy prediction model and fuzzy neural net diagnostic model of BF state were presented. Not only the internal logical-inference, but quantitative calculation of conformity and variation trend of BF state was given with those two fuzzy models, and they are operational models.Study on prediction and control models of temperature in BF is the most difficult problem in automation of BF ironmaking process and practical production. The exact更多还原