【作者】 梅彬；

【导师】 曹长修；

【作者基本信息】 重庆大学 ， 控制理论与控制工程， 2002， 硕士

【摘要】 钒是一种具有战略价值的金属材料，在钢铁冶金、有色金属加工、电子和航空航天等方面有着广泛的应用。我国目前已掌握了提钒技术，是世界四大产钒国之一。转炉提钒是我国主要的生产方式。转炉提钒是一个多元多相的高温化学反应过程，反应机理复杂，影响因素众多，受原材料、工艺环境变化的影响较大，不便于人工控制。为提高提钒技术水平，改变生产过程中存在的指标波动大的缺点，需要对生产过程实现计算机控制，以减少提钒操作中人为因素的影响。论文从工程应用的角度出发，针对我国转炉提钒手工操作的现状，在开发提钒静态模型的过程中，以转炉提钒为特定研究对象，考虑了传统数学模型适应性差、难于移植的缺点，利用神经网络、事例推理等人工智能技术开发出具有较好自学习和自适应能力的静态模型。论文首先介绍了建立提钒静态模型的意义，讨论了国内外转炉模型的研究应用情况及发展趋势，深入分析了各种建模方法的优缺点。针对提钒过程的复杂性，提出转炉提钒建模应以神经网络为主要技术手段，结合其他人工智能技术，建立起比传统数学模型具有更高精度的模型。论文结合工艺原理和实际提钒过程的特点，找出提钒过程的内在影响因素和影响静态模型的主要因素。然后分别应用传统BP算法、全量法和增量法建立冷却剂子模型，对三者的拟合和预报精度作了深入分析，并结合统计学习理论针对传统的BP算法存在的问题提出了改进的算法。仿真结果表明该算法具有较快的收敛速度和较强的学习能力。本文同时应用事例推理技术建立枪位控制模型，并给出了具体的实现步骤。最后，本文使用面向对象的可视化Visual C++ 6 .0编程语言，开发出提钒静态控制模型软件。更多还原

【Abstract】 As a metallic material with strategy value ,vanadium have got extensive application in steel metallurgy 、metallic machining 、electron and aviation. Now our country has hold the devanadium technology and become one of the four greatest devanadium countries in the world. Oxygen converter devanadium now has become the main production method in our country. The process of devanadium is a high temperature chemical reaction process and the mechanism of reaction is very complex, the affect factor is also so many, and the influence by the variety of raw material and technics condition is also great, so it is very inconvenient to operate manually. In order to improve the level of devanadium technology and change the status that the target in production process is always variable, it is necessary to realization the computer control in production process and reduce the influence by the manual factor.On the process of developing the static devanadium model and thinking of the status that the devanadium process is still manual operation in our country, the oxygen converter devanadium is chosen as a specific research object from the point of view of industrial application in this paper. And on the basis of considering the demerits of the traditional mathematical model, the paper has developed a perfect self-study and self-adaptive static model by means of some artificial intelligence technology such as NN, CBR etc.Firstly this paper introduce the meaning of developing devanadium static model and discuss the application and development of the domestic and international research, and then thoroughly analyze the merits and demerits in these model-making methods. Aiming at the complexity of devanadium process, this paper put forward an advice that the more precise model should be made by means of the NN and other AI technology.On the basis of combining the characteristic of the technics theory and practical devanadium process, this paper conclude the internal affect factor of devanadium process and the main factor of static model. Then the paper develop a refrigerant model by means of traditional BP algorithm、statistics theory 、experience theory and thoroughly analyze the combination of these three methods and their predict precision .And then the paper put forward an advanced algorithm on the basis of combining the statistics theory and the problems on the traditional BP algorithm .The imitate results show that the algorithm has an ability of fast convergence speed and<WP=6>strong study power. At the same time this paper discuss some technical problem about how to use the CBR technology to establish the spear position control model.At last, this paper develop a software for devanadium static control model by means of object-oriented programming language Visual C++ 6.0.更多还原