【作者】 应宇乾；

【导师】 陈金水； 卢建刚；

【作者基本信息】 浙江大学 ， 控制理论和控制工程， 2012， 硕士

【摘要】 烧结生产是高炉进料的一个重要预处理过程,能够最大程度地减小高炉原料的波动,为高炉的平稳生产提供可靠保障。随着铁矿石价格的攀升和全社会节能环保意识的增强,在钢铁工业中展开面向节能降耗的技术改进势在必行。作为影响高炉产量和质量的重要环节,烧结过程技术改进在整个炼铁过程节能降耗技术改进中必不可少。因此,烧结过程的研究具有重要理论价值和实际意义。本文研究了烧结点火过程的控制问题和烧结热状态过程的建模控制问题,采用PIDNN (Proportional-Integral-Derivative Neural Network)控制点火炉温度,提出建立时间序列数据研究单元思路,利用多模型模糊加权预测对烧结过程热状态进行建模,再采用模糊控制方法对烧结热状态进行控制。本文的主要工作和贡献如下：(1)采用PIDNN智能控制算法控制烧结点火炉炉温,在点火炉煤气负压和热值频繁波动的情况下,神经网络的自学习功能能够使控制系统能够实时地跟踪现场对象并加以合适的控制将被控参数准确地稳定在要求范围内。根据现场仪表的特性,将PIDNN控制方法和专家系统技术相结合,实现了对烧结点火炉温进行智能控制的目标,并且在烧结现场实施运行情况良好。(2)提出建立时间序列数据研究单元思路,采用数据驱动建模思想,针对烧结矿从配料混匀后铺在结台车上点火开始,到破碎冷却前的烧结矿热处理过程,按照台车行进方向进行分段设定研究单元,记录其全过程的数据,包括点火温度,料层厚度,所经过风箱的负压和温度。(3)提出多模型模糊加权预测方法,利用反向传播神经网络和广义回归神经网络独立对温度预测模型进行辨识,得到两组模型后分别针对同一组输入进行预测输出,将预测结果通过模糊加权得到新的预测输出,从而建立起烧结过程热状态的预测模型。(4)将烧结机理想工况下的运行数据进行记录,建立数据研究单元,利用多模型模糊加权预测方法建立理想工况下风箱温度预测模型预测风箱温度目标值,然后建立模糊规则,通过控制风箱负压对每个风箱温度进行独立模糊控制。更多还原

【Abstract】 Sintering is important for iron-making industry. As an essential pre-process of blast furnace materials, sintering could minimize the fluctuations of the materials, and thus guarantee the steady operation of furnace. With the rising price of iron ore and public enhancing consciousness of energy saving and environment protection, it is imperative to improve the technology of iron making in terms of saving energy and reducing cost. In this technical innovation, the sintering process has attracted more and more attention as it plays an important role. Therefore, it is meaningful to study the iron ore sintering process.The thesis focuses mainly on temperature control of sintering ignition oven and modeling of sintering heating process. We adopt PIDNN (Proportional-Integral-Derivative Neural Network) control algorithm to control ignition oven temperature, based on the idea of establishing time-series data unit, fulfill the modeling of thermal state of sintering process with the help of fuzzy weitghted multi-model and then implement the control of process by fuzzy control algorithm.In detail, the major contributions of this thesis are summarized as follows:(1) PIDNN control algorithm is proposed to control ignition oven temperature. Regardless of the frequent fluctuations of pressure and calorific value of gas, with the help of neural network’s self-learning behavior, the PIDNN control system turns out that the temperature fluctuates accurately within requirements.(2) An idea of establishing time-series data unit is employed. We divide the sintering process into data units using data-driven method, from the mixing of ingredients to the crushing of burned sinter. The sintering machine is divided into 16 data units. All the sintering parameters will be recorded for each data unit including ignition temperature, material thickness, pressure and temperature of box while each unit runs from start to end.(3) A fuzzy weighted multi-model method to build prediction model of sintering process is developed. We model the process by BPNN and GRNN respectively, and combine the results by fuzzy weighted method to build a new predict model.(4) We extract the recorded parameters of sintering process operated in ideal condition to build the ideal operation data set. With this data set we use fuzzy weighted multi-model algorithm to calculate the ideal setting of box temperature. After we get the goal setting of temperatures, we utilize fuzzy rules to control each box temperature by each box pressure.更多还原