【作者】 向齐良；

【导师】 吴敏；

【作者基本信息】 中南大学 ， 控制理论与控制工程， 2008， 博士

【摘要】 铁矿石烧结过程是钢铁生产的重要环节,烧结矿是高炉炼铁的主要原料,烧结矿质量的优劣直接影响到炼铁生产的产量、质量及能源消耗。目前,在我国铁矿石烧结生产过程中,控制技术落后、自动化水平较低的问题已经成为制约烧结矿产量、质量的一个瓶颈。烧结过程是一个具有强非线性、强耦合性、不确定性、时变、时滞的复杂工业过程,采用传统的控制技术难以满足生产要求。本文综合运用机理分析、先进控制理论、人工智能技术等多学科知识,对烧结过程及其控制的方法和技术进行了深入研究,提出一种基于烧结终点预测的烧结过程智能控制策略,建立了相应的智能控制系统,为有效解决铁矿石烧结过程的控制问题提供了新的途径。论文的主要研究内容和贡献如下:(1)基于烧结终点预测的智能控制系统结构针对烧结过程特性和目前烧结生产过程中存在的主要控制问题,综合运用神经网络、模糊控制和专家控制等多种智能控制方法,提出了一种基于烧结终点预测的智能控制系统结构,为解决烧结过程控制问题提供了一种有效的解决方案。(2)烧结终点预测模型针对烧结过程的大滞后性、非线性以及参数信息的不完整性,结合灰色理论和改进的神经网络方法,建立了烧结终点灰色BP神经网络预测模型,在烧结工况稳定时能有效预测烧结终点;针对烧结过程工况不稳定时该模型预测精度不高的缺陷,建立基于主元分析的预测模型,对灰色BP神经网络模型进行修正,实验及应用结果表明其具有较高的预测精度。(3)烧结终点混杂模糊—预测控制模型针对烧结过程的混杂特性,综合运用模糊控制、预测控制等智能化控制方法,建立了烧结终点混杂模糊—预测控制模型。在稳态时,采用模糊控制为主的控制策略;在非稳态时,采用预测控制为主的控制策略。同时,通过研究多模型柔性切换控制技术实现两模型的协同工作,解决了烧结终点的优化控制问题。(4)基于满意度的智能协调优化方法针对台车速度对混合料料槽料位的持续性影响,在建立混合料料槽料位专家控制模型的基础上,采用基于满意度的烧结过程智能协调优化方法,综合协调控制烧结终点和混合料料槽料位,以保证料槽料位处于安全状态的同时,减小烧结终点的波动,从而实现了整个烧结过程的优化控制。(5)烧结过程智能控制系统基于SIMATIC PCS 7集散控制系统,采用一种多层分布式软件体系结构设计了烧结过程智能控制系统的整体框架;利用OPC通信技术实现了应用软件和基础自动化系统的通信;运用VC++6.0完成了系统功能模块的设计,并且实现了烧结终点软测量模型、预测模型、混杂模糊一预测控制以及协调优化等智能优化控制策略。实际工业应用效果验证了该系统的可行性和有效性。通过应用基于烧结终点预测的烧结过程智能控制策略,提高了铁矿石烧结工艺过程优化控制水平,有效地抑制了烧结终点的波动,产量质量得到了提高,降低了工人的劳动强度,取得了显著的经济效益和社会效益。同时也为复杂工业过程优化控制提供一种实用的、值得借鉴的工业化实现方法。更多还原

【Abstract】 Iron ore sintering process is an important step in iron and steel production. As the main raw material of the blast furnace, the sinter quality has a direct influence on the yield and quality of iron and the energy consumption. At present, the backward control technology and low-level automatization of iron ore sintering process have become the bottleneck to restrict the yield and quality of the sinters in China.Since iron ore sintering is a complex industrial process with strong nonlinearity and coupling, uncertainty, time variation, and time-delay, it is difficult to meet the production requirements by using the traditional control techniques. Thus, this dissertation makes a deep research on sintering process and the related control methods and techniques by comprehensive application of multi-subject knowledge, such as the mechanism analysis, advanced control theory and artificial intelligence technique, etc. An intelligent control strategy based on the prediction for the Burning Through Point (BTP) is proposed in this dissertation, and a corresponding intelligent control system is also established, which provides a new effective approach to solve the control problems in iron ore sintering process. The main study contents and achievements include:(1) Structure of intelligent control system based on BTP predictionView of the characteristics and the main existing control problems in the sintering process, a structure of intelligent control system based on BTP prediction is presented by integrating various intelligent control methods, such as neural networks, fuzzy control and expert control. The designed control structure provides an effective solution to the control problems in sintering process.(2) Prediction model of BTPDue to the characteristics of long time-delay, nonlinearity and incompletion of parameter information in sintering process, the gray BP neural networks prediction model of BTP is established by using the grey theory and improved neural networks combinatively. When the sintering operating condition is stable, this model can predict the BTP effectively. In order to solve the insufficiency of downtrend of predictive precision when the state of sintering process is unstable, the prediction model based on PCA has been put forward to modify the gray BP neural networks prediction model. The results of simulations and actual applications show that the prediction model of BTP has high prediction precision.(3) Hybrid fuzzy-predictive control model of BTPConsidering the hybrid characters of sintering process, the hybrid fuzzy-predictive control model of BTP is established based on intelligent control methods of fuzzy control and predictive control. While BTP is in a steady state, the fuzzy control model is mainly used in the system. Otherwise, the predictive control model is mainly used. In addition, multi-model flexible switching control technique is applied to realize the smoothly switching between the two models, which solves the optimal control problem of BTP in sintering process.(4) Intelligent coordination and optimization method based on Satisfactory Solution Principle (SSP)Mainly aimed at the problem that the pallet velocity directly affects the sinter bed position continuously, the sinter bed position expert control model is set up. Then, an intelligent coordination and optimization method based on SSP is proposed to synthetically coordinate and control the BTP and the sinter bed position. The proposed method ensures the normal state of sinter bed and reduces the fluctuation of BTP, so as to realize the optimization control of the overall sintering process.(5) Intelligent control system for sintering processBased on SIMATIC PCS 7 distributed control system, the intelligent control system frame is designed by using the multi-layer distributed software architecture. The communication between the application software and basic automation system is realized with OPC. By using VC++ 6.0, the system function modules are designed, moreover, the BTP soft-sensing model, BTP prediction model and intelligent optimization and control strategies including the hybrid fuzzy-predictive control of BTP, coordination of BTP are realized. The feasibility and effectiveness of the designed system are verified by the practical control results.The application of intelligent control strategies based on the prediction for the BTP to iron ore sintering process, improves the optimizaiotn control level of the iron ore sintering process, restrains the fluctuation of BTP effectively, and improves the yield and quality of the sinters. Moreover, it reduces the labor intensity of workers, and achieves remarkable economic benefits and social benefits. Meanwhile, a set of practical and recommendable industrialized methods is supplied for the optimization control of complex industrial processes.更多还原