【作者】 吴晓峰；

【导师】 费敏锐；

【作者基本信息】 上海大学 ， 控制理论与控制工程， 2009， 博士

【摘要】 在烧结生产中,烧结终点是与烧结机质量、产量和成本密切相关的重要工艺参数,是保证高炉获得优质技术指标的关键所在。烧结终点位置适宜且保持稳定,可以提高成品率并充分利用烧结面积,在保证质量的同时得到最大生产率,降低能耗。但由于烧结生产具有较大的时滞性和动态时变性,以及用于判断和预报终点的参数无法直接检测等原因,因此,长期以来烧结终点控制一直是钢铁企业自动化过程控制中的难点。本文结合莱钢265m2烧结机的具体工艺生产特点,紧紧围绕预报和稳定控制烧结终点,以提高烧结质量这一主题,在烧结终点预报模型、烧结终点控制方法和系统设计、机尾断面红外图像在线判断烧结终点、优化控制烧结终点等方面展开研究,取得了如下创新性的研究成果:(1)在烧结终点预报方面,基于支持向量机理论(SVMs)提出了一种预报烧结终点的新方法。该方法以原料参数作为预报参数,采用支持向量机方法对莱钢265m2烧结机烧结过程进行预报,建立了烧结终点的长期预报模型。通过与BP神经网络建立的预报模型进行比较表明,支持向量机良好的泛化性能更适合于动态复杂的烧结过程。本文提出的预报模型有较强的泛化能力,能够准确的预报烧结终点。(2)在烧结终点控制方面,提出烧结终点模糊控制策略,将烧结终点短期预报模型应用于烧结终点模糊控制系统中。以正常拐点风箱的废气温度(BRT)和风箱废气温度曲线拐点(BRP)两种预报参数分别实现烧结终点短期预报。设计模糊控制策略控制烧结终点,并建立烧结终点控制系统验证其有效性。针对烧结过程非线性特性,提出基于最小二乘支持向量机(LS-SVMs)建模的预测控制算法。首先采用LS-SVMs离线建立了烧结过程的非线性模型;然后在系统运行过程中,将离线模型在每一个采样周期关于当前采样点进行线性化,采用广义预测控制算法(GPC)进行控制器设计,通过调节台车速度控制烧结风箱废气温度,从而实现烧结终点的间接控制。通过Simulink建立烧结过程预测控制系统,验证其控制效果。从实验结果可看出,设计的烧结过程的预测控制系统具有良好的控制效果,同时系统易于实现,对进一步的实际应用有参考指导意义。(3)在烧结质量判断方面,设计红外CCD装置采集烧结机尾断面图像,提取其特征信息,成功地建立支持向量机多分类器判断烧结状况(欠烧、过烧、正常),进而建立烧结矿质量预报模型,实现对烧结矿生产在线实时的综合评价和烧结终点的有效控制。(4)在烧结过程参数优化模型与控制方面,针对烧结过程复杂的优化控制问题,建立了烧结过程参数优化模型,采用聚类的匹配优化算法获得最优的操作参数,对整个烧结过程控制(配料控制、混合料水分控制、点火炉燃烧控制、烧结过程温度控制)进行操作优化指导。优化后的参数改善了现场的控制效果,提高了生产效率,并且减少了能源消耗。更多还原

【Abstract】 In the sintering production, Burning Through Point (BTP) is a very important technical parameter related to sinter quality, quantity and cost, and which is the key point for blast furnace to get high grade technical target. The right position and keeping stabilization of BTP can improve product ratio and use sinter area completely, and maximizes the sinter production rate, and decrease the energy cost. But because of the long-time delays and dynamic complexity of sintering process and the parameters for judge and analyze the BTP can not be measured directly, it is difficult to solve the BTP control problem. Therefore, this problem has been considered as a key difficulty of steel enterprise automation in a very long time.According to the particular sintering production of Laiwu Iron and Steel Corporation, the dissertation pays more attention to the prediction and control of BTP for the improvement of sintering quality. The following aspects are investigated in the dissertation: the BTP prediction model and control method, on-line inference the sintering quality based on cross-section infrared thermal imaging of discharge end, optimization and control in the sintering process. These researches earn theoretical significance and practical value. The main creative works of the dissertation are as follows:(1) In the BTP prediction, a new prediction method based on support vector machines (SVMs) is presented for BTP. The long-time prediction model of BTP is constructed for Laigang No.2 265m2 Sinter machine. The results indicate SVMs outperform the three-layer Backpropagation (BP) neural network in predicting BTP with better generalization performance, and are satisfactory.(2) In the BTP control, the fuzzy control strategy is investigated for BTP. The short-time prediction model is applied to the BTP fuzzy control system. The BTP short-time prediction model is constructed via burn rising temperature(BRT) and burn rising position(BRP). The application results have verified the effectiveness of the control system.A predictive control algorithm based on least squares support vector machines (LS-SVMs) model for the sintering process with strong nonlinearity is presented. The nonlinear offline model is built via LS-SVMs. In the process of system operation, the offline model is linearized at each sampling instant, and the generalized predictive control (GPC) algorithm is employed to the controller design. The GPC controller is designed to adjust strand speed for sintering temperature control. The performance is demonstrated by several simulation results. The results show the effectiveness of the presented algorithm.(3) In the sintering quality, the reasonable feature parameters are obtained by processing the cross-section infrared thermal imaging of discharge end. The multi-class classifier based on SVMs is constructed to on-line inference the sintering quality (undersintering, oversintering and normal).(4) In the sintering process optimization and control, the parameters optimization model is constructed to guide the sintering process control. The optimum parameters are obtained by using matching optimization algorithm based on clustering method. The sintering production quality is improved obviously and the energy consumption is decreased.更多还原