一类高炉炉温不确定动态矩阵预测控制方法研究
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摘要
高炉是一个多变量、大滞后、非线性、大噪声的系统,影响高炉运行的因素非常多,测量困难。而保持良好合理的炉温是高炉生产稳定运行的关键因素之一,是实现高炉长寿、高产、优质、低耗的直接保证。炉温过高会使焦比升高并使铁产量降低,并且导致发生炉况故障;炉温过低会使炉内反应热量不足甚至导致高炉事故发生。因此,如何正确选择炉温并维持炉温的稳定对于高炉炼铁生产具有重要意义,如何对高炉炉温进行预测控制成为高炉过程控制的核心。
本文在查阅了大量国内外相关文献的基础上,首先介绍了高炉炉温控制的发展,接着对高炉炉温控制进行深入分析以及对动态矩阵算法和阶梯式控制的研究,同时充分考虑高炉炼铁过程中不确定性因素的影响,提出了针对一类高炉炉温的不确定阶梯式动态矩阵预测控制算法。
本文算法继承了传统动态矩阵预测控制算法的特点:建立以喷煤量、鼓风量和冷却水水流量为控制输入量,温度为输出量的预测模型,并利用预测模型得到系统未来的预测输出。然后,比较观测值和预测模型输出值之间的误差进行反馈校正,再与参考温度值进行比较,得出最优控制律,进行滚动优化。同时,本文算法在高炉炉温控制系统中引入阶梯式控制来求解出最优控制量,避免动态矩阵预测控制算法中复杂的矩阵运算。最后,由于高炉炼铁过程中的不确定性因素的影响的存在,为了保证在求解最优控制律时有解,引入容许控制集,并在容许控制集内,调节不确定性参数,使得温度预测输出值的误差范围在±5°C内。通过仿真研究,验证了本文采用的算法能对当前炉温的发展做出正确的预测,控制稳定性好,是一种提高高炉炉温控制精度的有效方法。另外,还介绍了高炉炉温控制系统的人机交互界面及其实现的功能。………….
Blast furnace is a multi-variable, large time delay, nonlinear and large noise system. The factors that affect the operation of blast furnace are too many, and the measurement is difficulty. Maintain a good and reasonable furnace temperature is a key factor for stable operation of blast furnace production, and is the direct assurance for blast furnace to achieve long life, high yield, high-quality, low consumption. The high furnace temperature will increase the coke, reduce the iron production and lead to occurrence of faults in blast furnace. The low furnace temperature will make blast furnace lack of reaction heat and even lead to blast furnace accident. Therefore, how to choose furnace temperature correctly and maintain furnace temperature stability has great significance to the iron making of blast furnace. How to predict and control the blast furnace temperature has become the core of blast furnace process control.
In this article, on the basis of consulting a large number of domestic and international relevant documents, the development of the blast furnace temperature controlling is proposed firstly. Then through analyzing the blast furnace temperature controlling in depth and investigating dynamic matrix control algorithm and stair-like control, at the same time, fully considering the impaction of uncertainties in the process of blast furnace ironmaking, one kind of uncertain stair-like dynamic matrix predictive control algorithm for a class of tempreture in the blast furnace is proposed.
The algorithm inherited the advantages of traditional DMC algorithm. The prediction model is established in case that the coal rate, blast volume and flow of cooling water are the control inputs, and the temperature is control output. The system's future output is gained using the prediction model. Carry out feedback correction by compared with errors between the observed values and the output values of prediction model. Then compared with the reference temperature to obtain optimal control rate and rolling optimization. At the same time, this algorithm, in the blast furnace temperature control system, introduced a stair-like control to solve the volume of optimal control and avoid the complex matrix operations in DMC algorithm. Finally, as the impaction of uncertainties exists in the process of blast furnace ironmaking, in order to ensure that the optimal control rate is solvable, admissible control set is introduced. Adjust the uncertainty parameters in admissible control set to make the temperature’s predicted output value within the error range of±5°C . The result of simulating test shows that the predictive control algorithm can make a correct prediction of the current temperature, and has the advantages of good stability, which implies that this control approach is effective for improving the accuracy of the blast furnace temperature control. In addition, the man-machine interface of blast furnace temperature control system and its functions are introduced in this article.…………….
本文在查阅了大量国内外相关文献的基础上,首先介绍了高炉炉温控制的发展,接着对高炉炉温控制进行深入分析以及对动态矩阵算法和阶梯式控制的研究,同时充分考虑高炉炼铁过程中不确定性因素的影响,提出了针对一类高炉炉温的不确定阶梯式动态矩阵预测控制算法。
本文算法继承了传统动态矩阵预测控制算法的特点:建立以喷煤量、鼓风量和冷却水水流量为控制输入量,温度为输出量的预测模型,并利用预测模型得到系统未来的预测输出。然后,比较观测值和预测模型输出值之间的误差进行反馈校正,再与参考温度值进行比较,得出最优控制律,进行滚动优化。同时,本文算法在高炉炉温控制系统中引入阶梯式控制来求解出最优控制量,避免动态矩阵预测控制算法中复杂的矩阵运算。最后,由于高炉炼铁过程中的不确定性因素的影响的存在,为了保证在求解最优控制律时有解,引入容许控制集,并在容许控制集内,调节不确定性参数,使得温度预测输出值的误差范围在±5°C内。通过仿真研究,验证了本文采用的算法能对当前炉温的发展做出正确的预测,控制稳定性好,是一种提高高炉炉温控制精度的有效方法。另外,还介绍了高炉炉温控制系统的人机交互界面及其实现的功能。………….
Blast furnace is a multi-variable, large time delay, nonlinear and large noise system. The factors that affect the operation of blast furnace are too many, and the measurement is difficulty. Maintain a good and reasonable furnace temperature is a key factor for stable operation of blast furnace production, and is the direct assurance for blast furnace to achieve long life, high yield, high-quality, low consumption. The high furnace temperature will increase the coke, reduce the iron production and lead to occurrence of faults in blast furnace. The low furnace temperature will make blast furnace lack of reaction heat and even lead to blast furnace accident. Therefore, how to choose furnace temperature correctly and maintain furnace temperature stability has great significance to the iron making of blast furnace. How to predict and control the blast furnace temperature has become the core of blast furnace process control.
In this article, on the basis of consulting a large number of domestic and international relevant documents, the development of the blast furnace temperature controlling is proposed firstly. Then through analyzing the blast furnace temperature controlling in depth and investigating dynamic matrix control algorithm and stair-like control, at the same time, fully considering the impaction of uncertainties in the process of blast furnace ironmaking, one kind of uncertain stair-like dynamic matrix predictive control algorithm for a class of tempreture in the blast furnace is proposed.
The algorithm inherited the advantages of traditional DMC algorithm. The prediction model is established in case that the coal rate, blast volume and flow of cooling water are the control inputs, and the temperature is control output. The system's future output is gained using the prediction model. Carry out feedback correction by compared with errors between the observed values and the output values of prediction model. Then compared with the reference temperature to obtain optimal control rate and rolling optimization. At the same time, this algorithm, in the blast furnace temperature control system, introduced a stair-like control to solve the volume of optimal control and avoid the complex matrix operations in DMC algorithm. Finally, as the impaction of uncertainties exists in the process of blast furnace ironmaking, in order to ensure that the optimal control rate is solvable, admissible control set is introduced. Adjust the uncertainty parameters in admissible control set to make the temperature’s predicted output value within the error range of±5°C . The result of simulating test shows that the predictive control algorithm can make a correct prediction of the current temperature, and has the advantages of good stability, which implies that this control approach is effective for improving the accuracy of the blast furnace temperature control. In addition, the man-machine interface of blast furnace temperature control system and its functions are introduced in this article.…………….
引文
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[4]周莉英,王欣,吴敏.基于RBF神经网络的铁水含硅量预报模型[J].电气传动, 2002,3: 25-27.
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[8]埜上洋.高炉数学模型的改进及其在实际操作中的应用[J].世界钢铁, 2003, 5: 1321.
[9]梁栋.高炉炉况智能化判断[D].重庆大学硕士论文, 2005.
[10]贺诗波,刘祥官,郜传厚,黄丽英,黄雅彬.高炉硅含量预测控制的时间序列混合建模[J].浙江大学学报, 2007, 41: 1739-1742.
[11]曾九孙,刘祥官,罗世华,颜光.主成分回归和偏最小二乘法在高炉冶炼中的应用[J]. 2009, 36: 33-36.
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[13]周驰化.模糊专家系统在高炉炉温预测控制中的研究与应用[D].西南石油大学硕士论文, 2001.
[14]王韬.预测控制在焦炉炉温控制中的研究[D].东北大学硕士论文, 2006.
[15]徐涛.预测控制在锅炉控制系统中的应用[D].大连海事大学硕士论文, 2004.
[16]李常贤.复杂系统的预测控制及其应用[D].浙江大学硕士论文, 2003.
[17]李书臣,徐心和,李平.预测控制最新算法综述[J].系统仿真学报, 2004, 16(6): 1314-1319.
[18]徐胜红,孙庆祥,顾文锦,蒋文钟.非线性预测控制模型方法综述[J].海军航空工程学院学报, 2007, 22(6): 633-636.
[19] Rouhani R. & Mehra R . K. Model Algorithmic Control (MAC), Basic Theoretical Properties[J]. Automatica, 1982, 18(4): 401-414.
[20] Cutler C. R . & Ramaker B. L. Dynamic Matrix Control-A Computer Control Algorithm[C]. Proc. of Joint Automatic Control Conference, 1980.
[21] Clarke D. W., Mohtadi C. & Tuffs P. S. Generalized Predictive Control[J]. Part 1 and Part 2, Automatica, 1987, 23(2): 137-160.
[22] Clarke D. W. & Mohtadi C. Properties of Generalized Predictive Control[J]. Automatica, 1989, 25(6): 859-875.
[23] Clarke D. W. Application of Generalized Predictive Control to Industrial Processes[J]. IEEE Control Syst. 1988, 8(2): 49-55.
[24]舒笛前.预测控制系统及其应用[M].北京:机械工业出版社, 1998, 6: 65-67.
[25] Yamamoto T., Omatu S. & Kaneda M. A Design Method of Self-tuning PID Controllers[C]. Proc. of American Control Conference, 1994, USA, 3263-3267.
[26] Wang W. & Hentiksen R. A Simplified Algorithm of Weighted Generalized Predictive Adaptive Control[J]. Modeling, Identification and Control, 1991, 12(3): 139-148.
[27] Lim K. W. & Ling K. V. Generalized Predictive Control of a Heat Exchanger [J]. IEEE Control Systems Magazine, 1989, 9(5): 9-12.
[28] Corradini M. L. & Orlando G. A. VSC Algorithm based on Generalized Predictive control[J]. Automatica, 1997, 33(5): 927-934.
[29] Scattolini R. & Bittani S. On the Choice of the Horizon in Long-range Predictive Control Some Simple Criteria[J]. Automatics, 1990, 20(5): 915-917.
[30] Ordys A. W. & Clarke D. W. A State-space Description for GRC controllers[J]. Int J. System, 1993, 24(9): 1727-1744.
[31] Clake D. W., Mosca E. & Scattolini R. Robustness of an adaptive Predictive Controller[J]. IEEE Trans. AC, 1994, 39(5): 1052-1056.
[32] Wang W. & Hentiksen R. A Globally Convergent Generalized Pole-Placement Adaptive control Algorithm[J]. Modeling, Identification and Control, 1991, 12(3): 139-148.
[33] Chai T. Y., Mao K. Z. & Qin X. F. Decoupling Design of Multivariable Generalized Predictive Control[J]. IEEE Part D, 1994, 141(3): 197-201.
[34] Mohamed L. H., Vincent W., et al. Fuzzy model-based predictive control[J]. Decision and Control, 1998, 3: 2927-2929.
[35] Zhang H. A multivariable fuzzy generalized predictive control approach and its performance analysis[J]. Aee.1998, 4: 2276-2280.
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