炼铁生产流程的分散协调优化方法研究
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摘要
在能源供给日益紧张的当今社会,节能降耗作为目前我国经济发展必须遵循的原则之一,有着特殊的重要地位。然而我国钢铁企业目前普遍采用的炼铁流程各工序各自为政的孤立优化控制模式,使得当原料成分波动以及各类运行故障发生时,炼铁流程的整体控制调节效果不佳,易造成不必要的能量耗费,难以实现炼铁过程的平稳运行。针对这一问题,本文提出了一种面向多时间尺度大规模互联系统的协调优化算法,并应用于炼铁流程的控制和优化,具有计算速度快、综合性能好等优点。
本文的研究内容主要包括以下几个方面：
1.提出一种互联大系统的分散化预测控制算法。该算法采用预测控制滚动优化的形式,利用反馈校正及时修正模型误差,在每个滚动窗口内,使用约减状态空间分解算法实现互联大系统的分散化控制,通过内外部状态分解减小优化问题求解规模,在保证优化性能的同时,显著提高了优化计算的实时性。
2.在此基础上,提出了一种面向多时间尺度系统的分散化预测控制实现方法。通过虚拟改变快子系统(响应速度快的子系统)采样周期的方式解决由时间多尺度带来的问题,同时,预测窗口长度由参与该次优化的最慢子系统(响应速度最慢的子系统)决定,以解决计算实时性的问题。此外,通过对控制量实施滤波规则减少执行器的频繁调节动作,从而保证生产的平稳性。
3.使用部分杭州钢铁股份有限公司炼铁厂(以下简称杭钢)的实际生产数据,对本论文提出的方法进行了仿真实验,并与现有的集中式预测控制算法、独立式分散化预测控制算法和常规分散式预测控制算法在计算时间、优化性能、动态响应等方面进行了比较。结果表明,本论文提出的方法很好地处理了计算实时性和优化性能之间的矛盾,综合性能最优。在此基础上,本文给出了杭钢炼铁过程的整体协调优化方案。
As one of the principles that must be followed in economy development of our country, reducing energy consumption is of great importance in modern society which is increasingly short of energy supply. However, in the present, steel enterprises in China commonly apply the isolated mode of optimization and control to sub-processes of iron-making flow, and when raw material content fluctuates and various operation disturbances take place, the overall control effect on iron-making flow may not be so satisfied, with unnecessary energy cost and non-smooth running. To solve the above problem, this thesis proposed a coordinated optimization algorithm oriented at multi-time-scale inter-connected large systems and applied it in optimization and control of the simulated iron-making flow system. Results show that this algorithm has the advantage of fine real-time computation ability and good combination property.
The contents of this thesis are described as follows:
1. A decentralized predictive control algorithm targeted at multi-time-scale inter-connected large systems was proposed. This algorithm operates in rolling-horizon optimization form, and implements feedback for model correction. In each rolling horizon, original reduced state-space decomposition algorithm is used to realize decentralized control of inter-connected large system, and the size of optimization problem is decreased through the decomposition of interior and exterior states, thus the real-time computation ability is greatly improved while optimization performance hardly deteriorates.
2. Based on this, a decentralized predictive control realization oriented at multi-time-scale system was proposed. The problem that resulted from multi-time scale is solved by virtually changing the sampling period of fast sub-system, and the length of prediction horizon is determined by the slowest sub-system, in order to solve the problem of bad real-time computation ability. Besides, a filter rule is enforced upon control variables to reduce frequent regulation of actuators, thus to confirm the smoothness of production.
3. Using partial actual production data of Hangzhou Steel and Iron Corporation, simulated experiment was carried out on the sub-system models of iron-making flow to test the effectiveness of algorithm of alterable horizon-decentralized predictive control, and contrast simulations with existed algorithms were made which included integrated predictive control algorithm, isolated decentralized predictive control algorithm and normal distributed predictive control algorithm, and the optimization effects were compared in indices of computation time, object function and responses. Results show that algorithm of alterable horizon decentralized predictive control deals extraordinarily well with performance index and real-time computation ability. Besides, the integral coordinated optimization solution to Hangzhou Steel and Iron Corporation ironmaking process is given in this thesis.
本文的研究内容主要包括以下几个方面：
1.提出一种互联大系统的分散化预测控制算法。该算法采用预测控制滚动优化的形式,利用反馈校正及时修正模型误差,在每个滚动窗口内,使用约减状态空间分解算法实现互联大系统的分散化控制,通过内外部状态分解减小优化问题求解规模,在保证优化性能的同时,显著提高了优化计算的实时性。
2.在此基础上,提出了一种面向多时间尺度系统的分散化预测控制实现方法。通过虚拟改变快子系统(响应速度快的子系统)采样周期的方式解决由时间多尺度带来的问题,同时,预测窗口长度由参与该次优化的最慢子系统(响应速度最慢的子系统)决定,以解决计算实时性的问题。此外,通过对控制量实施滤波规则减少执行器的频繁调节动作,从而保证生产的平稳性。
3.使用部分杭州钢铁股份有限公司炼铁厂(以下简称杭钢)的实际生产数据,对本论文提出的方法进行了仿真实验,并与现有的集中式预测控制算法、独立式分散化预测控制算法和常规分散式预测控制算法在计算时间、优化性能、动态响应等方面进行了比较。结果表明,本论文提出的方法很好地处理了计算实时性和优化性能之间的矛盾,综合性能最优。在此基础上,本文给出了杭钢炼铁过程的整体协调优化方案。
As one of the principles that must be followed in economy development of our country, reducing energy consumption is of great importance in modern society which is increasingly short of energy supply. However, in the present, steel enterprises in China commonly apply the isolated mode of optimization and control to sub-processes of iron-making flow, and when raw material content fluctuates and various operation disturbances take place, the overall control effect on iron-making flow may not be so satisfied, with unnecessary energy cost and non-smooth running. To solve the above problem, this thesis proposed a coordinated optimization algorithm oriented at multi-time-scale inter-connected large systems and applied it in optimization and control of the simulated iron-making flow system. Results show that this algorithm has the advantage of fine real-time computation ability and good combination property.
The contents of this thesis are described as follows:
1. A decentralized predictive control algorithm targeted at multi-time-scale inter-connected large systems was proposed. This algorithm operates in rolling-horizon optimization form, and implements feedback for model correction. In each rolling horizon, original reduced state-space decomposition algorithm is used to realize decentralized control of inter-connected large system, and the size of optimization problem is decreased through the decomposition of interior and exterior states, thus the real-time computation ability is greatly improved while optimization performance hardly deteriorates.
2. Based on this, a decentralized predictive control realization oriented at multi-time-scale system was proposed. The problem that resulted from multi-time scale is solved by virtually changing the sampling period of fast sub-system, and the length of prediction horizon is determined by the slowest sub-system, in order to solve the problem of bad real-time computation ability. Besides, a filter rule is enforced upon control variables to reduce frequent regulation of actuators, thus to confirm the smoothness of production.
3. Using partial actual production data of Hangzhou Steel and Iron Corporation, simulated experiment was carried out on the sub-system models of iron-making flow to test the effectiveness of algorithm of alterable horizon-decentralized predictive control, and contrast simulations with existed algorithms were made which included integrated predictive control algorithm, isolated decentralized predictive control algorithm and normal distributed predictive control algorithm, and the optimization effects were compared in indices of computation time, object function and responses. Results show that algorithm of alterable horizon decentralized predictive control deals extraordinarily well with performance index and real-time computation ability. Besides, the integral coordinated optimization solution to Hangzhou Steel and Iron Corporation ironmaking process is given in this thesis.
引文
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