铁矿石烧结过程智能集成优化控制技术及其应用研究
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摘要
烧结过程是钢铁冶金生产的重要工序之一,直接影响到高炉炉况和钢铁产量。烧结过程是一个工艺流程长、影响因素多、机理复杂的动态系统,采用传统的控制理论和方法难以解决过程优化运行和优化控制问题。
本文通过深入分析烧结过程的工艺特点,论述烧结过程智能集成优化控制方法和控制策略,将复杂的烧结过程优化控制分解为烧结配料优化控制和烧结过程热状态优化控制两个阶段。根据烧结过程控制的目标和特点,分别对烧结配料优化控制、烧结终点优化控制和烧结过程多目标优化控制等技术进行研究,建立烧结生产智能集成优化控制系统。论文的主要研究内容如下：
(1)智能集成优化控制结构
针对铁矿石烧结过程的复杂特性和控制特性,提出一种智能集成优化控制系统的基本框架,从控制思想、过程建模方法和集成控制形式三方面,给出烧结过程智能集成优化控制系统的结构描述、设计原则和设计步骤,为铁矿石烧结过程的优化控制提供一种新思路。
(2)烧结配料优化控制
配料是烧结的基础。烧结配料效果的好坏直接影响烧结矿化学成分及其稳定性,并影响到原料的使用成本。本文分别建立一配和二配物料关系模型、烧结矿化学成分神经网络预测模型和基于线性规划的烧结生产成本优化模型,并在此基础上设计一种配料专家规则优化算法,通过稳定控制烧结矿化学成分和降低生产成本,实现配料结构的优化。
(3)烧结终点预测与多模型模糊切换控制
烧结终点位置是烧结过程的重要状态参数。为实现烧结过程的热状态优化控制,不仅需要获得当前实时的烧结终点位置,更重要的是获得烧结终点未来状态的变化趋势。本文对烧结终点的控制方法进行深入研究,针对烧结终点的大滞后特性,建立烧结终点神经网络预测模型。针对烧结过程的非线性、强耦合和模糊特性,研究模糊控制技术和预测控制技术的集成方法,采用模糊切换技术,建立烧结终点模糊-预测集成优化控制模型,使模型既具有较快的响应速度又有较强的鲁棒性。
(4)基于评价函数法的多目标优化控制技术
针对烧结过程中多个控制环节的控制目标相互影响、相互制约的问题,提出一种基于评价函数法的烧结过程多目标优化控制方法,通过构筑系统目标评价函数,将多目标优化问题转化为单目标优化问题进行求解,降低模型的求解难度,实现烧结终点和混合料槽料位的协调优化控制。
(5)烧结过程智能集成控制系统
以提出的智能集成优化控制系统框架为基础,采用EIC计算机控制系统实现铁矿石烧结过程的基础自动化和信息管理,开发烧结过程智能集成优化控制系统,将开发的烧结生产集成优化控制系统嵌入某钢铁企业烧结厂的烧结控制系统中,实现铁矿石烧结过程的集成优化控制。
通过应用铁矿石烧结过程智能集成优化技术,能优化配料结构,降低烧结矿化学成分和烧结终点的波动,从而实现烧结过程高产、质优和低耗的生产目的。实际工业应用效果证明该系统的工业有效性。
As an important step in iron ore production, the sintering process has a direct influence on state of the blast furnace and output of steel. Since the sintering process is a dynamic system with long circuit, multivariable and complex mechanism, it is hard to perform optimal control of process by using traditional control theory and methods.
In this paper, the intelligent integrated optimal controlling methods and strategies of sintering process are discussed, after an analysis of the characteristics of sintering process. The complicated optimal control of sintering process is divided into two parts:optimistic control of blending and heat status of sinter process. According to goals and characteristics of sintering process, controlling of blending, burning through point (BTP) and multi-objective optimal control technique are investigation respectively, and integrated optimal control system of sintering process is developed. The main study achievements include:
(1) Intelligent integrated optimal control structure
Based on the analysis of the complicated control characteristics of the iron ore sintering process, the basic frame of the intelligent integrated optimal control is proposed. The three parts of basic concept, which are the description of structure, the principles and steps of design, are put forward. This method provided a new idea for optimization control of the iron ore sintering process.
(2) Optimal control of blending
Blending is the foundation of the sintering process. The predicted model of composition based on artificial neural networks are established, the production cost optimal model based on linear programming is proposed, and the expert rules model aimed to adjust the percentages is constructed, then structure of the blending in the sintering process is optimized.
(3) Predictive model and multi-model transition controller based on fuzzy weight of BTP
BTP is crucial factor during sintering, which affects heat status of sintering process. The strategies of controlling BTP are studied deeply in this paper. Due to the sintering process having the characteristics of long time-delay, the prediction models of BTP based on neural network are put forward. Based on the analysis of the characteristics of strong nonlinear and strong coupling in the process, the integrated control method of fuzzy control technology and predictive control technology is studied. The optimal control models based on the multi-model transition controller of BTP is established. The performances of the two models have been compared through simulation. And the multi-model transition control method has better stability and adaptability.
(4) Multi-objective Optimization and Control based on Evaluation Function Algorithm
A multi-objective optimal control method based on evaluation function algorithm is proposed. The objective evaluation function of the system is designed, which transform the multi-objective optimization problems into the single-objective optimization problems and eventually results in coordinating control of the burning through point and the bunker-lever.
(5) Intelligent integrated optimal control system of sintering process
The basic automation is realized through the computer control system of EIC. The intelligent integrated optimal control system is developed based on the proposed frame of the intelligent integrated optimal control system, which has been embedded in the EIC in the sintering process of some Iron & Steel Co. and eventually results in integrated optimal control of the iron ore sintering process.
Through the application of the intelligent integrated optimal control technology in the iron-ore sintering process, the structure of blending is optimized, and the fluctuation of sinter compositions and BTP is restrained. The output and quality of sinter are improved effectively. The results of actual runs show that the system is effective.
本文通过深入分析烧结过程的工艺特点,论述烧结过程智能集成优化控制方法和控制策略,将复杂的烧结过程优化控制分解为烧结配料优化控制和烧结过程热状态优化控制两个阶段。根据烧结过程控制的目标和特点,分别对烧结配料优化控制、烧结终点优化控制和烧结过程多目标优化控制等技术进行研究,建立烧结生产智能集成优化控制系统。论文的主要研究内容如下：
(1)智能集成优化控制结构
针对铁矿石烧结过程的复杂特性和控制特性,提出一种智能集成优化控制系统的基本框架,从控制思想、过程建模方法和集成控制形式三方面,给出烧结过程智能集成优化控制系统的结构描述、设计原则和设计步骤,为铁矿石烧结过程的优化控制提供一种新思路。
(2)烧结配料优化控制
配料是烧结的基础。烧结配料效果的好坏直接影响烧结矿化学成分及其稳定性,并影响到原料的使用成本。本文分别建立一配和二配物料关系模型、烧结矿化学成分神经网络预测模型和基于线性规划的烧结生产成本优化模型,并在此基础上设计一种配料专家规则优化算法,通过稳定控制烧结矿化学成分和降低生产成本,实现配料结构的优化。
(3)烧结终点预测与多模型模糊切换控制
烧结终点位置是烧结过程的重要状态参数。为实现烧结过程的热状态优化控制,不仅需要获得当前实时的烧结终点位置,更重要的是获得烧结终点未来状态的变化趋势。本文对烧结终点的控制方法进行深入研究,针对烧结终点的大滞后特性,建立烧结终点神经网络预测模型。针对烧结过程的非线性、强耦合和模糊特性,研究模糊控制技术和预测控制技术的集成方法,采用模糊切换技术,建立烧结终点模糊-预测集成优化控制模型,使模型既具有较快的响应速度又有较强的鲁棒性。
(4)基于评价函数法的多目标优化控制技术
针对烧结过程中多个控制环节的控制目标相互影响、相互制约的问题,提出一种基于评价函数法的烧结过程多目标优化控制方法,通过构筑系统目标评价函数,将多目标优化问题转化为单目标优化问题进行求解,降低模型的求解难度,实现烧结终点和混合料槽料位的协调优化控制。
(5)烧结过程智能集成控制系统
以提出的智能集成优化控制系统框架为基础,采用EIC计算机控制系统实现铁矿石烧结过程的基础自动化和信息管理,开发烧结过程智能集成优化控制系统,将开发的烧结生产集成优化控制系统嵌入某钢铁企业烧结厂的烧结控制系统中,实现铁矿石烧结过程的集成优化控制。
通过应用铁矿石烧结过程智能集成优化技术,能优化配料结构,降低烧结矿化学成分和烧结终点的波动,从而实现烧结过程高产、质优和低耗的生产目的。实际工业应用效果证明该系统的工业有效性。
As an important step in iron ore production, the sintering process has a direct influence on state of the blast furnace and output of steel. Since the sintering process is a dynamic system with long circuit, multivariable and complex mechanism, it is hard to perform optimal control of process by using traditional control theory and methods.
In this paper, the intelligent integrated optimal controlling methods and strategies of sintering process are discussed, after an analysis of the characteristics of sintering process. The complicated optimal control of sintering process is divided into two parts:optimistic control of blending and heat status of sinter process. According to goals and characteristics of sintering process, controlling of blending, burning through point (BTP) and multi-objective optimal control technique are investigation respectively, and integrated optimal control system of sintering process is developed. The main study achievements include:
(1) Intelligent integrated optimal control structure
Based on the analysis of the complicated control characteristics of the iron ore sintering process, the basic frame of the intelligent integrated optimal control is proposed. The three parts of basic concept, which are the description of structure, the principles and steps of design, are put forward. This method provided a new idea for optimization control of the iron ore sintering process.
(2) Optimal control of blending
Blending is the foundation of the sintering process. The predicted model of composition based on artificial neural networks are established, the production cost optimal model based on linear programming is proposed, and the expert rules model aimed to adjust the percentages is constructed, then structure of the blending in the sintering process is optimized.
(3) Predictive model and multi-model transition controller based on fuzzy weight of BTP
BTP is crucial factor during sintering, which affects heat status of sintering process. The strategies of controlling BTP are studied deeply in this paper. Due to the sintering process having the characteristics of long time-delay, the prediction models of BTP based on neural network are put forward. Based on the analysis of the characteristics of strong nonlinear and strong coupling in the process, the integrated control method of fuzzy control technology and predictive control technology is studied. The optimal control models based on the multi-model transition controller of BTP is established. The performances of the two models have been compared through simulation. And the multi-model transition control method has better stability and adaptability.
(4) Multi-objective Optimization and Control based on Evaluation Function Algorithm
A multi-objective optimal control method based on evaluation function algorithm is proposed. The objective evaluation function of the system is designed, which transform the multi-objective optimization problems into the single-objective optimization problems and eventually results in coordinating control of the burning through point and the bunker-lever.
(5) Intelligent integrated optimal control system of sintering process
The basic automation is realized through the computer control system of EIC. The intelligent integrated optimal control system is developed based on the proposed frame of the intelligent integrated optimal control system, which has been embedded in the EIC in the sintering process of some Iron & Steel Co. and eventually results in integrated optimal control of the iron ore sintering process.
Through the application of the intelligent integrated optimal control technology in the iron-ore sintering process, the structure of blending is optimized, and the fluctuation of sinter compositions and BTP is restrained. The output and quality of sinter are improved effectively. The results of actual runs show that the system is effective.
引文
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