钛坯天然气加热炉优化控制策略及其工业应用研究
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摘要
天然气加热炉加热过程是钛坯生产的重要工序之一,其加热效果直接影响着钛材的轧制质量。由于钛的加热特性特殊,在加热过程中对加热炉的炉温以及炉内气氛的控制要求十分严格,传统的加热炉控制方法应用在钛坯加热过程中往往难以满足控制要求。
本文以天然气为燃料的钛坯加热炉为研究对象,充分分析钛坯天然气加热过程存在不确定、非线性、大滞后等控制难点,结合实际天然气加热炉加热过程中的钛坯加热特性,对加热过程中的温度和气氛等方面的控制进行了深入研究,建立了钛坯天然气加热炉优化控制系统。论文的主要研究工作和创新点如下：
(1)给出基于加热炉炉温预测模型的钛坯天然气加热炉优化控制结构
针对钛坯天然气加热炉钛坯加热过程的复杂特性,提出一种基于加热炉炉温预测的模糊自组织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,给出钛坯加热过程优化控制系统的结构描述、设计原则和设计步骤,为钛坯加热过程的优化控制提供一种新思路。
(2)提出基于神经网络和改进粒子群优化算法的加热炉炉温预测模型建模方法
钛坯加热炉加热过程具有不确定和大滞后的特点,预测模型的精度直接影响优化控制的性能。本文提出一种基于改进粒子群优化算法的神经网络炉温预测模型。该模型采用改进的粒子群算法对神经网络的权值进行初始化,在传统的自适应粒子群算法基础上,增加了位置状态的跳变因子和跳变策略,有效提高了粒子群算法的全局搜索能力。基于粒子群算法优化得到的神经网络初始化权值,建立BP神经网络炉温预测模型,描述加热过程的动态变化特性。
(3)提出钛坯加热炉自组织模糊优化控制方法
针对钛坯加热炉加热过程中的炉温控制问题,由于加热炉炉温影响参数众多,包括炉膛温度、空燃比、炉膛负压、辊道速度等,并且这些参数之间存在复杂的耦合关系,本文采用自组织方法设计了炉温模糊控制器。该控制器能够根据当前的输出情况自主生成和修正模糊控制规则表,克服了传统模糊控制中模糊规则表固定不变的缺陷。另外,引入变论域思想对模糊控制器的论域进行伸缩,并采用一种自适应变异差分进化算法应用于伸缩因子的优化求解,以克服变论域控制中伸缩因子难以确定的缺点。
针对加热炉使用的脉冲式加热燃烧方式,本文分析了其与连续式加热燃烧方式在原理上和操作上的不同之处,设计了合理的烧嘴设定模块,结合炉温变论域自组织模糊控制器,使炉温能够有效跟随设定值。同时,采用模糊控制方法平衡炉内气氛,保证钛坯在微氧化的环境下充分加热。
(4)钛坯天然气加热炉优化控制系统实现及应用
以所提出的天然气加热炉钛坯加热过程优化控制系统结构为基础,结合现场的钛坯加热炉工艺及生产条件,采用模块化和集成化思想,建立了钛坯天然气加热炉智能优化控制系统,实现了钛坯天然气加热炉炉温优化控制。
通过应用天然气加热炉钛坯加热过程优化控制技术,减小了炉温波动情况,降低了能源消耗,获得了显著的经济效益与环境效益,实际工业应用效果证明了该系统的工业有效性。
The reheating process of gas furnace, which directly affects the rolling quality of the titanium billet, is an important procedure in the production of titanium billet. Because of the special heating characteristics of the titanium, the furnace temperature and atmosphere are strictly limited. The traditional control metheds applied in the reheating process of steel billet furnace can't be directly used in the titanium billet reheating process.
Focusing on the natural gas reheating furnace of titanium, this dissertation deeply analyses the natural gas reheating process control difficulties, which affect the control performance, such as is uncertainty, nonlinearity and strong time-delay etc. Combining with the titanium billet heating characteristic in the reheating process, an intensive study focusing on the control of the furnace temperature has been made in this dissertation. Then, an intelligent optimization control system is build for the natural gas reheating furnace of titanium. The main contents are as follows:
(1) An optimization control architecture for the natural gas reheating furnace of titanium
To deal with the complex characteristics and control features in the reheating process of the titanium billet gas furnace, a basic architecture of fuzzy-self-organizing optimization control system is proposed based on the reheating furnace model. From three aspects consisting of control idea, process modeling and optimization control, this dissertation presents structure description, design principles and design steps for the optimization control system in the heating process of the natural gas reheating furnace of titanium. A new ideal was provided for optimal control of the Iron-titanium billet heating process.
(2) Process modeling based on neural network for the titanium billet heating process.
Because of the uncertainty and strong time-delay characteristics of the titanium billet reheating process, the accuracy of the processing model affects the performance of the optimization control directly. Based on improved particle swarm optimization (PSO) algorithm, this dissertation builds a neural network temperature prediction model, in which the neural network weight is initialized using the PSO algorithm. Compared with conventional PSO, the improved algorithm effectively improved the global search capability by adding jump factors and jump strategies of the location state. With the initialized neural network weights gained from the improved PSO optimal algorithm, a BP neural network prediction model for furnace temperature has been established to describe the dynamic characteristic of the reheating process.
(3) A self-organizing fuzzy optimal control method for Titanium billet heating furnace.
In the process of the temperature control for titanium billet reheating furnace, furnace temperature is affected by many factors coupling with each other, such as wall temperature, air-fuel ratio, furnace pressure and roller speed etc. A fuzzy controller is designed based on the self-organizing method, which can autonomously generate and correct the fuzzy control rule according to the current output, and can overcome the shortcoming of a fixed fuzzy control rule table in traditional fuzzy controller. In addition, with the variable universe theory, the universe of fuzzy controller is stretched or contracted, and the adaptive mutation differential evolution algorithm is used to find optimal stretch factors in variable universe.
According to the analysis of the differences in principle and operation between the pulse combustion method applied in this paper and the traditional continuous heating method, a flame burner setting method has been designed, with which the errors between the measured valued of furnace temperature and the goal value can be well controlled. Meanwhile, fuzzy control method is applied to control furnace atmosphere, so that titanium billet is fully burning at a low oxygen atmosphere.
(4) Optimal control for the natural gas reheating furnace of titanium.
Combining with the practical mechenism and production conditions of the titanium billet furnace, an intelligent optimization control system, which applies the ideal of modular and integration, is proposed to achieve the optimal control for natural gas reheating furnace of titanium.
The application of the optimal control technology in the heating process of natural gas reheating furnace has reduced furnace temperature fluctuations as well as energy consumption, which brings significant economic and environmental. The running results proved its industrial effectiveness.
本文以天然气为燃料的钛坯加热炉为研究对象,充分分析钛坯天然气加热过程存在不确定、非线性、大滞后等控制难点,结合实际天然气加热炉加热过程中的钛坯加热特性,对加热过程中的温度和气氛等方面的控制进行了深入研究,建立了钛坯天然气加热炉优化控制系统。论文的主要研究工作和创新点如下：
(1)给出基于加热炉炉温预测模型的钛坯天然气加热炉优化控制结构
针对钛坯天然气加热炉钛坯加热过程的复杂特性,提出一种基于加热炉炉温预测的模糊自组织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,给出钛坯加热过程优化控制系统的结构描述、设计原则和设计步骤,为钛坯加热过程的优化控制提供一种新思路。
(2)提出基于神经网络和改进粒子群优化算法的加热炉炉温预测模型建模方法
钛坯加热炉加热过程具有不确定和大滞后的特点,预测模型的精度直接影响优化控制的性能。本文提出一种基于改进粒子群优化算法的神经网络炉温预测模型。该模型采用改进的粒子群算法对神经网络的权值进行初始化,在传统的自适应粒子群算法基础上,增加了位置状态的跳变因子和跳变策略,有效提高了粒子群算法的全局搜索能力。基于粒子群算法优化得到的神经网络初始化权值,建立BP神经网络炉温预测模型,描述加热过程的动态变化特性。
(3)提出钛坯加热炉自组织模糊优化控制方法
针对钛坯加热炉加热过程中的炉温控制问题,由于加热炉炉温影响参数众多,包括炉膛温度、空燃比、炉膛负压、辊道速度等,并且这些参数之间存在复杂的耦合关系,本文采用自组织方法设计了炉温模糊控制器。该控制器能够根据当前的输出情况自主生成和修正模糊控制规则表,克服了传统模糊控制中模糊规则表固定不变的缺陷。另外,引入变论域思想对模糊控制器的论域进行伸缩,并采用一种自适应变异差分进化算法应用于伸缩因子的优化求解,以克服变论域控制中伸缩因子难以确定的缺点。
针对加热炉使用的脉冲式加热燃烧方式,本文分析了其与连续式加热燃烧方式在原理上和操作上的不同之处,设计了合理的烧嘴设定模块,结合炉温变论域自组织模糊控制器,使炉温能够有效跟随设定值。同时,采用模糊控制方法平衡炉内气氛,保证钛坯在微氧化的环境下充分加热。
(4)钛坯天然气加热炉优化控制系统实现及应用
以所提出的天然气加热炉钛坯加热过程优化控制系统结构为基础,结合现场的钛坯加热炉工艺及生产条件,采用模块化和集成化思想,建立了钛坯天然气加热炉智能优化控制系统,实现了钛坯天然气加热炉炉温优化控制。
通过应用天然气加热炉钛坯加热过程优化控制技术,减小了炉温波动情况,降低了能源消耗,获得了显著的经济效益与环境效益,实际工业应用效果证明了该系统的工业有效性。
The reheating process of gas furnace, which directly affects the rolling quality of the titanium billet, is an important procedure in the production of titanium billet. Because of the special heating characteristics of the titanium, the furnace temperature and atmosphere are strictly limited. The traditional control metheds applied in the reheating process of steel billet furnace can't be directly used in the titanium billet reheating process.
Focusing on the natural gas reheating furnace of titanium, this dissertation deeply analyses the natural gas reheating process control difficulties, which affect the control performance, such as is uncertainty, nonlinearity and strong time-delay etc. Combining with the titanium billet heating characteristic in the reheating process, an intensive study focusing on the control of the furnace temperature has been made in this dissertation. Then, an intelligent optimization control system is build for the natural gas reheating furnace of titanium. The main contents are as follows:
(1) An optimization control architecture for the natural gas reheating furnace of titanium
To deal with the complex characteristics and control features in the reheating process of the titanium billet gas furnace, a basic architecture of fuzzy-self-organizing optimization control system is proposed based on the reheating furnace model. From three aspects consisting of control idea, process modeling and optimization control, this dissertation presents structure description, design principles and design steps for the optimization control system in the heating process of the natural gas reheating furnace of titanium. A new ideal was provided for optimal control of the Iron-titanium billet heating process.
(2) Process modeling based on neural network for the titanium billet heating process.
Because of the uncertainty and strong time-delay characteristics of the titanium billet reheating process, the accuracy of the processing model affects the performance of the optimization control directly. Based on improved particle swarm optimization (PSO) algorithm, this dissertation builds a neural network temperature prediction model, in which the neural network weight is initialized using the PSO algorithm. Compared with conventional PSO, the improved algorithm effectively improved the global search capability by adding jump factors and jump strategies of the location state. With the initialized neural network weights gained from the improved PSO optimal algorithm, a BP neural network prediction model for furnace temperature has been established to describe the dynamic characteristic of the reheating process.
(3) A self-organizing fuzzy optimal control method for Titanium billet heating furnace.
In the process of the temperature control for titanium billet reheating furnace, furnace temperature is affected by many factors coupling with each other, such as wall temperature, air-fuel ratio, furnace pressure and roller speed etc. A fuzzy controller is designed based on the self-organizing method, which can autonomously generate and correct the fuzzy control rule according to the current output, and can overcome the shortcoming of a fixed fuzzy control rule table in traditional fuzzy controller. In addition, with the variable universe theory, the universe of fuzzy controller is stretched or contracted, and the adaptive mutation differential evolution algorithm is used to find optimal stretch factors in variable universe.
According to the analysis of the differences in principle and operation between the pulse combustion method applied in this paper and the traditional continuous heating method, a flame burner setting method has been designed, with which the errors between the measured valued of furnace temperature and the goal value can be well controlled. Meanwhile, fuzzy control method is applied to control furnace atmosphere, so that titanium billet is fully burning at a low oxygen atmosphere.
(4) Optimal control for the natural gas reheating furnace of titanium.
Combining with the practical mechenism and production conditions of the titanium billet furnace, an intelligent optimization control system, which applies the ideal of modular and integration, is proposed to achieve the optimal control for natural gas reheating furnace of titanium.
The application of the optimal control technology in the heating process of natural gas reheating furnace has reduced furnace temperature fluctuations as well as energy consumption, which brings significant economic and environmental. The running results proved its industrial effectiveness.
引文
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