铅锌烧结过程状态智能预测与优化控制策略
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摘要
铅锌金属在国防、电子等众多工业领域有广泛的应用,铅锌烧结过程的稳定性及烧结块质量的好坏,对铅锌冶炼生产效率的高低有着举足轻重的影响。烧结过程状态反映了铅锌烧结生产状况,状态的稳定和优化有助于提高烧结块的质量和产量。针对铅锌烧结过程的非线性、不确定性特点,本文主要围绕过程状态智能集成建模与优化控制策略开展研究,取得的研究成果主要包括以下五个方面。
(1)综合生产目标与过程状态参数的关系分析及优化控制结构
铅锌烧结状态反映了烧结程度,影响到烧结块的质量和产量,并且烧结状态参数众多,对烧结过程综合生产目标的影响程度也不同。本文深入分析操作参数、过程状态参数和综合生产目标的关系,提出了状态集成预测、综合生产目标优化和过程状态参数优化的优化思想。由此确定状态优化控制目标,提出铅锌烧结过程状态智能集成优化控制结构,分析状态集成优化控制的工作原理,从而为铅锌烧结过程的优化控制提供一种新思路。
(2)过程状态参数预测模型
透气性和烧穿点位置直接影响到烧结块的质量和产量,是铅锌烧结过程控制的重要状态参数。为实现铅锌烧结过程的状态优化控制,不仅需要获得当前实时的状态指标参数,更重要的是获得未来状态的变化趋势。本文针对透气性的时变和不确定性,建立基于RBF神经网络的透气性预测模型,较准确地进行透气性的实时预测。由于烧穿点主要受到烧结料面烟气温度的影响,采用固定点和非固定点的实验方法,研究铅锌烧结机内烟气温度分布规律,采用神经网络建立烟气温度场分布模型,从而建立烧穿点灰色预测模型；考虑工况波动的影响,采用支持向量机建立烧穿点工艺参数预测模型；然后采用动态加权法对两个模型进行集成,建立烧穿点状态预测模型,从而进行烧穿点的实时预测。采用MATLAB7.0仿真软件,对模型进行验证。仿真结果表明,利用本文方法建立的烧穿点集成预测模型能够获得更高的的预测精度,其预测效果和性能优于单一预测模型。
(3)基于遗传蚁群算法的状态优化设定
为达到高产、优质的生产目标,必须对透气性和烧穿点进行优化控制,使得烧结生产稳定在最优的状态。基于工艺机理分析和控制需求,将过程状态参数和综合生产目标之间的关系,归纳为一个带有不等式约束状态参数指标的综合收益函数形式描述问题。首先采用罚函数法将将具有多约束条件的目标函数转换为无约束的罚函数形式；然后采用遗传算法对目标函数寻优,获得优化问题的次优解；接着采用蚁群算法进行二次优化,结果作为烧结状态的最优设定值。仿真结果验证了该优化算法的有效性。
(4)基于自适应免疫禁忌搜索算法的状态优化控制
基于铅锌烧结过程状态的预测和状态优化设定,根据状态优化控制目标,将烧结状态优化控制问题归纳为一个非线性多目标优化问题。针对铅锌烧结过程参数难检测、强非线性和时滞的特点,本文研究自适应免疫禁忌优化算法,用于求解获得一组过程操作参数,实现烧结过程的状态稳定优化控制。
(5)集成优化控制应用研究
基于状态智能集成优化控制器,提出一个状态智能集成优化控制系统递阶结构。结合某企业实际运行数据,对本文所提方法进行仿真验证。优化结果表明,由于对烧结状态采用了优化控制策略,能够使透气性状态和烧穿点状态降低波动,为实现铅锌烧结过程优化控制奠定了基础。
Lead and Zinc are widely used in many fields, such as military industry, electronic industry, etc. Stability of the lead-zinc sintering process (LZSP) and quality of sinter are essential to the LZSP. Since the state of sinter reflects the status of the LZSP, a stable and optimal state of sinter is of great help to increasing the quality and quantity (Q&Q) of agglomerate. Based on the features of strong nonlinearity and uncertainty, of the LZSP, this dissertation studies an intelligent integrated modeling and optimization control strategy for the LZSP, and produces achievements mainly in the following five aspects.
(1) The analysis of the relationships between global production target and state parameters, and the structure of optimization and control for sintering process
The state of the LZSP reflects the status of the LZSP and infects the Q&Q of sintering agglomerate. Note that the number of state parameters is large and they have different effects on the global production target, this dissertation makes an in-depth analysis on the relationships between the operation parameters, state parameters, and global production target, and determines the target for state optimization control. Then, the structure of the state intelligent integrated optimization and control is devised. Finally, the principle of state integrated optimization and controller is presented. This method provides a new idea for optimization and control of the LZSP.
(2) Prediction models of the state parameters
Since permeability and burn through point (BTP) directly affect the Q&Q of sintering agglomerate, they are the most important stste parameters in the LZSP. To carry out the optimization and control of the LZSP, we require not only the current state parameters but also their future changing trends. Based on the features of time-vary and uncertainty of the permeability, we establish a radial basis function (RBF) neural network model to accurately predict the permeability. The BTP is mainly affected by a surface temperature, an experiment method which combines fixed measurement points with non-fixed measurement points is used to investigate the distribution of gas temperature in the sintering machine. Based on the analysis results, we use a back propagation (BP) neural network to establish the model of the gas temperature distribution (GTD), and further build a grey model for the BTP. To consider the influence caused by the status fluctuations, we use the support vector machine to establish a technology parameter model for the BTP. Then, we integrate these two models into an integrated state prediction model of the BTP using dynamic weights. MATLAB 7.0 is used to verify the validity of the presented optimization method. The experimental results show that the prediction precision of the integrated model is higher than that of a single prediction model.
(3) Genetic-ant-algorithm-based state optimization and setting
In order to achieve the production target of high Q&Q, we need to optimize and control the permeability and BTP effectively, and stabilize the LZSP at an optimal state. Based on the analysis of machnism and control requirements, we express the relation between the state parameters and the production target as a synthetic profit function with inequality constrains. In order to solve this problem, the penalty function method is used to transform the muti-target-constrained optimization problem to an unconstrained optimization problem. Then, we use a genetic algorithm to perform coarse optimization, and an ant algorithm to carry out fine optimization. This gives us a suboptimal solution. The solution is then used as an optimal setting of the state. Simulation results show the validity of the method of the genetic-ant-algorithm-based state optimization and setting.
(4) Self-adapt-immune-tabu-search-based state optimization and control
Based on the prediction, optimization and setting of the state in the LZSP, and according to the target of state optimization and control, we formulate the problem of state optimization and control of the LZSP as a nonlinear and multi-objective optimization problem. To deal with the problems of unmeasurable parameters, nonlinearity and a time delay in the LZSP, we utilize a self-adapt immune tabu search optimization algorithm to optimize the target function, and to obtain a set of optimal operation parameters. This allows us to implement the state stabilization and optimal control.
(5) Application investigation of integrated optimization and control
Based on the controller of state intelligent integrated optimization, this dissertation presents a hierarchical configuration of state intelligent integrated optimization and control system. Simulation results show that the fluctuations of permeability and BTP are suppressed to a low level by the state optimization and control strategy. The system lays the foundation of implementing optimization and control of the LZSP.
(1)综合生产目标与过程状态参数的关系分析及优化控制结构
铅锌烧结状态反映了烧结程度,影响到烧结块的质量和产量,并且烧结状态参数众多,对烧结过程综合生产目标的影响程度也不同。本文深入分析操作参数、过程状态参数和综合生产目标的关系,提出了状态集成预测、综合生产目标优化和过程状态参数优化的优化思想。由此确定状态优化控制目标,提出铅锌烧结过程状态智能集成优化控制结构,分析状态集成优化控制的工作原理,从而为铅锌烧结过程的优化控制提供一种新思路。
(2)过程状态参数预测模型
透气性和烧穿点位置直接影响到烧结块的质量和产量,是铅锌烧结过程控制的重要状态参数。为实现铅锌烧结过程的状态优化控制,不仅需要获得当前实时的状态指标参数,更重要的是获得未来状态的变化趋势。本文针对透气性的时变和不确定性,建立基于RBF神经网络的透气性预测模型,较准确地进行透气性的实时预测。由于烧穿点主要受到烧结料面烟气温度的影响,采用固定点和非固定点的实验方法,研究铅锌烧结机内烟气温度分布规律,采用神经网络建立烟气温度场分布模型,从而建立烧穿点灰色预测模型；考虑工况波动的影响,采用支持向量机建立烧穿点工艺参数预测模型；然后采用动态加权法对两个模型进行集成,建立烧穿点状态预测模型,从而进行烧穿点的实时预测。采用MATLAB7.0仿真软件,对模型进行验证。仿真结果表明,利用本文方法建立的烧穿点集成预测模型能够获得更高的的预测精度,其预测效果和性能优于单一预测模型。
(3)基于遗传蚁群算法的状态优化设定
为达到高产、优质的生产目标,必须对透气性和烧穿点进行优化控制,使得烧结生产稳定在最优的状态。基于工艺机理分析和控制需求,将过程状态参数和综合生产目标之间的关系,归纳为一个带有不等式约束状态参数指标的综合收益函数形式描述问题。首先采用罚函数法将将具有多约束条件的目标函数转换为无约束的罚函数形式；然后采用遗传算法对目标函数寻优,获得优化问题的次优解；接着采用蚁群算法进行二次优化,结果作为烧结状态的最优设定值。仿真结果验证了该优化算法的有效性。
(4)基于自适应免疫禁忌搜索算法的状态优化控制
基于铅锌烧结过程状态的预测和状态优化设定,根据状态优化控制目标,将烧结状态优化控制问题归纳为一个非线性多目标优化问题。针对铅锌烧结过程参数难检测、强非线性和时滞的特点,本文研究自适应免疫禁忌优化算法,用于求解获得一组过程操作参数,实现烧结过程的状态稳定优化控制。
(5)集成优化控制应用研究
基于状态智能集成优化控制器,提出一个状态智能集成优化控制系统递阶结构。结合某企业实际运行数据,对本文所提方法进行仿真验证。优化结果表明,由于对烧结状态采用了优化控制策略,能够使透气性状态和烧穿点状态降低波动,为实现铅锌烧结过程优化控制奠定了基础。
Lead and Zinc are widely used in many fields, such as military industry, electronic industry, etc. Stability of the lead-zinc sintering process (LZSP) and quality of sinter are essential to the LZSP. Since the state of sinter reflects the status of the LZSP, a stable and optimal state of sinter is of great help to increasing the quality and quantity (Q&Q) of agglomerate. Based on the features of strong nonlinearity and uncertainty, of the LZSP, this dissertation studies an intelligent integrated modeling and optimization control strategy for the LZSP, and produces achievements mainly in the following five aspects.
(1) The analysis of the relationships between global production target and state parameters, and the structure of optimization and control for sintering process
The state of the LZSP reflects the status of the LZSP and infects the Q&Q of sintering agglomerate. Note that the number of state parameters is large and they have different effects on the global production target, this dissertation makes an in-depth analysis on the relationships between the operation parameters, state parameters, and global production target, and determines the target for state optimization control. Then, the structure of the state intelligent integrated optimization and control is devised. Finally, the principle of state integrated optimization and controller is presented. This method provides a new idea for optimization and control of the LZSP.
(2) Prediction models of the state parameters
Since permeability and burn through point (BTP) directly affect the Q&Q of sintering agglomerate, they are the most important stste parameters in the LZSP. To carry out the optimization and control of the LZSP, we require not only the current state parameters but also their future changing trends. Based on the features of time-vary and uncertainty of the permeability, we establish a radial basis function (RBF) neural network model to accurately predict the permeability. The BTP is mainly affected by a surface temperature, an experiment method which combines fixed measurement points with non-fixed measurement points is used to investigate the distribution of gas temperature in the sintering machine. Based on the analysis results, we use a back propagation (BP) neural network to establish the model of the gas temperature distribution (GTD), and further build a grey model for the BTP. To consider the influence caused by the status fluctuations, we use the support vector machine to establish a technology parameter model for the BTP. Then, we integrate these two models into an integrated state prediction model of the BTP using dynamic weights. MATLAB 7.0 is used to verify the validity of the presented optimization method. The experimental results show that the prediction precision of the integrated model is higher than that of a single prediction model.
(3) Genetic-ant-algorithm-based state optimization and setting
In order to achieve the production target of high Q&Q, we need to optimize and control the permeability and BTP effectively, and stabilize the LZSP at an optimal state. Based on the analysis of machnism and control requirements, we express the relation between the state parameters and the production target as a synthetic profit function with inequality constrains. In order to solve this problem, the penalty function method is used to transform the muti-target-constrained optimization problem to an unconstrained optimization problem. Then, we use a genetic algorithm to perform coarse optimization, and an ant algorithm to carry out fine optimization. This gives us a suboptimal solution. The solution is then used as an optimal setting of the state. Simulation results show the validity of the method of the genetic-ant-algorithm-based state optimization and setting.
(4) Self-adapt-immune-tabu-search-based state optimization and control
Based on the prediction, optimization and setting of the state in the LZSP, and according to the target of state optimization and control, we formulate the problem of state optimization and control of the LZSP as a nonlinear and multi-objective optimization problem. To deal with the problems of unmeasurable parameters, nonlinearity and a time delay in the LZSP, we utilize a self-adapt immune tabu search optimization algorithm to optimize the target function, and to obtain a set of optimal operation parameters. This allows us to implement the state stabilization and optimal control.
(5) Application investigation of integrated optimization and control
Based on the controller of state intelligent integrated optimization, this dissertation presents a hierarchical configuration of state intelligent integrated optimization and control system. Simulation results show that the fluctuations of permeability and BTP are suppressed to a low level by the state optimization and control strategy. The system lays the foundation of implementing optimization and control of the LZSP.
引文
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