烧结混合料制备过程智能集成优化控制策略及其工业应用
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摘要
烧结过程是炼铁工艺中的重要流程,其中的配料与混合制粒过程属于混合料制备过程。在实际工业生产中,混合料制备过程并未得到充分优化,在配料方面存在混合料成分准确率不高,烧结矿质量波动的问题；存在成本和硫含量偏高,经济效益和环境效益不高的问题；在混合制粒方面,存在混合料粒度分布欠合理,烧结工序能耗高的问题。针对上述问题,本文围绕混合料制备过程智能集成优化控制策略展开研究,主要的研究工作与创新点如下：
(1)基于机理分析和数据驱动的烧结配料过程智能集成建模方法
针对烧结矿质量预测复杂,配料准确率较低而导致烧结矿质量波动的问题,提出一种烧结矿质量级联集成预测模型。首先,分析烧结过程中化学反应物质质变和量变关系,建立烧结矿质量的机理模型；根据灰色关联度分析方法确定影响烧结矿质量的关键参数,并将其区分为已知的配料参数信息和未知的烧结过程状态参数；依据烧结生产稳定性要求,建立T-S模糊融合的GM(1,1)灰色模型和最小二乘支持向量机(LS-SVM)集成模型,获得烧结过程状态参数合理的预测值。在此基础上,分别建立具有全局逼近能力BP神经网络模型(BPNN)和局部泛化能力的LS-SVM模型以预测烧结矿质量,并从信息论的观点出发,提出一种根据预测误差序列的变异程度加权的信息熵融合方法,通过对机理模型、BPNN和LS-SVM模型的预测结果进行加权集成,获得准确的烧结矿质量预测值。仿真结果和实际运行表明级联集成模型的预测精度高于单级预测模型和级联单一预测模型,能够准确预测烧结矿质量,满足配料计算对预测精度和数据完备性的要求。
通过研究焦粉配比与烧结矿质量之间的数值关系,建立以烧结矿质量为约束的焦粉配比优化模型；通过研究烧结过程的主要物理、化学变化,建立基于热平衡的焦粉配比下限模型,从而缩小焦粉配比优化模型的搜索范围,克服了传统经验配比难以实现焦粉用量优化的局限性,为实现烧结配料过程节能目标奠定了基础。
(2)兼顾降低成本与减少硫含量的烧结配料多目标优化模型
针对传统配料方法中存在成本和污染物元素含量偏高,而一般的配料优化模型既缺乏对烧结配料工艺过程的合理描述,也缺乏对能耗、环境效益的关注问题,结合烧结配料中一次配料与二次配料不同的工艺特点和指标要求,在分析烧结过程原料关键化学成分指标和经济性基础上,以库存量和烧结矿化学成分指标为约束条件,建立一种基于线性加权和的烧结配料多目标优化模型(包括一次配料和二次配料多目标优化模型),较传统的配料成本优化模型而言,实现兼顾降低配料成本与减少硫含量的烧结配料多目标优化。
(3)烧结配料多目标综合优化方法
在建立基于线性加权和的烧结配料多目标优化模型的基础上,本文设计基于线性规划(LP)和遗传-粒子群(GA-PSO)的烧结配料多目标综合优化算法,搜索一次配料与二次配料优化模型的最优配比。该算法首先采用LP方法求解基于线性加权和的烧结配料多目标优化模型的最优解,若LP方法计算失效,则采用GA-PSO算法进行搜索,算法搜索初期采用基本粒子群算法(PSO),当PSO收敛停滞,采用遗传算法(GA)的交叉、变异操作增加粒子群的多样性,避免了基本PSO算法收敛末期的振荡特点,实现快速收敛。本文将烧结配料多目标综合优化算法应用工业生产现场,实现烧结配料配比优化。
(4)基于粒度分布评估的混合制粒智能集成优化控制策略
混合料粒度分布影响因素众多,而工艺指标仅对混合料粒度分布进行定性描述。粒度分布优化控制缺乏准确的数学模型和明确的优化目标,采用传统优化方法与控制策略难以实现粒度分布优化控制。针对上述问题,本文提出一种基于粒度分布评估与优化的控制算法。首先,提出粒级参数的概念,将连续的粒度分布用离散的粒级参数表示；其次,分析筛分实验粒级参数与对应混合料的烧结状态,采用料层厚度和平均透气性指数建立模糊评估函数,建立粒级参数与对应评估值的样本集,采用基于BP神经网络方法建立粒级参数的评估模型；再次,以评估模型为目标函数,以粒级参数为决策变量,在生产边界条件的约束下采用PSO算法求解获得最优粒级参数；最后,建立水分设定模型,通过代入最优粒级参数求解得到制粒过程水分优化设定值,克服了传统水分控制难以实现粒度分布优化控制的局限性。仿真实验表明：算法有效改善了制粒效果。
(5)混合料制备过程优化控制策略的工业应用
结合工业现场实际,根据某360m2烧结生产线进行系统软件和数据流设计,建立烧结配料优化与决策支持系统,实现企业兼顾配料成本与减少焦粉用量、降低硫含量目标的综合优化,取得了明显的经济效益和环境效益。
Sintering is one of the important loops in the iron ores smelting process. The burden and mixing granulation belong to Iron Ores Mixture Preparation (IOMP). In actual industrial process, IOMP process has not been optimized enough. In the burden process, the proportions are with low accuracy leading the fluctuation of sintered ore quality, and the high sulfur content and cost cause a low economical and environmental benefit. In mixing granulation process, irrational granularity distribution may cause high energy consumption in sinttering. To deal with the problems, the thesis focuses on intelligent integrated optimization and control strategy in IOMP. Main researches and innovations of the thesis are given below:
(1) An intelligent integrated modeling method for burden process based on mechanism analysis and data-driven method
To deal with the quality fluctuation problem caused by the complexity of sintering quality prediction and low accuracy of ores proportions, a kind of intelligent integrated models with cascade structure for sinter qualities prediction is put forward. Firstly, after analyzing the qualitative and quantitative changes of chemicals in sintering process, the mechanism models of sinter qualities are worked out. On the basis of grey relational analysis, key parameters affecting the quality of sintered ore are found, and then they are separated into two parts:the known proportion information and the unkown sintering states parameters. According to the sintering stability requirements, Integrated T-S fuzzy fusion models, which are consisted by Gray Model (GM(1,1)) and Least Squares Support Vector Machine (LS-SVM), are worked out to give rational prediction values of sintering states papameters. On the above basis, BP Neural Network (BPNN) with global approximation capbility and LS-SVM with local generalization capability are seperatly worked out to predict the qualites of sintered ore. After that, from the viewpoint of information theory, an information entropy fusion method weitghting outputs by the variation degree of prediction error sequence is designed to integrate the outputs of mechanism model, BPNN and LS-SVM, so that the qualites of sintered ore can be predicted correctly. The simulation and industrial applications show that the accuracy of intelligent integrated model with cascade structure is higher than single-stage prediction model or single model with cascade structure. It can predict qualites of sintered ore correctly and satisfy data completeness requirements during burden optimization.
After analyzing the numerical relationship between coke ratio and qualites of sintered ore, a coke proportion optimization model constrainted by qualites of sintered ore is proposed. Then according to the analysis of the main physical and chemical changes in sintering process, a computing model for lower limit of coke proportion based on heat balance is worked out to narrow the searching range of coke in the optimization model. The model can overcome the difficulty of conventional experience-based proportioning method that it is hard to realize coke proportion optimization, and establishes the foundation of energy saving optimization.
(2) A multi-objective optimization model of burden process for cost and sulfur content reducing
In the burdening process, the traditional proportion methods usually is with high cost and sulfur content, and the ordinary optimization models can neither appoarch to the burden process precisely nor consider the cost, energy consumption and environment benifit comprehensively. To deal with those problems, linear weighted multi-objective optimization models of burden process (including primary and secondary proportion optimization models) using inventory and sintered ore chemical constraints, which is based on the different characteristics of primary and secondary proportions as well as the economy and key chemical components analysises of different materials, are proposed in the thesis. The optimization models could realize the comprehensive optimization of reducing cost and sulfur content compared with conventional proportion cost optimization model.
(3) Multi-objective comprehensive optimization algorithm of burden process
Based on the linear weighted multi-object optimization models of burden process, a multi-objective comprehensive optimization algorithm, which is integrated Linear Program (LP) with Genetic Algorithm and Particle Swarm Optimization (GA-PSO), is proposed to search the optimal solutions of primary and secondary proportions in the thesis. Firstly, LP method is applied to solve the linear weighted multi-object optimization models. If it does not work, GA-PSO algorithm is implemented. In this algorithm, Particle Swarm Optimization (PSO) algorithm is used during the early period. When PSO stops converging, the operations of crossing and mutations from Genetic Algorithm (GA) are executed with a certain probability to increase diversity of particle group, which avoids the fluctuation in late convergence and increase the convergence rate. The multi-objective comprehensive optimization algorithm is realized in actual industrial application to optimize the burden process.
(4) Intelligent integrated optimal control algorithm for mixing granulation based on evaluation of granularity distribution
The factors effecting granularity distributions of iron ores mixture are various, while the process index of granularity distributions is quantitative. Hence, the granularity distribution optimal control lacks of accurate mathmatics models and clear targets, the process can hardly be optimized by using traditional optimal control strategies. Aimed at those problems, the thesis puts forward a control algorithm based on evaluation and optimization of granularity distribution. Firstly, the conception of granularity parameters is proposed to describe the continuous granularity distribution. Secondly, by analyzing the screening experiment datas and the corresponding sintering states, the height of material layer and the average permeability index are chosen to build the fuzzy evaluating functions, and then the sample set of granularity parameters and evaluation values can be obtained and BPNN can be trained to build the granularity parameters evaluating model. Thirdly, the optimal granularity parameters could be calculated using PSO algorithm, by taking the granularity parameters evaluating model, granularity parameters and process boundaries as objective function, decision variables and constraints, respectively. Finally, humidity setting model is built to covert the granularity parameters into real-time optimal humidity control setting value in mixing granulation process。The algorithm overcomes the limitation that the conventional humidity control is difficult to realize the granularity distribution optimal control. The simulation shows that the algorithm improves the effection of granulity distribution.
(5) Industrial application of optimal control strategy in IOMP
Based on industrial reality, software and data stream are designed, and then the burden optimization and decision support system is built for a360m2sintering production line to realize the comprehensive optimization of cost, energy consumption and sulfur content reducing, which bring obvious economical and environmental benefits.
(1)基于机理分析和数据驱动的烧结配料过程智能集成建模方法
针对烧结矿质量预测复杂,配料准确率较低而导致烧结矿质量波动的问题,提出一种烧结矿质量级联集成预测模型。首先,分析烧结过程中化学反应物质质变和量变关系,建立烧结矿质量的机理模型；根据灰色关联度分析方法确定影响烧结矿质量的关键参数,并将其区分为已知的配料参数信息和未知的烧结过程状态参数；依据烧结生产稳定性要求,建立T-S模糊融合的GM(1,1)灰色模型和最小二乘支持向量机(LS-SVM)集成模型,获得烧结过程状态参数合理的预测值。在此基础上,分别建立具有全局逼近能力BP神经网络模型(BPNN)和局部泛化能力的LS-SVM模型以预测烧结矿质量,并从信息论的观点出发,提出一种根据预测误差序列的变异程度加权的信息熵融合方法,通过对机理模型、BPNN和LS-SVM模型的预测结果进行加权集成,获得准确的烧结矿质量预测值。仿真结果和实际运行表明级联集成模型的预测精度高于单级预测模型和级联单一预测模型,能够准确预测烧结矿质量,满足配料计算对预测精度和数据完备性的要求。
通过研究焦粉配比与烧结矿质量之间的数值关系,建立以烧结矿质量为约束的焦粉配比优化模型；通过研究烧结过程的主要物理、化学变化,建立基于热平衡的焦粉配比下限模型,从而缩小焦粉配比优化模型的搜索范围,克服了传统经验配比难以实现焦粉用量优化的局限性,为实现烧结配料过程节能目标奠定了基础。
(2)兼顾降低成本与减少硫含量的烧结配料多目标优化模型
针对传统配料方法中存在成本和污染物元素含量偏高,而一般的配料优化模型既缺乏对烧结配料工艺过程的合理描述,也缺乏对能耗、环境效益的关注问题,结合烧结配料中一次配料与二次配料不同的工艺特点和指标要求,在分析烧结过程原料关键化学成分指标和经济性基础上,以库存量和烧结矿化学成分指标为约束条件,建立一种基于线性加权和的烧结配料多目标优化模型(包括一次配料和二次配料多目标优化模型),较传统的配料成本优化模型而言,实现兼顾降低配料成本与减少硫含量的烧结配料多目标优化。
(3)烧结配料多目标综合优化方法
在建立基于线性加权和的烧结配料多目标优化模型的基础上,本文设计基于线性规划(LP)和遗传-粒子群(GA-PSO)的烧结配料多目标综合优化算法,搜索一次配料与二次配料优化模型的最优配比。该算法首先采用LP方法求解基于线性加权和的烧结配料多目标优化模型的最优解,若LP方法计算失效,则采用GA-PSO算法进行搜索,算法搜索初期采用基本粒子群算法(PSO),当PSO收敛停滞,采用遗传算法(GA)的交叉、变异操作增加粒子群的多样性,避免了基本PSO算法收敛末期的振荡特点,实现快速收敛。本文将烧结配料多目标综合优化算法应用工业生产现场,实现烧结配料配比优化。
(4)基于粒度分布评估的混合制粒智能集成优化控制策略
混合料粒度分布影响因素众多,而工艺指标仅对混合料粒度分布进行定性描述。粒度分布优化控制缺乏准确的数学模型和明确的优化目标,采用传统优化方法与控制策略难以实现粒度分布优化控制。针对上述问题,本文提出一种基于粒度分布评估与优化的控制算法。首先,提出粒级参数的概念,将连续的粒度分布用离散的粒级参数表示；其次,分析筛分实验粒级参数与对应混合料的烧结状态,采用料层厚度和平均透气性指数建立模糊评估函数,建立粒级参数与对应评估值的样本集,采用基于BP神经网络方法建立粒级参数的评估模型；再次,以评估模型为目标函数,以粒级参数为决策变量,在生产边界条件的约束下采用PSO算法求解获得最优粒级参数；最后,建立水分设定模型,通过代入最优粒级参数求解得到制粒过程水分优化设定值,克服了传统水分控制难以实现粒度分布优化控制的局限性。仿真实验表明：算法有效改善了制粒效果。
(5)混合料制备过程优化控制策略的工业应用
结合工业现场实际,根据某360m2烧结生产线进行系统软件和数据流设计,建立烧结配料优化与决策支持系统,实现企业兼顾配料成本与减少焦粉用量、降低硫含量目标的综合优化,取得了明显的经济效益和环境效益。
Sintering is one of the important loops in the iron ores smelting process. The burden and mixing granulation belong to Iron Ores Mixture Preparation (IOMP). In actual industrial process, IOMP process has not been optimized enough. In the burden process, the proportions are with low accuracy leading the fluctuation of sintered ore quality, and the high sulfur content and cost cause a low economical and environmental benefit. In mixing granulation process, irrational granularity distribution may cause high energy consumption in sinttering. To deal with the problems, the thesis focuses on intelligent integrated optimization and control strategy in IOMP. Main researches and innovations of the thesis are given below:
(1) An intelligent integrated modeling method for burden process based on mechanism analysis and data-driven method
To deal with the quality fluctuation problem caused by the complexity of sintering quality prediction and low accuracy of ores proportions, a kind of intelligent integrated models with cascade structure for sinter qualities prediction is put forward. Firstly, after analyzing the qualitative and quantitative changes of chemicals in sintering process, the mechanism models of sinter qualities are worked out. On the basis of grey relational analysis, key parameters affecting the quality of sintered ore are found, and then they are separated into two parts:the known proportion information and the unkown sintering states parameters. According to the sintering stability requirements, Integrated T-S fuzzy fusion models, which are consisted by Gray Model (GM(1,1)) and Least Squares Support Vector Machine (LS-SVM), are worked out to give rational prediction values of sintering states papameters. On the above basis, BP Neural Network (BPNN) with global approximation capbility and LS-SVM with local generalization capability are seperatly worked out to predict the qualites of sintered ore. After that, from the viewpoint of information theory, an information entropy fusion method weitghting outputs by the variation degree of prediction error sequence is designed to integrate the outputs of mechanism model, BPNN and LS-SVM, so that the qualites of sintered ore can be predicted correctly. The simulation and industrial applications show that the accuracy of intelligent integrated model with cascade structure is higher than single-stage prediction model or single model with cascade structure. It can predict qualites of sintered ore correctly and satisfy data completeness requirements during burden optimization.
After analyzing the numerical relationship between coke ratio and qualites of sintered ore, a coke proportion optimization model constrainted by qualites of sintered ore is proposed. Then according to the analysis of the main physical and chemical changes in sintering process, a computing model for lower limit of coke proportion based on heat balance is worked out to narrow the searching range of coke in the optimization model. The model can overcome the difficulty of conventional experience-based proportioning method that it is hard to realize coke proportion optimization, and establishes the foundation of energy saving optimization.
(2) A multi-objective optimization model of burden process for cost and sulfur content reducing
In the burdening process, the traditional proportion methods usually is with high cost and sulfur content, and the ordinary optimization models can neither appoarch to the burden process precisely nor consider the cost, energy consumption and environment benifit comprehensively. To deal with those problems, linear weighted multi-objective optimization models of burden process (including primary and secondary proportion optimization models) using inventory and sintered ore chemical constraints, which is based on the different characteristics of primary and secondary proportions as well as the economy and key chemical components analysises of different materials, are proposed in the thesis. The optimization models could realize the comprehensive optimization of reducing cost and sulfur content compared with conventional proportion cost optimization model.
(3) Multi-objective comprehensive optimization algorithm of burden process
Based on the linear weighted multi-object optimization models of burden process, a multi-objective comprehensive optimization algorithm, which is integrated Linear Program (LP) with Genetic Algorithm and Particle Swarm Optimization (GA-PSO), is proposed to search the optimal solutions of primary and secondary proportions in the thesis. Firstly, LP method is applied to solve the linear weighted multi-object optimization models. If it does not work, GA-PSO algorithm is implemented. In this algorithm, Particle Swarm Optimization (PSO) algorithm is used during the early period. When PSO stops converging, the operations of crossing and mutations from Genetic Algorithm (GA) are executed with a certain probability to increase diversity of particle group, which avoids the fluctuation in late convergence and increase the convergence rate. The multi-objective comprehensive optimization algorithm is realized in actual industrial application to optimize the burden process.
(4) Intelligent integrated optimal control algorithm for mixing granulation based on evaluation of granularity distribution
The factors effecting granularity distributions of iron ores mixture are various, while the process index of granularity distributions is quantitative. Hence, the granularity distribution optimal control lacks of accurate mathmatics models and clear targets, the process can hardly be optimized by using traditional optimal control strategies. Aimed at those problems, the thesis puts forward a control algorithm based on evaluation and optimization of granularity distribution. Firstly, the conception of granularity parameters is proposed to describe the continuous granularity distribution. Secondly, by analyzing the screening experiment datas and the corresponding sintering states, the height of material layer and the average permeability index are chosen to build the fuzzy evaluating functions, and then the sample set of granularity parameters and evaluation values can be obtained and BPNN can be trained to build the granularity parameters evaluating model. Thirdly, the optimal granularity parameters could be calculated using PSO algorithm, by taking the granularity parameters evaluating model, granularity parameters and process boundaries as objective function, decision variables and constraints, respectively. Finally, humidity setting model is built to covert the granularity parameters into real-time optimal humidity control setting value in mixing granulation process。The algorithm overcomes the limitation that the conventional humidity control is difficult to realize the granularity distribution optimal control. The simulation shows that the algorithm improves the effection of granulity distribution.
(5) Industrial application of optimal control strategy in IOMP
Based on industrial reality, software and data stream are designed, and then the burden optimization and decision support system is built for a360m2sintering production line to realize the comprehensive optimization of cost, energy consumption and sulfur content reducing, which bring obvious economical and environmental benefits.
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