SVM和CBR的建模研究及其在转炉炼钢过程的应用
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摘要
炼钢是钢铁工业的重要生产过程,而转炉炼钢是最主要的炼钢生产方式。随着模型技术和计算机技术的不断发展,转炉炼钢生产的自动化水平得到不断提高。转炉炼钢二级过程控制是衡量生产自动化水平并关系冶炼钢水质量的关键环节,然而,因我国炼钢生产工艺和原料质量等方面的限制,国外引进的模型系统无法达到预期效果。因此,建立适合我国国情的转炉炼钢二级过程控制模型,对提高我国转炉炼钢生产自动化水平具有重要意义。本文以基于副枪的碱性氧气顶吹转炉为研究对象,针对转炉炼钢生产二级过程控制中所要涉及的模型展开系统深入地研究。本文主要包括以下内容：
根据转炉炼钢生产对模型的实际需求情况,将所研究模型划分为三类,即主吹阶段控制模型,二吹阶段控制模型和预报模型。分别针对基于副枪的转炉炼钢生产中的三个关键问题开展相关的建模研究工作。
1)提出基于支持向量机和案例推理的主吹阶段控制模型。针对主吹阶段石灰加入量问题,首先提出基于支持向量机的碱度偏差估计模型,用于估计炉渣实际碱度与目标碱度的偏差,进一步,将对碱度偏差的估计结果引入传统的石灰加入量计算公式,实现对石灰加入量计算的改进；针对主吹阶段氧气吹入量的计算,提出基于混合相似度案例检索的案例推理模型,在案例检索过程中采用基于几何相似和最近邻方法的混合相似度案例检索方法,对于不同的案例检索策略,分别采用加权求和方法和增量回归方法对相似案例进行重用,使所建立的模型能够充分挖掘数据中不同特性的信息。
2)提出基于混合智能方法的二吹阶段控制模型。针对二吹阶段冷却剂加入量问题,首先提出基于支持向量机的冷却剂加入决策模型,确定是否需要加入冷却剂。如果需要加入,则建立基于支持向量机分类和基于案例推理回归的组合模型计算冷却剂加入量,以融合不同模型的优势；在对二吹阶段氧气量吹入量模型的研究中,提出基于动态案例库的案例库维护策略,同时,在案例检索过程中引入奖惩措施以改善相似度计算过程,以提高相似案例检索的精度和模型的精度。
3)提出基于支持向量机和微粒群的熔池碳含量和温度预报模型。主要包括吹炼终点碳温预报模型、主吹阶段熔池碳温实时预报模型和二吹阶段熔池碳温实时预报模型。针对高维空间数据密度随维数增长而指数下降的问题,提出基于机理分析和互信息计算的变量选择策略,在此基础上,建立基于支持向量机的终点碳温预报模型,模型中所涉及参数使用微粒群方法确定,以提高模型精度；主吹阶段熔池碳温实时预报模型的建立以理论脱碳模型为基础,首先建立脱碳模型中关键参数与生产条件之间的关系,进而使用确定的参数建立脱碳模型,得到主吹阶段熔池碳含量的实时预报结果。主吹阶段熔池实时温度的预报描述为实时碳含量的函数,最终建立主吹阶段熔池碳温的实时预报模型；二吹阶段熔池实时碳含量和温度预报模型基于理论模型和案例推理方法建立,使用案例推理方法确定理论模型中的参数,实现各炉次模型参数的自学习,在实现二吹阶段熔池碳温实时预报的同时,实现对吹炼终点的控制。
Steelmaking production is an important process in steel industry and basic oxygen furnace (BOF) steelmaking is the most popular mode. With the development of modeling and computer technology, BOF steelmaking production automation is continuously improved. Process control of BOF steelmaking reflects the automation level and affects the quality of steel. However, due to constraints of steel production process and quality of raw material, the imported model systems can not achieve the desired results. Therefore, modeling suitable models for national conditons has great significance to improve the level of automation of BOF steel production. Sub-lance based basic oxygen furnace is selected as the object of study and the models related to the process control of BOF are deeply researched in this paper. The main contents of this paper are as follows:
1) Support vector machine (SVM) and case based reasoning (CBR) based control models in main blow phase are researched. For lime addition amount control, an alkalinity deviation estimation model based on SVM is proposed to evaluate the difference between actual alkalinity and aim alkalinity of slag. Further more, traditional lime addition calculation formula is modified by introducing the alkalinity deviation; for the problem of oxygen blow volume control, a case-based reasoning model with mixed case retrieve and case reuse is proposed, k nearest neighborhood and geometrical similarity methods are used in case retrieve step and correspond to the weighted sum and incremental regression in reuse step to adequately mine the information from different aspects.
2) The control models based on hybrid intelligent methods in second blowing stage are researched. For the coolant addition amount, SVM based decision model is built to determine whether the coolant is needed. If needed, SVM and CBR based combine model is adopted to calculate the coolant addition amount. For oxygen volume control, a dynamic case base strategy is applied and Bayesian rewards and punishment are introduced when calculating the similarity between current case and history case to improve the accuracy of case retrieval.
3) SVM and particle swarm optimal (PSO) based carbon content and temperature prediction models mainly contain the endpoint prediction and real time prediction are researched. Mechanism analysis and mutual information calculation based variable selection are proposed to choose appropriate input variables for endpoint carbon content and temperature prediction. Then the SVM method is used for modeling and predicts the endpoint. In main blow stage, bath carbon content and temperature real-time forecasting model is built based on theory decarburization model. The second blowing bath carbon and temperature prediction models are based on experience model and CBR model. The real-time bath carbon content and temperature prediction can also realize the endpoint control.
根据转炉炼钢生产对模型的实际需求情况,将所研究模型划分为三类,即主吹阶段控制模型,二吹阶段控制模型和预报模型。分别针对基于副枪的转炉炼钢生产中的三个关键问题开展相关的建模研究工作。
1)提出基于支持向量机和案例推理的主吹阶段控制模型。针对主吹阶段石灰加入量问题,首先提出基于支持向量机的碱度偏差估计模型,用于估计炉渣实际碱度与目标碱度的偏差,进一步,将对碱度偏差的估计结果引入传统的石灰加入量计算公式,实现对石灰加入量计算的改进；针对主吹阶段氧气吹入量的计算,提出基于混合相似度案例检索的案例推理模型,在案例检索过程中采用基于几何相似和最近邻方法的混合相似度案例检索方法,对于不同的案例检索策略,分别采用加权求和方法和增量回归方法对相似案例进行重用,使所建立的模型能够充分挖掘数据中不同特性的信息。
2)提出基于混合智能方法的二吹阶段控制模型。针对二吹阶段冷却剂加入量问题,首先提出基于支持向量机的冷却剂加入决策模型,确定是否需要加入冷却剂。如果需要加入,则建立基于支持向量机分类和基于案例推理回归的组合模型计算冷却剂加入量,以融合不同模型的优势；在对二吹阶段氧气量吹入量模型的研究中,提出基于动态案例库的案例库维护策略,同时,在案例检索过程中引入奖惩措施以改善相似度计算过程,以提高相似案例检索的精度和模型的精度。
3)提出基于支持向量机和微粒群的熔池碳含量和温度预报模型。主要包括吹炼终点碳温预报模型、主吹阶段熔池碳温实时预报模型和二吹阶段熔池碳温实时预报模型。针对高维空间数据密度随维数增长而指数下降的问题,提出基于机理分析和互信息计算的变量选择策略,在此基础上,建立基于支持向量机的终点碳温预报模型,模型中所涉及参数使用微粒群方法确定,以提高模型精度；主吹阶段熔池碳温实时预报模型的建立以理论脱碳模型为基础,首先建立脱碳模型中关键参数与生产条件之间的关系,进而使用确定的参数建立脱碳模型,得到主吹阶段熔池碳含量的实时预报结果。主吹阶段熔池实时温度的预报描述为实时碳含量的函数,最终建立主吹阶段熔池碳温的实时预报模型；二吹阶段熔池实时碳含量和温度预报模型基于理论模型和案例推理方法建立,使用案例推理方法确定理论模型中的参数,实现各炉次模型参数的自学习,在实现二吹阶段熔池碳温实时预报的同时,实现对吹炼终点的控制。
Steelmaking production is an important process in steel industry and basic oxygen furnace (BOF) steelmaking is the most popular mode. With the development of modeling and computer technology, BOF steelmaking production automation is continuously improved. Process control of BOF steelmaking reflects the automation level and affects the quality of steel. However, due to constraints of steel production process and quality of raw material, the imported model systems can not achieve the desired results. Therefore, modeling suitable models for national conditons has great significance to improve the level of automation of BOF steel production. Sub-lance based basic oxygen furnace is selected as the object of study and the models related to the process control of BOF are deeply researched in this paper. The main contents of this paper are as follows:
1) Support vector machine (SVM) and case based reasoning (CBR) based control models in main blow phase are researched. For lime addition amount control, an alkalinity deviation estimation model based on SVM is proposed to evaluate the difference between actual alkalinity and aim alkalinity of slag. Further more, traditional lime addition calculation formula is modified by introducing the alkalinity deviation; for the problem of oxygen blow volume control, a case-based reasoning model with mixed case retrieve and case reuse is proposed, k nearest neighborhood and geometrical similarity methods are used in case retrieve step and correspond to the weighted sum and incremental regression in reuse step to adequately mine the information from different aspects.
2) The control models based on hybrid intelligent methods in second blowing stage are researched. For the coolant addition amount, SVM based decision model is built to determine whether the coolant is needed. If needed, SVM and CBR based combine model is adopted to calculate the coolant addition amount. For oxygen volume control, a dynamic case base strategy is applied and Bayesian rewards and punishment are introduced when calculating the similarity between current case and history case to improve the accuracy of case retrieval.
3) SVM and particle swarm optimal (PSO) based carbon content and temperature prediction models mainly contain the endpoint prediction and real time prediction are researched. Mechanism analysis and mutual information calculation based variable selection are proposed to choose appropriate input variables for endpoint carbon content and temperature prediction. Then the SVM method is used for modeling and predicts the endpoint. In main blow stage, bath carbon content and temperature real-time forecasting model is built based on theory decarburization model. The second blowing bath carbon and temperature prediction models are based on experience model and CBR model. The real-time bath carbon content and temperature prediction can also realize the endpoint control.
引文
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