基于TSSA-SVR模型的焦炭质量预测模型研究  

Research on coke quality prediction model based on TSSA-SVR model

作　　者：暴子旗 卢才武[1,2] 章赛 宋思远[1,2] BAO Ziqi;LU Caiwu;ZHANG Sai;SONG Siyuan(School of Resources Engineering,Xi an University of Architecture and Technology,Xi an 710055,China;Xi an Key Laboratory of Perceptual Computing and Decision Making for Intelligent Industry,Xi an University of Architecture and Technology,Xi an 710055,China)

机构地区：[1]西安建筑科技大学资源工程学院,陕西西安710055 [2]西安市智慧工业感知计算与决策重点实验室,陕西西安710055

出　　处：《中国矿业》2022年第6期86-92,共7页China Mining Magazine

基　　金：陕西省自然科学基础研究计划联合基金项目资助(编号:2019JLP-16)。

摘　　要：焦炭质量对高炉冶炼的生产有着极大影响,建立精确度高、适应性好的焦炭质量预测模型对企业生产具有重要意义。为解决生产过程中焦炭质量难以实时测量的问题,提出一种基于混沌麻雀搜索算法(TSSA)优化支持向量回归机(SVR)的焦炭质量预测模型。首先采用改进Tent混沌映射初始化种群,加强麻雀搜索算法(SSA)的全局搜索能力,然后利用TSSA模型对SVR模型的参数进行优化,有效克服了传统SVR模型的参数选取问题。选取配合煤中的水分、灰分、挥发分等七项指标作为模型的输入,焦炭质量中的抗碎强度、耐磨强度、反应性、反应后强度四项指标作为模型的输出,依据焦化厂历史生产数据,对TSSA-SVR模型进行实例验证,并与SSA-SVR模型、SVR模型进行对比分析,实验结果表明,提出的方法具有较好的准确度和适应性,对焦炭生产具有一定的实用价值。The quality of coke has a great impact on the production of blast furnace smelting.The establishment of coke quality prediction model with high accuracy and good adaptability is of great significance to enterprise production,in order to solve the problem that it is difficult to measure coke quality in real time,a coke quality prediction model based on chaotic sparrow search algorithm(TSSA)and optimized support vector regression(SVR)is proposed.Firstly,the improved Tent chaotic map is used to initialize the population and strengthen the global search ability of sparrow search algorithm(SSA),and then TSSA model is used to optimize the parameters of SVR model,which effectively overcomes the problem of parameter selection of traditional SVR model.Seven indexes such as moisture,ash and volatile in blended coal are selected as the input of the model,and the four indexes of coke quality,including crushing strength,wear resistance,reactivity index and strength after reaction,are selected as the output of the model,the TSSA-SVR model is verified according to the historical production data of coking plant,and compared with SSA-SVR and SVR models.The experimental results show that the proposed method has good accuracy and adaptability,and has certain practical value for coke production.

关 键 词：焦炭质量 Tent混沌映射 麻雀搜索算法 支持向量回归机 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]