基于混匀矿烧结基础特性配矿专家系统的开发与应用
|
摘要
应用BP神经网络技术分别建立混匀矿烧结基础特性预报模型和烧结矿质量预报模型。采用Visual C++2010与Matlab 2008混合编程的方式,开发了烧结配矿专家系统软件。结果表明:同化性温度、液相流动性指数和粘结相强度的预报命中率分别为90%、83.3%和90%,成品率、转鼓指数和低温还原粉化RDI_(+3.15)的预报命中率分别为90%、90%和85%。通过专家系统研究了褐铁矿配比对混匀矿烧结基础特性和烧结矿质量的影响,随褐铁矿配比的增加,烧结矿的成品率、转鼓指数和低温还原粉化RDI_(+3.15)先升高后降低。当混匀矿的同化温度、液相流动性指数和粘结相强度分别为1 259℃、1.22和1 727 N时,烧结矿的质量指标最优。
The BP neural network technology is used to establish the prediction model of sintering basic characteristics and the prediction model of sinter quality. Using C + + Visual 2010 and Matlab 2008 mixed programming,the expert system software was developed. Results show: The prediction hit rate of the assimilation temperature,the liquid phase mobility index and the bond strength were 90%,83. 3% and 90%,respectively. The prediction hit rate of production yield,tumbler index,low temperature reduction degradation RDI_(+3.15) were 90%,90% and 85%,respectively. It has been researched by expert system that the influence of the ratio of limonite on the sintering basic characteristics and sinter quality. With the increasing proportion of limonite,sinter yield,tumbler index and low temperature reduction degradation RDI_(+3.15) first increased and then decreased.When the assimilation temperature,the liquid phase mobility index and the bond strength were 1 259 ℃,1. 22 and 1 727 N,respectively. The quality index of sinter was the best.
The BP neural network technology is used to establish the prediction model of sintering basic characteristics and the prediction model of sinter quality. Using C + + Visual 2010 and Matlab 2008 mixed programming,the expert system software was developed. Results show: The prediction hit rate of the assimilation temperature,the liquid phase mobility index and the bond strength were 90%,83. 3% and 90%,respectively. The prediction hit rate of production yield,tumbler index,low temperature reduction degradation RDI_(+3.15) were 90%,90% and 85%,respectively. It has been researched by expert system that the influence of the ratio of limonite on the sintering basic characteristics and sinter quality. With the increasing proportion of limonite,sinter yield,tumbler index and low temperature reduction degradation RDI_(+3.15) first increased and then decreased.When the assimilation temperature,the liquid phase mobility index and the bond strength were 1 259 ℃,1. 22 and 1 727 N,respectively. The quality index of sinter was the best.
引文
[1]史俊,郭汉杰.临钢高比例进口矿粉的烧结配矿研究[J].山西冶金.2009,121(5):12.
[2]程国彪,高丙寅,张春,等.大比例配用进口粉矿的烧结试验及生产[J].烧结球团,2004,29(1):8-9.
[3]刘自民,吴胜利,金俊,宋灿阳,朱娟.马钢烧结优化配矿技术的研究[J].烧结球团,2012,37(2):13-18.
[4]DAUTER OLIVEIRA,WU Sheng Li,DAI Yu Ming,XU Jian,CHEN Hong.Sintering Properties and Optimal Blending Schemes of Iron Ores[J].Journal of iron and steel research,international,2008,19(6):1-5.
[5]UENO N,NAKAGAWA Y,TANIMIZU K,et al.Management and control systems for raw materials operation in iron steel industries[C]//Automation in Mining,Mineral and Metal Processing.1986:473-478.
[6]李智,姚驻斌.免疫遗传算法在烧结矿配料优化中的应用[J].上海金属,2004,26(6):23-28.
[7]范晓慧,甘敏,李文琦,等.烧结混合料适宜制粒水分的预测[J].北京科技大学学报,2012,34(4):373-377.
[8]韩庆虹,金永龙,张军红.人工神经网络在烧结固体燃耗预测中的应用[J].冶金能源,2005,24(2):9-11.
[9]LAITINEN P.J,SAXEN H.A neural network based model of sinter quality and sinter plant performance indices[J].Ironmaking and Steelmaking,2007,34(2):109-114.
[10]吕学伟,白晨光,邱贵宝,等.三种优化烧结配料方法的比较[J].烧结球团,2006,31(2):11-15.
[11]袁晓丽,范晓慧,万新,等.烧结优化配矿模型的设计与软件开发[J].中南大学学报(自然科学版),2009,40(6):1476-1481.
[12]苏高利,邓芳萍.论基于MATLAB语言的BP神经网络的改进算法[J].科技通报,2003,19(2):130-135.
[13]贺星,刘永葆,孙丰瑞.基于改进Levenberg-Marquardt算法的燃气轮机特性拟合优化[J].海军工程大学学报,2012,24(4):37-38.
[14]于勇,陶文,姚志超,李士琦.冀东铁精粉烧结成矿过程的剖析[J].钢铁研究学报,2003,15(6):1-5.
[15]王荣成,傅菊英.高铁低硅烧结技术研究[J].钢铁,2007,42(6):18-20.
[16]刘东辉,吕庆,邹雷雷,等.褐铁矿配比对钒钛磁铁矿烧结基础特性的影响[J].钢铁,2014,49(4):13-17.L
[17]裴元东,赵志星,马泽军,安钢,赵勇,潘文.高比例褐铁矿烧结机理分析及试验研究[J].烧结球团,2011,36(5):1.
[18]吕庆,刘东辉,邹雷雷,陈树军.褐铁矿配比对钒钛磁铁烧结矿性能的影响[J].钢铁钒钛,2014,35(5):78-82.
[2]程国彪,高丙寅,张春,等.大比例配用进口粉矿的烧结试验及生产[J].烧结球团,2004,29(1):8-9.
[3]刘自民,吴胜利,金俊,宋灿阳,朱娟.马钢烧结优化配矿技术的研究[J].烧结球团,2012,37(2):13-18.
[4]DAUTER OLIVEIRA,WU Sheng Li,DAI Yu Ming,XU Jian,CHEN Hong.Sintering Properties and Optimal Blending Schemes of Iron Ores[J].Journal of iron and steel research,international,2008,19(6):1-5.
[5]UENO N,NAKAGAWA Y,TANIMIZU K,et al.Management and control systems for raw materials operation in iron steel industries[C]//Automation in Mining,Mineral and Metal Processing.1986:473-478.
[6]李智,姚驻斌.免疫遗传算法在烧结矿配料优化中的应用[J].上海金属,2004,26(6):23-28.
[7]范晓慧,甘敏,李文琦,等.烧结混合料适宜制粒水分的预测[J].北京科技大学学报,2012,34(4):373-377.
[8]韩庆虹,金永龙,张军红.人工神经网络在烧结固体燃耗预测中的应用[J].冶金能源,2005,24(2):9-11.
[9]LAITINEN P.J,SAXEN H.A neural network based model of sinter quality and sinter plant performance indices[J].Ironmaking and Steelmaking,2007,34(2):109-114.
[10]吕学伟,白晨光,邱贵宝,等.三种优化烧结配料方法的比较[J].烧结球团,2006,31(2):11-15.
[11]袁晓丽,范晓慧,万新,等.烧结优化配矿模型的设计与软件开发[J].中南大学学报(自然科学版),2009,40(6):1476-1481.
[12]苏高利,邓芳萍.论基于MATLAB语言的BP神经网络的改进算法[J].科技通报,2003,19(2):130-135.
[13]贺星,刘永葆,孙丰瑞.基于改进Levenberg-Marquardt算法的燃气轮机特性拟合优化[J].海军工程大学学报,2012,24(4):37-38.
[14]于勇,陶文,姚志超,李士琦.冀东铁精粉烧结成矿过程的剖析[J].钢铁研究学报,2003,15(6):1-5.
[15]王荣成,傅菊英.高铁低硅烧结技术研究[J].钢铁,2007,42(6):18-20.
[16]刘东辉,吕庆,邹雷雷,等.褐铁矿配比对钒钛磁铁矿烧结基础特性的影响[J].钢铁,2014,49(4):13-17.L
[17]裴元东,赵志星,马泽军,安钢,赵勇,潘文.高比例褐铁矿烧结机理分析及试验研究[J].烧结球团,2011,36(5):1.
[18]吕庆,刘东辉,邹雷雷,陈树军.褐铁矿配比对钒钛磁铁烧结矿性能的影响[J].钢铁钒钛,2014,35(5):78-82.