能源预测及能源优化技术在冶金企业中的应用研究
摘要
近些年来冶金工业快速发展,由于这一行业生产耗能高的特点,它已成为现代工业企业中的耗能大户。中钢集团吉林铁合金公司是国内冶金行业的重要骨干企业,是全国最大、品种最多的铁合金生产和供应基地。在铁合金生产过程中,主要的耗能设备为矿热炉。矿热炉是一个复杂的系统,其冶炼过程具有时变性、非线性和强耦合特点,三相电压、三相弧流设定点的不同都会对电弧长度产生影响。因此,选择合理的矿热炉供电策略能够有效的降低消耗、电极损耗和耐材侵蚀,缩短冶炼周期,从而降低冶炼成本、提高生产率。
常规的供电策略制定通常为如何确定工作电流,而在矿热炉的冶炼过程中,冶炼周期和铁合金每吨电耗及电弧功率均有着密切的关系。因此,合理的供电策略的制定不仅仅是工作电流的选择问题。本文在查阅了大量国内外相关文献的基础上,以中钢集团吉林铁合金公司冶炼钨铁合金的生产过程为背景,针对冶炼过程中的不同工况特点把握有利的加热条件：首先对矿热炉生产工艺进行分析,采用支持向量机的方法,建立了矿热炉的炉况判断模型,并根据炉内能量的状况给出了熔化期四个阶段的冶炼进程估计；然后基于能量平衡方程建立熔化期的能量输入优化模型,针对不同的炉况,根据能量输入优化指标,采用遗传算法进行工作电流、电压以及电抗的选取,得到一种合理的供电策略,进而实现矿热炉输入能量的优化。通过对吉林铁合金股份有限公司404#炉生产钨铁合金的40炉次实验数据进行对比的,结果验证了本文所提方法的有效性。
由于在矿热炉冶炼过程中采用了输入能量优化技术,从整体上提高了矿热炉冶炼过程自动化程度,提高了能源信息管理水平,取得了显著的经济效益和社会效益。更为重要的是,通过本课题研究,摸索和积累了一定经验,为复杂工业过程的建模及优化控制提供了一套实用的、值得借鉴的工业节能实现方法。
The metallurgical industry has developed rapidly in the last few years. it has already become the big energy eaters in the modern industrial enterprise by its various power consume equipment and the higher quality demand of electric energy. Jilin Ferroalloy Company is the important leading enterprise of the domestic metallurgical trade, and especially the largest ferroalloy production factory. The main dissipation energy equipment is a hot stove of ore. The smelting course of ore is a complicated system, characterized by the non-linear and strong coupling. Three-phase voltage and three-phase have some impacts on length of electric arc. So, the reasonable power supply strategy can reduce the production efficiency, consumption of resources and electrode loss. The smelting cycle is shortened and the production efficiency can be raised.
Routine power supply tactics is to confirm the work electric current. But in the course of smelting of ore, smelting cycle has a close relation with the power of electric arc and each ton electricity of ferroalloy. So the decision of the rational power supply tactics is not merely the choice problem of the working electric current. regarding the production process of the ferroalloy subsidiary factory of Jilin steel group, this paper analyze the favorable heating condition to smelting in different operating mode, establish the hot stove condition judgment model and provide the estimation of four stages of melting stage of state of energy in the stove according to the smelting process through the method of vector quantity supporting machine. Based on the model of optimization of the balanced, melting stage of equation of energy is set up. For different stove conditions and the index of energy optimizing, the genetic algorithm is adopted to choose job electric current, voltage and reactance. Rational power supply tactics is got and then the hot stove of ore imports is optimized. The result of the test has verified the validity of the method that this paper proposed.
For adopting introduction energy optimization technology in the production process of, hot stove, it has improve the automatic degree of smelt course, strengthened energy information managerial ability, made the remarkable economic benefits and social benefit. The more important thing is that through this subject research, certain experience is accumulated and industrialized implementation methods of model and optimization of complicated industry process is presented in this paper which can be a favorable reference.
常规的供电策略制定通常为如何确定工作电流,而在矿热炉的冶炼过程中,冶炼周期和铁合金每吨电耗及电弧功率均有着密切的关系。因此,合理的供电策略的制定不仅仅是工作电流的选择问题。本文在查阅了大量国内外相关文献的基础上,以中钢集团吉林铁合金公司冶炼钨铁合金的生产过程为背景,针对冶炼过程中的不同工况特点把握有利的加热条件：首先对矿热炉生产工艺进行分析,采用支持向量机的方法,建立了矿热炉的炉况判断模型,并根据炉内能量的状况给出了熔化期四个阶段的冶炼进程估计；然后基于能量平衡方程建立熔化期的能量输入优化模型,针对不同的炉况,根据能量输入优化指标,采用遗传算法进行工作电流、电压以及电抗的选取,得到一种合理的供电策略,进而实现矿热炉输入能量的优化。通过对吉林铁合金股份有限公司404#炉生产钨铁合金的40炉次实验数据进行对比的,结果验证了本文所提方法的有效性。
由于在矿热炉冶炼过程中采用了输入能量优化技术,从整体上提高了矿热炉冶炼过程自动化程度,提高了能源信息管理水平,取得了显著的经济效益和社会效益。更为重要的是,通过本课题研究,摸索和积累了一定经验,为复杂工业过程的建模及优化控制提供了一套实用的、值得借鉴的工业节能实现方法。
The metallurgical industry has developed rapidly in the last few years. it has already become the big energy eaters in the modern industrial enterprise by its various power consume equipment and the higher quality demand of electric energy. Jilin Ferroalloy Company is the important leading enterprise of the domestic metallurgical trade, and especially the largest ferroalloy production factory. The main dissipation energy equipment is a hot stove of ore. The smelting course of ore is a complicated system, characterized by the non-linear and strong coupling. Three-phase voltage and three-phase have some impacts on length of electric arc. So, the reasonable power supply strategy can reduce the production efficiency, consumption of resources and electrode loss. The smelting cycle is shortened and the production efficiency can be raised.
Routine power supply tactics is to confirm the work electric current. But in the course of smelting of ore, smelting cycle has a close relation with the power of electric arc and each ton electricity of ferroalloy. So the decision of the rational power supply tactics is not merely the choice problem of the working electric current. regarding the production process of the ferroalloy subsidiary factory of Jilin steel group, this paper analyze the favorable heating condition to smelting in different operating mode, establish the hot stove condition judgment model and provide the estimation of four stages of melting stage of state of energy in the stove according to the smelting process through the method of vector quantity supporting machine. Based on the model of optimization of the balanced, melting stage of equation of energy is set up. For different stove conditions and the index of energy optimizing, the genetic algorithm is adopted to choose job electric current, voltage and reactance. Rational power supply tactics is got and then the hot stove of ore imports is optimized. The result of the test has verified the validity of the method that this paper proposed.
For adopting introduction energy optimization technology in the production process of, hot stove, it has improve the automatic degree of smelt course, strengthened energy information managerial ability, made the remarkable economic benefits and social benefit. The more important thing is that through this subject research, certain experience is accumulated and industrialized implementation methods of model and optimization of complicated industry process is presented in this paper which can be a favorable reference.
引文
[1]史丹.我国能源供需的演变趋势与能源发展的战略重点《能源发展战略研究》“十五”国家高科技发展计划能源技术领域专家委员会编著[D].化学工业出版社,2004.
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[8]邱绍岐,祝桂华.电炉炼钢原理与工艺[M].北京,冶金工业出版社,2001：6-8.
[9]B. bowmna,谷玉和译,世界电工会议论文[J].4A,02：23-27.
[10]张琦,蔡九菊等.冶金工业副产煤气的高效利用[J].中国冶金,2006,3：44-47.
[11]张传秀,倪晓峰.浅谈高炉煤气发电的经济效益、环境效益和社会效益[J].冶金环境保护,2005,1：17-23.
[12]张玉柱,胡长庆,炼铁节能与工艺计算[M].冶金工业出版社,2005.
[13]冯为民、丛力群,冶金企业能源管理系统[J].控制工程,2005,12(6)：597-600.
[14]张琦,蔡九菊等.钢铁企业煤气平衡问题的探讨[J].节能,2004,12：76-79.
[15]杨若仪,刘文和.低热值煤气燃气轮机联合循环发电技术在钢铁厂的应用[J].钢铁技术,2004,2：19-24.
[16]中国钢铁工业协会.中国钢铁工业生产统计指标体系指标解释[M].冶金工业出版社,2003.
[17]刘卫,铁合金生产[M].冶金工业出版社,2005.1.
[18]邱绍岐,祝桂华.电炉炼钢原理与工艺[M].冶金工业出版社,2001.
[19]俞海明,段建勇,牛系浩,王新成,李栋.降低电弧炉冶炼铁耗的工艺实践[J].特殊钢,2002,23(3)：38-40.
[20]栾心汉,唐琳,李小明,候苏波.铁合金生产节能及精炼技术[M].西北工业大学出版社,2006.6.
[21]陈光等,钢铁工业能源优化模型及应用[J].华东冶金学院学报,1989,6：43-51.
[22]Cherkassky V, Mulier F. Learning from Data:Concepts[M]. Theory and Methods. NY:John Viley &Sons,1997.
[23]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1)：32-42.
[24]彭文季,郭鹏程,罗兴錡,基于最小二乘支持向量机和信息融合技术的水电机组 振动故障诊断研究[J].2007.26(6)：137-142.
[25]张英,苏宏业,褚健.基于模糊最小二乘支持向量机的软测量建模[J].控制与决策,2004,20(6)：621-624.
[26]周王民,马戎,基于FNN的电弧炉路况判断系统研究[J].测控技术。2007,26(3)：46-49.
[27]Boneh D, Franklin M. Identity Based Encryption from the Weil Pairing[J].SIAMJ. of Computing,2003,32(3):586-615.
[28]Melgani F, Bruzzone L. Classification of hyper spectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.
[29]傅杰.现代电弧炉冶炼周期综合控制理论及应用[J].北京科技大学学报,2004,26(6)：588-595.
[30]邓开楠.基于专家系统的电弧炉炉况判断方法研究[D].东北大学,2005.
[31]曲飞,吴敏,曹卫华,何勇.基于支持向量机的高炉炉况诊断方法[J].钢铁.2007.10.42：17-19.
[32]韩颖,毛志忠.电弧炉能量输入模型的研究[D].东北大学.
[33]顾根华等.考虑运行电抗与热平衡的电弧炉合理供电[J].加热设备,2001.1：32-37.
[34]Dua. V. etl. An. Outer-Apporxlmation Algorithm for the Solution of Multiparametric MINLP Problems [J]. Computers & Chemieal Engineering,1998, 22(1):955-958.
[35]Skrifvars, Hans etl. Comparison of certain MINLP Algorithms when applied to a model structure determination and Parameter estimation Problem[J]. Computers & Chemical Engineering,1998,22(12):1829-1835.
[36]K. mycernadGPost. Poreess. Technology of steel making from Sponge Iorn in the Eleertic Aer Funrace[J]. Stahl und Eisen,1993:194-204.
[37]袁平,王福利.电弧炉冶炼过程先进控制方法的研究与应用[D].东北大学.
[38]武骏,孙彦辉,李京社,李士琦.现代交流电弧炉电气运行合理化的研究[J].钢铁,1999,34(9)：19-22.
[39]尹洪超等.化工过程综合混合离散非线性规划的新算法明[J].大连理工大学学报,1995,35(5)：633-638.
[40]韩力群.人工神经网络理论、设计及应用—人工神经细胞、人工神经网络和人工神经系统[M].北京：化学工业出版社,2002.
[41]孙翎,张金隆,迟嘉显.基于粗糙集的CBR系统案例特征项权值确定[J].计算机工程与应用,2003,30：44-46.
[2]周庆安.国内外钢铁企业节能水平对比分析[J].上海节能,2006.N0.1：25-32.
[3]丁永昌,徐曾启,徐原久.电炉钢节电技术[M].湖北人民出版社,1981.
[4]孔昭庆.我国钨工业现状与可持续发展战略取向[J].《中国钨业》,2007,5：53-57.
[5]胡名洋.中国特钢的未来之路[J].《中国冶金报》,2004年5月,第五版.
[6]孔昭庆.继往开来,开拓创新,为全面振兴钨业而奋斗[J].《中国钨工业年鉴》,2007年.
[7]周群,庞晓波.吉林钨钼铁合金股份有限公司发展战略研究[F].吉林大学,2005.5.
[8]邱绍岐,祝桂华.电炉炼钢原理与工艺[M].北京,冶金工业出版社,2001：6-8.
[9]B. bowmna,谷玉和译,世界电工会议论文[J].4A,02：23-27.
[10]张琦,蔡九菊等.冶金工业副产煤气的高效利用[J].中国冶金,2006,3：44-47.
[11]张传秀,倪晓峰.浅谈高炉煤气发电的经济效益、环境效益和社会效益[J].冶金环境保护,2005,1：17-23.
[12]张玉柱,胡长庆,炼铁节能与工艺计算[M].冶金工业出版社,2005.
[13]冯为民、丛力群,冶金企业能源管理系统[J].控制工程,2005,12(6)：597-600.
[14]张琦,蔡九菊等.钢铁企业煤气平衡问题的探讨[J].节能,2004,12：76-79.
[15]杨若仪,刘文和.低热值煤气燃气轮机联合循环发电技术在钢铁厂的应用[J].钢铁技术,2004,2：19-24.
[16]中国钢铁工业协会.中国钢铁工业生产统计指标体系指标解释[M].冶金工业出版社,2003.
[17]刘卫,铁合金生产[M].冶金工业出版社,2005.1.
[18]邱绍岐,祝桂华.电炉炼钢原理与工艺[M].冶金工业出版社,2001.
[19]俞海明,段建勇,牛系浩,王新成,李栋.降低电弧炉冶炼铁耗的工艺实践[J].特殊钢,2002,23(3)：38-40.
[20]栾心汉,唐琳,李小明,候苏波.铁合金生产节能及精炼技术[M].西北工业大学出版社,2006.6.
[21]陈光等,钢铁工业能源优化模型及应用[J].华东冶金学院学报,1989,6：43-51.
[22]Cherkassky V, Mulier F. Learning from Data:Concepts[M]. Theory and Methods. NY:John Viley &Sons,1997.
[23]张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1)：32-42.
[24]彭文季,郭鹏程,罗兴錡,基于最小二乘支持向量机和信息融合技术的水电机组 振动故障诊断研究[J].2007.26(6)：137-142.
[25]张英,苏宏业,褚健.基于模糊最小二乘支持向量机的软测量建模[J].控制与决策,2004,20(6)：621-624.
[26]周王民,马戎,基于FNN的电弧炉路况判断系统研究[J].测控技术。2007,26(3)：46-49.
[27]Boneh D, Franklin M. Identity Based Encryption from the Weil Pairing[J].SIAMJ. of Computing,2003,32(3):586-615.
[28]Melgani F, Bruzzone L. Classification of hyper spectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.
[29]傅杰.现代电弧炉冶炼周期综合控制理论及应用[J].北京科技大学学报,2004,26(6)：588-595.
[30]邓开楠.基于专家系统的电弧炉炉况判断方法研究[D].东北大学,2005.
[31]曲飞,吴敏,曹卫华,何勇.基于支持向量机的高炉炉况诊断方法[J].钢铁.2007.10.42：17-19.
[32]韩颖,毛志忠.电弧炉能量输入模型的研究[D].东北大学.
[33]顾根华等.考虑运行电抗与热平衡的电弧炉合理供电[J].加热设备,2001.1：32-37.
[34]Dua. V. etl. An. Outer-Apporxlmation Algorithm for the Solution of Multiparametric MINLP Problems [J]. Computers & Chemieal Engineering,1998, 22(1):955-958.
[35]Skrifvars, Hans etl. Comparison of certain MINLP Algorithms when applied to a model structure determination and Parameter estimation Problem[J]. Computers & Chemical Engineering,1998,22(12):1829-1835.
[36]K. mycernadGPost. Poreess. Technology of steel making from Sponge Iorn in the Eleertic Aer Funrace[J]. Stahl und Eisen,1993:194-204.
[37]袁平,王福利.电弧炉冶炼过程先进控制方法的研究与应用[D].东北大学.
[38]武骏,孙彦辉,李京社,李士琦.现代交流电弧炉电气运行合理化的研究[J].钢铁,1999,34(9)：19-22.
[39]尹洪超等.化工过程综合混合离散非线性规划的新算法明[J].大连理工大学学报,1995,35(5)：633-638.
[40]韩力群.人工神经网络理论、设计及应用—人工神经细胞、人工神经网络和人工神经系统[M].北京：化学工业出版社,2002.
[41]孙翎,张金隆,迟嘉显.基于粗糙集的CBR系统案例特征项权值确定[J].计算机工程与应用,2003,30：44-46.