氩氧精炼低碳铬铁生产过程数学模型的建立及控制策略的研究
|
摘要
在铁合金冶炼过程中,控制铁水碳含量和温度是整个吹炼工艺的核心,然而冶炼铁合金是一个非常复杂的多元多相高温状态下进行的非线性的物理化学反应过程,存在很多不确定的因素,且难以获得铁水成分准确连续的检测信息,即使有一些间接的检测方法,其精度也不能达到令人满意的程度,给冶炼铁合金的过程与终点控制带来很大困难。传统冶炼方法是根据经验观察炉口的火焰和火花、铁水的装入量和氧气的累计消耗量来估算终点的碳含量,对过程温度不进行控制,这种方式不仅延长了冶炼时间,降低了炉龄,而且反应过程中碳含量受到很多不确定因素的影响,使冶炼工艺不稳定,直接影响最终冶炼铁合金的质量。
本文提出的氩氧精炼(Argon Oxygen Decarburization,简称AOD)铁合金工艺是在转炉生产中碳铬铁的基础上,参考了炼钢专业AOD炉生产不锈钢的理论和实践。目标是以低碳铬铁冶炼生产过程为具体研究对象,在深入分析冶炼过程机理的基础上,建立低碳铬铁生产过程数学模型,研究冶炼过程及冶炼终点的控制策略,为创建高碳铁水直接生产中低碳铬铁的新工艺提供理论支持,为开创氩氧精炼铁合金节能工艺产业化生产的先例提供技术保障,实现铁合金企业生产过程节能降耗的全局优化。基于以上目标本文完成的主要研究内容如下:
1.通过流程再造,设计并完成了由2台炉子实现由高碳铬铁到中、低碳铬铁的生产工艺,高碳铬铁在电弧炉中溶化后,经灌渣,直接进入AOD炉吹炼,顶枪吹氧气,底枪吹氩氧混合气体,顶部高压氧枪吹氧使铁水脱碳速度快,可缩短冶炼时间,提高生产能力,通过底枪控制吹入不同比例的氧、氩混合气体,降低一氧化碳的分压,使脱碳反应正向进行,实现降碳保铬的目的,借助工程实现,开创了氩氧精炼铁合金节能工艺产业化的先例。
2.作为一个多相火法冶金过程,铁合金顶底复吹AOD精炼过程十分复杂。注意到该精炼过程的物理和化学特性,包括其热力学和动力学特征,考虑体系的质量和热量衡算,精炼过程的不等温状态,通过机理分析,研究和掌握铁水温度、供气速度、氧化反应速度等参数的耦合关系,建立了供氧速率与铁水脱碳速度、供氧速率与铁水温度、供氩速率与铁水温度之间的数学模型。采用灰色系统建模方法,建立了供氩速率与铁水脱碳速度之间的数学模型。本模型可与基础级自动化配合进行脱碳全过程的供氧流量自动控制,根据模型运算结果及时调整供氧流量,加强底吹气体的搅拌作用,进一步改善铁水—炉渣反应,使熔池内成分和温度的不均匀性得到有效改善,提高了终点温度和成分的命中率,避免了铁水中铬的过氧化,使之获得较好的脱碳效果,较优化的气体消耗,较长的炉衬寿命,为小型AOD炉的改造提供理论基础。
3.针对铁水碳含量不能在线检测的问题,基于模型,设计采用选择铁水温度作为二次输出量,铁水碳含量作为主要输出量,针对此方案比较了采用PID控制算法和推理控制算法控制铁水温度进而控制铁水碳含量,理论证明了在模型准确的前提下,推理控制算法控制性能优于PID算法。分析了采用推理控制算法控制铁水温度和碳含量,当模型不准确时对系统输出性能的影响,进而提出一种改进的推理控制算法,在推理控制的基础上引入了反馈,当模型准确时,反馈环节不起作用,当模型不准确时,反馈控制起到消除稳态偏差的作用,通过理论证明和仿真结果验证,得出了反馈推理控制在控制铁水终点温度和碳含量时,当模型存在误差时,具有很强的鲁棒性,且能够消除稳态偏差的结论。
4.氩氧精炼低碳铬铁生产过程DCS控制系统及实现中的相关问题。控制系统由二级组成,第一级采用双CPU工控机,负责处理复杂数据运算、集中操作监视;第二级采用西门子S7-300系列PLC,系统分为6个控制子系统,子系统与中央控制器共用3套PLC,对生产线上各台设备进行自动控制。为了配合顶枪气体流量控制系统、底枪气体流量控制系统、加料控制系统3个子系统的全自动运行,研究了铁水温度在线检测系统,提出了基于比色测温原理和黑体等温空腔理论的底枪测温法,稳定了铁水的发射率,应用基于最小二乘法的曲线回归实现红外测温设备的现场校准,完成温度补偿,通过在样炉上的精炼实验证明了底枪测温系统具有较高的精度,可以满足实际生产的要求,本系统已投入生产,运行稳定可靠,为控制策略的实现提供了硬件平台。
In ferroalloy refining process, the end point control is the core of the blowing process,however refining ferroalloy is very complex and multiphase which is carried out under high temperature chemical reaction process of nonlinear physical. There are many uncertain factors, and it is difficult to obtain accurate and continuous detection information, though there are some indirect detection methods, its accuracy can not achieve satisfactory level, refine ferroalloy process and endpoint control has been caused great difficulties . the traditional method is based on empirical observation smelting furnace mouth of the flames and sparks, hot metal into the cumulative volume and oxygen consumption to estimate the carbon end Volume. This approach can not only extend the smelting time, reduce the efficiency of the furnace and smelting, but also the carbon content during the reaction by many uncertain factors, making the smelting process instability directly which affects the final quality of the refining ferroalloy. For these reasons, this in-depth study of the temperature of hot metal detection method, a low-carbon ferrochrome production process of the mathematical model, based on the above two conditions, low carbon ferrochrome smelting process to complete the controller design reasoning, blowing through the timely adjustment The oxygen end of the carbon content of hot metal flow control and end temperature, solution flow rate of the original value of fixed oxygen blowing the issue and realize the dynamic control flow of oxygen blowing, improve the refining effect of the end of hit rate increased to shorten the refining time.
The proposed new process is the production of carbon ferrochrome converter based on the reference to the production of stainless steel professional AOD furnace theory and practice. The goal is to ferroalloy smelting production process for a specific object of study, low-carbon ferrochrome production process through the establishment of mathematical model and control strategy for the creation of high carbon molten iron directly to low-carbon ferrochrome production provide theoretical support for the new technology, and creating a Argon Oxygen refining ferroalloy production of energy-saving technology industry precedent to provide technical support, ferroalloy production process to achieve global energy consumption optimization. Based on the above to complete the main objective of this paper are as follows:
1. Through reengineering process, high-carbon ferrochrome proposed direct production of liquid low-carbon ferrochrome in the new technology, new processes needs to complet 2 units from the stove to medium high carbon ferrochrome, low carbon ferrochrome production, melted in electric arc furnace in high-carbon ferrochrome, after filling slag, blowing directly into the AOD furnace, AOD furnace with top lance blowing oxygen. Bottom Gun argon oxygen gas mixture, at the top of high pressure oxygen blowing lance to make hot metal decarburization speed, can reduce the refining time, improving production capacity, blown by bottom gun control different ratios of oxygen and argon mixed gas, reduce carbon monoxide in sub-pressure, the decarburization reaction forward and the achievement of the purpose of lowering carbon chromium security, with project implementation, creating a saving of argon oxygen refining process ferroalloy industry precedent.
2.As a multi-phase pyre-metallurgical process, alloy side and top combined blowing AOD refining process is very complicated. Noted that the refining process of physical and chemical properties, including its thermodynamic and kinetic features, consider the system of mass and heat balance, refining the process of isothermal state, through the mechanism analysis, study and master the hot metal temperature, gas velocity, oxidation Response speed, mobility and other parameters of hot metal coupling relationship established with the hot metal oxygen decarbonization rate of speed, temperature, oxygen supply rate and the mathematical model between hot metal; for argon between the rate and the mathematical model of hot metal temperature. Gray system modeling method, a hot metal for the decarbonization rate and rate of argon between the mathematical model. The model can be carried out with basic-level automation with the whole process of decarbonization oxygen flow automatic control, the results of the model to adjust oxygen blowing operation flow, strengthen the role of bottom-blowing gas mixing, and further improve the hot metal - slag reaction, so that the molten pool composition and temperature inhomogeneity be effectively improved, carbon and oxygen reaction step closer to balance and improve the endpoint temperature and composition of the hit rate and reduce the oxygen blowing end product, to avoid the hot metal chromium peroxide to improve the alloy, metal collection Yield and quality of molten iron, thus obtain is better results of decarburization than the optimal gas consumption, longer lining life, the transformation for small AOD furnace to provide a theoretical basis.
3.Used as a secondary output selection of hot metal temperature, hot metal carbon content as the main output, by comparing the PID control algorithm and inference control algorithm to control the temperature and then control the end of hot metal hot metal carbon content, theoretical proof of the inference control algorithm is better than PID control algorithm. By the end of inferential control algorithm to control the temperature and carbon content of molten iron, through theory proved, obtained control of inferential control endpoint temperature and carbon content of molten iron, fast settling time without overshoot, has strong robustness, and can eliminate Steady-state deviation.
4. Development of low carbon ferrochrome production of argon oxygen refining process of implementation of DCS control systems and related issues. Control system consists of two components, the first-class dual-CPU IPC to deal with complex data operations, centralized operation of surveillance; centralized operation to monitor use of the Siemens MP377-15 touch screen as the monitor interface, PROFIBUS-DP and MPI through the composition of real-time bus Communication network for data communication with the underlying PLC. In order to achieve decentralized control, centralized management and monitoring functions; the second level with 3 sets of Siemens S7-300 series PLC, the control system is divided into five subsystems, namely, tilting the furnace control system, top gun lift control system, top gun Gas flow control system, the end of the gun gas flow control system, the feeding control system. 5 share control subsystem and the central controller 3 sets produced by Siemens S7-300 programmable logic controller, Production line control system for automatic control of the device. The end of the gun which the central controller and gas flow control system share a table produced S7300 Siemens programmable logic controller, tilting the furnace control system and the top gun lift control system, the end of the gun gas flow control system share a programmable logic controller, the feeding control system 1 set S7300 alone programmable logic controller, the system has been put into production, stable and reliable operation. AOD furnace hot metal temperature of line detection methods, uncertainty caused by background radiation emissivity fluctuations is proposed based on principle and bold color temperature isothermal theory of bottom gun cavity temperature, stabilization of the molten iron of the emissivity. Application of curve regression based on least squares method to achieve infrared temperature measurement device calibration, temperature compensation complete. By refining furnace in the sample proved bottom gun of temperature measurement system with high accuracy and repeatability to meet the requirements of actual production.
本文提出的氩氧精炼(Argon Oxygen Decarburization,简称AOD)铁合金工艺是在转炉生产中碳铬铁的基础上,参考了炼钢专业AOD炉生产不锈钢的理论和实践。目标是以低碳铬铁冶炼生产过程为具体研究对象,在深入分析冶炼过程机理的基础上,建立低碳铬铁生产过程数学模型,研究冶炼过程及冶炼终点的控制策略,为创建高碳铁水直接生产中低碳铬铁的新工艺提供理论支持,为开创氩氧精炼铁合金节能工艺产业化生产的先例提供技术保障,实现铁合金企业生产过程节能降耗的全局优化。基于以上目标本文完成的主要研究内容如下:
1.通过流程再造,设计并完成了由2台炉子实现由高碳铬铁到中、低碳铬铁的生产工艺,高碳铬铁在电弧炉中溶化后,经灌渣,直接进入AOD炉吹炼,顶枪吹氧气,底枪吹氩氧混合气体,顶部高压氧枪吹氧使铁水脱碳速度快,可缩短冶炼时间,提高生产能力,通过底枪控制吹入不同比例的氧、氩混合气体,降低一氧化碳的分压,使脱碳反应正向进行,实现降碳保铬的目的,借助工程实现,开创了氩氧精炼铁合金节能工艺产业化的先例。
2.作为一个多相火法冶金过程,铁合金顶底复吹AOD精炼过程十分复杂。注意到该精炼过程的物理和化学特性,包括其热力学和动力学特征,考虑体系的质量和热量衡算,精炼过程的不等温状态,通过机理分析,研究和掌握铁水温度、供气速度、氧化反应速度等参数的耦合关系,建立了供氧速率与铁水脱碳速度、供氧速率与铁水温度、供氩速率与铁水温度之间的数学模型。采用灰色系统建模方法,建立了供氩速率与铁水脱碳速度之间的数学模型。本模型可与基础级自动化配合进行脱碳全过程的供氧流量自动控制,根据模型运算结果及时调整供氧流量,加强底吹气体的搅拌作用,进一步改善铁水—炉渣反应,使熔池内成分和温度的不均匀性得到有效改善,提高了终点温度和成分的命中率,避免了铁水中铬的过氧化,使之获得较好的脱碳效果,较优化的气体消耗,较长的炉衬寿命,为小型AOD炉的改造提供理论基础。
3.针对铁水碳含量不能在线检测的问题,基于模型,设计采用选择铁水温度作为二次输出量,铁水碳含量作为主要输出量,针对此方案比较了采用PID控制算法和推理控制算法控制铁水温度进而控制铁水碳含量,理论证明了在模型准确的前提下,推理控制算法控制性能优于PID算法。分析了采用推理控制算法控制铁水温度和碳含量,当模型不准确时对系统输出性能的影响,进而提出一种改进的推理控制算法,在推理控制的基础上引入了反馈,当模型准确时,反馈环节不起作用,当模型不准确时,反馈控制起到消除稳态偏差的作用,通过理论证明和仿真结果验证,得出了反馈推理控制在控制铁水终点温度和碳含量时,当模型存在误差时,具有很强的鲁棒性,且能够消除稳态偏差的结论。
4.氩氧精炼低碳铬铁生产过程DCS控制系统及实现中的相关问题。控制系统由二级组成,第一级采用双CPU工控机,负责处理复杂数据运算、集中操作监视;第二级采用西门子S7-300系列PLC,系统分为6个控制子系统,子系统与中央控制器共用3套PLC,对生产线上各台设备进行自动控制。为了配合顶枪气体流量控制系统、底枪气体流量控制系统、加料控制系统3个子系统的全自动运行,研究了铁水温度在线检测系统,提出了基于比色测温原理和黑体等温空腔理论的底枪测温法,稳定了铁水的发射率,应用基于最小二乘法的曲线回归实现红外测温设备的现场校准,完成温度补偿,通过在样炉上的精炼实验证明了底枪测温系统具有较高的精度,可以满足实际生产的要求,本系统已投入生产,运行稳定可靠,为控制策略的实现提供了硬件平台。
In ferroalloy refining process, the end point control is the core of the blowing process,however refining ferroalloy is very complex and multiphase which is carried out under high temperature chemical reaction process of nonlinear physical. There are many uncertain factors, and it is difficult to obtain accurate and continuous detection information, though there are some indirect detection methods, its accuracy can not achieve satisfactory level, refine ferroalloy process and endpoint control has been caused great difficulties . the traditional method is based on empirical observation smelting furnace mouth of the flames and sparks, hot metal into the cumulative volume and oxygen consumption to estimate the carbon end Volume. This approach can not only extend the smelting time, reduce the efficiency of the furnace and smelting, but also the carbon content during the reaction by many uncertain factors, making the smelting process instability directly which affects the final quality of the refining ferroalloy. For these reasons, this in-depth study of the temperature of hot metal detection method, a low-carbon ferrochrome production process of the mathematical model, based on the above two conditions, low carbon ferrochrome smelting process to complete the controller design reasoning, blowing through the timely adjustment The oxygen end of the carbon content of hot metal flow control and end temperature, solution flow rate of the original value of fixed oxygen blowing the issue and realize the dynamic control flow of oxygen blowing, improve the refining effect of the end of hit rate increased to shorten the refining time.
The proposed new process is the production of carbon ferrochrome converter based on the reference to the production of stainless steel professional AOD furnace theory and practice. The goal is to ferroalloy smelting production process for a specific object of study, low-carbon ferrochrome production process through the establishment of mathematical model and control strategy for the creation of high carbon molten iron directly to low-carbon ferrochrome production provide theoretical support for the new technology, and creating a Argon Oxygen refining ferroalloy production of energy-saving technology industry precedent to provide technical support, ferroalloy production process to achieve global energy consumption optimization. Based on the above to complete the main objective of this paper are as follows:
1. Through reengineering process, high-carbon ferrochrome proposed direct production of liquid low-carbon ferrochrome in the new technology, new processes needs to complet 2 units from the stove to medium high carbon ferrochrome, low carbon ferrochrome production, melted in electric arc furnace in high-carbon ferrochrome, after filling slag, blowing directly into the AOD furnace, AOD furnace with top lance blowing oxygen. Bottom Gun argon oxygen gas mixture, at the top of high pressure oxygen blowing lance to make hot metal decarburization speed, can reduce the refining time, improving production capacity, blown by bottom gun control different ratios of oxygen and argon mixed gas, reduce carbon monoxide in sub-pressure, the decarburization reaction forward and the achievement of the purpose of lowering carbon chromium security, with project implementation, creating a saving of argon oxygen refining process ferroalloy industry precedent.
2.As a multi-phase pyre-metallurgical process, alloy side and top combined blowing AOD refining process is very complicated. Noted that the refining process of physical and chemical properties, including its thermodynamic and kinetic features, consider the system of mass and heat balance, refining the process of isothermal state, through the mechanism analysis, study and master the hot metal temperature, gas velocity, oxidation Response speed, mobility and other parameters of hot metal coupling relationship established with the hot metal oxygen decarbonization rate of speed, temperature, oxygen supply rate and the mathematical model between hot metal; for argon between the rate and the mathematical model of hot metal temperature. Gray system modeling method, a hot metal for the decarbonization rate and rate of argon between the mathematical model. The model can be carried out with basic-level automation with the whole process of decarbonization oxygen flow automatic control, the results of the model to adjust oxygen blowing operation flow, strengthen the role of bottom-blowing gas mixing, and further improve the hot metal - slag reaction, so that the molten pool composition and temperature inhomogeneity be effectively improved, carbon and oxygen reaction step closer to balance and improve the endpoint temperature and composition of the hit rate and reduce the oxygen blowing end product, to avoid the hot metal chromium peroxide to improve the alloy, metal collection Yield and quality of molten iron, thus obtain is better results of decarburization than the optimal gas consumption, longer lining life, the transformation for small AOD furnace to provide a theoretical basis.
3.Used as a secondary output selection of hot metal temperature, hot metal carbon content as the main output, by comparing the PID control algorithm and inference control algorithm to control the temperature and then control the end of hot metal hot metal carbon content, theoretical proof of the inference control algorithm is better than PID control algorithm. By the end of inferential control algorithm to control the temperature and carbon content of molten iron, through theory proved, obtained control of inferential control endpoint temperature and carbon content of molten iron, fast settling time without overshoot, has strong robustness, and can eliminate Steady-state deviation.
4. Development of low carbon ferrochrome production of argon oxygen refining process of implementation of DCS control systems and related issues. Control system consists of two components, the first-class dual-CPU IPC to deal with complex data operations, centralized operation of surveillance; centralized operation to monitor use of the Siemens MP377-15 touch screen as the monitor interface, PROFIBUS-DP and MPI through the composition of real-time bus Communication network for data communication with the underlying PLC. In order to achieve decentralized control, centralized management and monitoring functions; the second level with 3 sets of Siemens S7-300 series PLC, the control system is divided into five subsystems, namely, tilting the furnace control system, top gun lift control system, top gun Gas flow control system, the end of the gun gas flow control system, the feeding control system. 5 share control subsystem and the central controller 3 sets produced by Siemens S7-300 programmable logic controller, Production line control system for automatic control of the device. The end of the gun which the central controller and gas flow control system share a table produced S7300 Siemens programmable logic controller, tilting the furnace control system and the top gun lift control system, the end of the gun gas flow control system share a programmable logic controller, the feeding control system 1 set S7300 alone programmable logic controller, the system has been put into production, stable and reliable operation. AOD furnace hot metal temperature of line detection methods, uncertainty caused by background radiation emissivity fluctuations is proposed based on principle and bold color temperature isothermal theory of bottom gun cavity temperature, stabilization of the molten iron of the emissivity. Application of curve regression based on least squares method to achieve infrared temperature measurement device calibration, temperature compensation complete. By refining furnace in the sample proved bottom gun of temperature measurement system with high accuracy and repeatability to meet the requirements of actual production.
引文
[1]舒杰辉,魏季和,池和冰等.不锈钢AOD精炼过程数学模拟进展.炼钢,2004年6月.
[2]王诚训.炉外精炼用耐火材料.北京:冶金工业出版社,1996,3-6.
[3]邢建军,张建平.AOD炉用耐火材料的使用研究.耐火材料,1997年第一期,20-25.
[4]贾振海.氩氧炉生产中、低、微碳铬铁的新工艺[J].铁合金,2005,(2):11-16.
[5]杨汝清.智能控制工程[M].上海:上海交通大学出版社,2001,(1):36-60.
[6]王永初.智能控制理论与系统的发展评述.华侨大学学报,2004,25(1):1-4.
[7] Ling KV,DexterAL.Expert control of air conditioning. Plant.Automation ,1994,35-38.
[8]孙增圻.智能控制理论与技术.北京:清华大学出版社,1997,100-170.
[9]曲英等.炼钢学原理.北京:冶金工业出版社,1980,150-210.
[10] Regawa T,Otatani T.Development of CaO-MgO refractories and their effects on refined mechanism of extremely clean steel[A].Proceedings of Unitect:93[C].Sao Paulo,Brazil,1993.
[11] Xiang Zhongyong,GUO Qingdi.Regenerative Blast Furnace Hot Stove.Beijing:Metallurgical Industry Press,1988.
[12]欧俭平,蒋绍坚,马爱纯等.高温空气燃烧技术用于钢包烘烤的节能效果.冶金能源,2003,22(6):30-32.
[13]刘浏,佟溥翘,崔淑贤.溅渣层形成和对炉衬保护机理的研究.钢铁, 1999年11月,Vol.34,No.11.
[14] CharlesJ,Messina and John R.Paules.Paules,Steelmaking Conference Proceedings,1996:153-155.
[15]赵沛.炉外精炼及铁水预处理实用技术手册.北京:冶金工业出版社,2004:307.
[16] Aoki T.The mechanism of the back-attack phenomenon on a bottom blowing tuyere investigated in model experiments〔J〕.Tetsu-to-hagane,1990,76(11):1996-2003.
[17]郑玉贵,姚治铭,柯伟.流体力学因素对冲刷腐蚀的影响机制.腐蚀科学与防护技术,2000年01月,第12卷第1期,110-114.
[18]王以法.人工智能钻井实时专家控制系统研究.石油学报,2003,Vol.23,NO.2:84-86.
[19] Gerald Gibson.A supervisory controller for optimization of building central cooling system.ASHRAE Transactions,2001,No.2:431-438.
[20] Cai Zixing,Wang Y,Cai J.A real-time expert control system.AI in Engineering,1996,Vol.10,No.4:317-322.
[21]陶永华.新型PID控制及其应用[M].北京:机械工业出版社,2003.7:1-7.
[22]施月循,邢玉禄,王寿同等.钢铁冶金工艺学[M].沈阳:东北大学出版社,1996.
[23]戴云阁,李文秀,龙腾春..现代转炉炼钢[M].沈阳:东北大学出版社,1998.
[24]孟劲松.复吹转炉终点控制的实践和理论[J].鞍钢技术,1995,(8):7-10.
[25] Teruki Makino,Shigeru Omiya,Nobukazu Kitagawa.Automatic Operation of Converters at Mizushima Works[J].Steelmaking Conference Proceedings, 1989:67-71.
[26] Rob Boom.Sublance Technology for Oxygen Steelmaking[J].Steel Technology International,1994,(2):90-94.
[27]陶钧,柴天佑,李小平等.转炉炼钢智能控制方法及应用[J].控制理论与应用,2001,18(增刊):129-133.
[28]孟劲松.复吹转炉终点控制的实践和理论[J].鞍钢技术,1995,(8):7-10.
[29]王顺晃,杜大川,刘宏才等.顶底复吹转炉的计算机控制[J].北京科技大学学报,1992,(5):563-568.
[30]屠海,洪新.转炉炼钢终点锰磷动态控制技术的开发[J].上海金属,2002,24(2):27-30.
[31]易继楷,侯媛彬编著.智能控制技术[M].北京:北京工业大学出版社,2003.
[32]李润生,李延辉,胡学军.神经网络在冶金中的应用[J].钢铁研究,1998(2):48-50.
[33]陈国翠.我国铬铁合金生产与发展[J].铁合金,2000,(5):40-42.
[34]王雅贞,张岩,张红文编.氧气顶吹转炉炼钢工艺与设备[M].北京:冶金工业出版社,2001.
[35] Isao Tone,Nobukazu Kitagawa,Katsuo Ishida.A Fully Automated BOF Refining System at the Mizushima Works..1991 Steelmaking Conference Proceedings:375-378.
[36] N.Ramaseder,W.Pirklbauer,J.Kalisch.Slanted.Sublance System for Blowing Process Control.MPT,1993(3):42-45.
[37] W.F.Nirschel,R.P.Stone, C.J.Carr.Overview of Steelmaking Process Control Sensors for BOF[J].Ladle and Continuous Casting Tundish, Iron and Steelmaker,,2001,28:61-65.
[38]王海,陈宏伟,张丽荣.氧气顶吹转炉自动控制系统的开发与应用[J].电工技术杂志,2004,6:57-60.
[39] Ken-Iwamura, Masao FurusawaMasakazu Miyamoto.New Endpoint Control System with Auto-parameter-turning in BOF[J] . Steelmaking Conference Proceedings,1995:715-719.
[40]刘文林.ControlLogix系统在AOD炉控制系统改造中的应用[J].沈阳工程学院学报(自然科学版),2006,(1):54-57.
[41]张鉴.终点碳控制的现状和前景[J].特殊钢,1995,16(4):1-6.
[42]蒋晓放.宝钢300 t转炉复吹现状与发展[J].宝钢技术,2002,(3):1-5.
[43]房荣波,魏元,杨海峰.转炉钢水出钢温度的预测[J].鞍钢技术,2002,(4):24-26.
[44]张润宇,肖兵,张文弟.转炉钢水含碳量的估计[J].自动化学报,1993,19 (3):381-383.
[45]李少远,席裕庚.智能控制的新进展[J].控制与决策,2000,15(1):1-5 .
[46] Yun S Y, Chang K S. Dynamic Predication Using Neural Network for Automation of BOF Process in Steel Industry.I&SM,1996(8):37-42 .
[47] Hint K J.Extending the Functional Equivalence of Radial Basis Function Network and Fuzzy Inference System[J].IEEE Trans on Neural Networks, 1996,(3):776-781 .
[48] Nicolas Plcon, Frederlc Alexandre, Patrick Bresson, et al.Artlficlal Neural Nemorks for the Presetting of a Steel Temper Mill[J].IEEEEXPET ,1996,(2): 22-27 .
[49] Tao J, Wang X.Intelligent Control Method and Application for BOF Steelmaking Process[J]. Proceedings of the IFAC World Congress,2002.
[50] Robert H N.Theory of the Back-propagation Neural Network. Proc,UCNN,Washington D.C.,1989(1):593-605.
[51] Tapas Kanungo, David M, Mount, Nathan S Netanyahu, et al.A Efficient k- Means Clustering Algorithm:Analysis and Implementation[J].IEEE Tansactions on Pattern Analysis and Machine Intelligence, 2002,24(7): 881-892.
[52]飞思科技产品研发中心.Matlab6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2003,1.
[53]喻淑仁.转炉炼钢过程静态控制及数学模型[J].炼钢,1995,(3):55-60.
[54]李新民,李强,田灵虎.AOD炉的工艺及设备特征.冶金设备,1995 ,(4):46-49.
[55]李强,李向东.AOD炉倾动机构的特征分析.特钢技术,1998 ,(3):37-39.
[56]刘莹,钟晓勤,瞿志豪.AOD炉倾动力矩精确计算及耳轴中心安全高度位置确定.上海应用技术学院学报,2007,7(2):87-90.
[57] Soetanto,D. Lapierre,L. Pascoal.An adaptive non-singular path-following control of dynamic wheeled robots.Decision and Control,2003.Proceedings,42nd IEEE Conferrence on, P.1765-1770,2003.
[58] Ohishi,K.Kuramochi,K.Miyazaki,T.Koide,D.Tokumaru,H. Robust feedforward tracking servo system Considering force disturbance for optical disk recording.Control Applications,2004.Proceedings of the 2004 IEEE International Conference on,P527-532,2004.
[59] Wieland,P.Meurer,T.Graichen,K.Zeitz,M. Feedforward control design under input constraints for a tubular reactor model.Decision and Control(CDC),IEEE Conference on,San Diego,CA,P3968-3973,2006.
[60] K.Shimizu.Wear of Refractories for AOD Furnace.Taikabutsu Overseas,2001,53(4):198-204.
[61] W.Cao,Y.Miyamoto.Direct Slicing from AutoCad Solid Models for Rapid Prototyping.International Jounal of Advanced Manufacturing Technology, 2003,21(10):739-742.
[62] Jiantao Lu,Sihai Qiu.The vector control based on MRAS speed sensorless induction motor drive.Intelligent Control and Automation,the Fifth word Congress,P4550-4553,2004.
[63] S.Vaez-zadeh,E.Jalali.Combined Vector Control And Direct Torque Control Method For High Performance Induction Motor,Energy Conversion&Management, 48(12): P3095-3101,2007.
[64] Shanshan Wu,Yongdong Li,Zedong Zheng.Speed Sensorless Vector Control of Induction Motor Based on Full-Order Flux Observer.5th International Power Electronics and Motion Control Conference, (4):P1892-1895,2006.
[65]钟淑荣.国外炼钢生产的发展与21世纪的技术发展[J].鞍钢技术,1998,(3):5-8.
[66]韩永东,曾朝华,魏国芳.底吹氩气系统的建模、分析与智能控制.河北冶金,2002年第5期.
[67]王海江,朱宏利,魏季和等.不锈钢的侧顶复吹AOD精练过程.上海金属,2005年9月.
[68]董锋斌,蒋军,皇金锋等.钢包精炼炉智能底吹氩控制系统.机床与液压,2006年第8期.
[69]王海川,张友平,李文超.转炉吹炼中低碳锰铁的应用分析.铁合金, 2000年第6期.
[70]董锋斌,皇金锋.PLC在钢包底吹氩系统中的应用.仪表技术与传感器, 2006年第3期.
[71]王东,包燕平,徐保美等.VOD精炼过程的水模型实验.特殊钢,2006年3月第27卷第2期.
[72]杨阿娜,刘学华,蔡开科.炼钢过程中氧的控制.钢铁研究学报,2005年6月第17卷第3期.
[73]张国栋,肖景国,商桂海等.济钢25吨转炉自动化控制系统改进.山东冶金,2003,(5):44-47.
[74] Hint K J.Extending the Functional Equivalence of Radial Basis Function Network Inference System[J].IEEE Trans on Neural Networks,1996,(3):776-781.
[75] SIMATIC Software System Software for S7-300 and S7-400 ProgramDesign[M].Nuremberg:Siemens AG,1998.
[76] Wilson EL,Karr CL,Bennett JP.An adaptive,intelligent control system for slag foaming.Applied Intelligence,2004, Vol.20, No.2,P 165-177.
[77] Y.D. Yang, I.D. Sommerville,AMcLean. SOME FUNDAMENTAL CONSIDERATIONS PERTAINING TO OXIDE MELT INTERACTIONS AND THEIR INFLUENCE ON STEEL QUALITY.Transactions of the Indian Institute of Metals,2006 ,vol.59 ,no.5,P.655-669.
[78] Bachinina,G.A.,Botman,,S.V..Results of studying the physical properties of blast-furnace slags at the Il'ich Integrated Steel Plant . Steel in translation, V.26,no.10, P.6-12.
[79] J.Y.Yen,D.Mantha,L.Evrard.Effect of composition and temperature on viscosities of lead blast furnace slags.Minerals&MetallurgicalProcessing,2006,vol.23,no.3,P.165-170.
[80] M.S.C.Y-Y ih,et al.Optimal Variable Structure Controller for DC Motor Speed Control.IEEE proc.Pt.D.1984,131(1):41-48.
[81] V.I.Vtkin.Variable Structure Systems.Present and Future,Auto. Emote Control,1983(8):15-17.
[82]刘寿昌,杨希富著.锰铁合金用Ca0渣系脱磷的热力学研究.铁合金,2001 No.4,Tot.159,P8-11.
[83]陶钧.转炉炼钢过程智能控制方法及其应用的研究「D].东北大学,2001.
[84] Wilson EL,Karr CL, Bennett JP. An adaptive, intelligent control system for slag foaming.Applied Intelligence,2004 Vol.20 No.2,P 165-177.
[85] D.k.Belashchenko,B.R.Gel'chinskii,E.V.Dyul'dina.Simulation of multicomponent blast-furnace slags.Steel in translation,2004 Vol.34 No.9,P13-17.
[86]刘玠,蒋慎言,陈大纲.炼钢生产自动化技术.冶金工艺出版社,2006.
[87]房荣波,魏元,杨海峰.转炉钢水出钢温度的预测[J].鞍钢技术,2002,(4):24-26.
[88]刘柏松.RH-MFB脱碳过程模型与工艺优化.河北理工大学硕士学位论文,2005.
[89]李顺健.基于ANN的攀钢转炉炼钢终点控制模型.电子科技大学工程硕士学位论文,2008.
[90]李清.炼钢电弧炉全程动态模型与仿真研究.上海大学博士学位论文,2003.
[91]陈昌.转炉炼钢终点控制系统建模研究.沈阳工业大学硕士学位论文,2007.
[92]赵进.自动化炼钢技术的应用与研究.辽宁科技大学工程硕士学位论文, 2007.
[93]史国敏.侧顶复吹条件下AOD转炉熔池内流体流动现象的数学和物理模拟.上海大学博士学位论文,2007.
[94]郁能文.多功能RH精炼过程的数学和物理模拟.上海大学博士学位论文, 2001.
[95]胡汉涛.钢液RH精炼非平衡脱碳过程的数学模拟.上海大学博士学位论文, 2005.
[96]李波.合成氨脱碳系统的模拟与研究.大连理工大学硕士学位论文,2001.
[97]鱼洋洋.重钢转炉复吹工艺数理模拟研究.重庆大学硕士学位论文,2006.
[98]韩传基,刘柏松,艾立群等.RH-MFB精炼过程脱碳数学模型及工艺研究.钢铁研究学报,2007,19(4):17-22.
[99]廉宇峰,尤文,赵彬等.AOD冶炼脱碳过程的非等间隔灰色优化模型研究.应用科技,2010,37(1):57-60.
[100]胡文华.AOD冶炼脱碳反应模型与计算机模拟.冶金设备,2005,(1):19-24.
[101]李士琦,江国利,路俊萍等.AOD冶炼不锈钢工艺模型的研究.特殊钢, 2009,30(1):29-31.
[102]沈春飞,蒋兴元,尹世友等.AOD精炼304不锈钢渗氮、脱氮的计算模型与应用.特殊钢,2009,30(5):28-30.
[103]李永祥,马嵩,邹宗树.利用数学模型分析复吹转炉吹炼工艺.材料与冶金学报,2007,6 (4):260-263.
[104]赵成林,马嵩.复吹转炉炼钢过程数学模拟:动力学模型.材料与冶金学报,2004,3(2):99-103.
[105]李青,马志刚,杜斌.RH脱碳模型的建立和测试.上海大学学报(自然科学版),2002,8(2):119-123.
[106]刘柏松,艾立群.宝钢RH-MFB深脱碳模型.河北理工大学学报(自然科学版),2007,29(3):27-30.
[107]姜周华,阮小江,李阳等.兑铁水条件下UHP电弧炉脱碳数学模型.东北大学学报(自然科学版),2009,30(3):388-391.
[108]林东,赵成林,张贵玉等.复吹转炉炼钢过程脱碳模型.钢铁研究,2006,34(6):12-15.
[109]徐立云,张斌,王景成等.基于动态数学模型的冶金加热炉实时仿真器的研究.控制与决策,2002,17(2):207-210.
[110]王哲.矿热炉与AOD炉双联法冶炼不锈钢的设计与研究.西安建筑科技大学硕士学位论文,2009.
[111]马志刚,谢树元,杜斌等.脱碳模型在宝钢RH精炼的应用.中国冶金,2009,19(10):1-3.
[112]伍建军.冶金过程数学模型方法与软件调试浅谈.有色金属设计与研究,2008,29(2):9-11.
[113]陈义胜,贺友多,苍大强等.一个RH处理过程的脱碳动态模型.包头钢铁学院学报,2002,21(2):108-111.
[114]孙钻.智能控制技术在转炉终点动态控制建模中的应用研究.武汉科技大学硕士学位论文.
[115]崔衡,唐德池,包燕平.中间包底吹氩水模型试验及冶金效果.钢铁钒钛,2010,31(1):36-39.
[116]鱼洋洋.重钢转炉复吹工艺数理模拟研究.重庆大学硕士学位论文,2006.
[117]黄可为,杜斌.转炉合金最小成本控制模型.控制理论应用,2003,(2):11-13.
[118]王永富,李小平等.转炉炼钢动态过程预设定模型的混合建模与预报.东北大学学报,2003,24(8):715-718.
[119]陶钧,谢书明,柴天佑.转炉炼钢控制模型的研究与展望.钢铁,1999,34(8):69-72.
[120]韩敏,黄晓清,谭明祥等.转炉炼钢熔池碳含量动态变化研究.冶金自动化,2009,33(5):28-32.
[121]黄金侠.转炉炼钢终点静态控制预测模型.天津大学硕士学位论文,2005.
[122]蔡开科.转炉冶炼低碳钢终点氧含量控制.钢铁,2009,44(5):27-31.
[123]林东,赵成林,张贵玉等.转炉冶炼脱磷过程数学模型.材料与冶金学报,2008,7(2):85-88.
[124]周远华.重钢转炉复吹的模拟研究.重庆大学工程硕士学士论文,2004.
[125]张琳.基于GA-BP混合算法的转炉终点优化控制模型.重庆大学硕士学位论文,2004,
[126]周立平.AOD转炉技术.《一重技术》,2007,(1):12-14.
[127]池和冰,魏季和,舒杰辉等.不锈钢AOD转炉精炼过程数学模拟初探.上海金属,2007,29(4):49-58.
[128]宋海鹰,桂卫华,阳春华等.基于核偏最小二乘法的动态预测模型在铜转炉吹炼中的应用.中国有色金属报,2007,17(7):1201-1206.
[129]甄云璞,冯聚和.基于神经网络的转炉冶炼终点锰、磷静态预报模型.河北理工学院学报,2007,29(2):16-19.
[130]周洪煜,王驹,陈晓锋等.基于虚拟仪器技术的炼钢转炉传动机构在线监测与故障诊断系统.计算机测量与控制,2007,15(1):14-15.
[131]张靓.济钢转炉的自动化控制系统.中国冶金,2007,17(4):48-50.
[132]夏元弟.交流变频调速在转炉倾动控制中的应用.工业计量,2007,17(4):15-17.
[133]袁树明,王福明.利用人工神经网络系统提高转炉终点碳温双命中率.山东冶金,2007,29(2):63-65.
[134]关春丽,王明春,刚占库.通钢120t转炉实现计算机自动炼钢的实践.钢铁,2007,42(5):82-86.
[135]万学武,杨文栋,汤旭等.延长炼铜转炉寿命的实践.中国有色冶金,2007,(1):40-42.
[136]曲丽萍,曲永印,白晶等.氧气顶吹转炉智能控制策略的研究.人工智能技术应用,2007,(3):24-27.
[137]万雪峰,张贵玉,林东等.转炉吹炼过程中耗氧比模型.材料与冶金学报,2007,6(1):7-11.
[138]张大勇,张彩军.转炉复吹龄同步技术的优化与实践.河南冶金,2007,15(3):40-42.
[139] P.D.Hubbeling,G.A.Oostermeijer.转炉副枪与动态控制.钢铁,2007,42(4):83-86.
[140]陈俊东,张彩军,冯聚和.转炉静态机理模型与节能降耗.河北理工学院学报,2007,29(1):32-35.
[141]闫开宇,孙健.转炉倾动机构研究.《一重技术》,2007,(2):9-10.
[142]付海涛.转炉氧气流量中温度压力补偿的实现.冶金自动化,2007,(4):64-65.
[143]盛坚,杨永军.采用高温黑体式光纤温度计在线监测感应加热真空炉液相温场得可行性.国防军工热学、流量计量与测试技术交流会论文专集,2008.
[144]谢书明,高宪文,柴天佑.基于灰色模型的转炉炼钢终点预报研究.钢铁研究学院,1999,11(4):9-12.
[145]陶钧,谢书明,柴天佑.基于遗传算法和径向基函数神经网络的转炉炼钢模型.系统仿真学报,2000,12(3):241-244.
[146]谢书明,柴天佑,陶钧.一种转炉动态终点预报的新方法.自动化学报,2001,27(1):136-139.
[147]王永富,李小平,柴天佑等.转炉炼钢动态过程预设定模型的混合建模与预报.东北大学学报(自然科学报),2003,24(8):715-718.
[148]陶钧,柴天佑,李小平等.转炉炼钢智能控制方法及应用.控制理论与应用,2001,18(S):129-133.
[149]陈忠伟,袁守谦.LD转炉冶炼的静态数学模型及实现.炼钢,2000,16(5):31-34.
[150]吴晓东,刘旭兰,郑建忠.LF精炼终点钢水温度的预报模型.炼钢,2007,23(6):43-46.
[151]武拥军,姜周华,姜茂发等.LF炉精炼过程钢水温度预报模型.钢铁研究学报,2002,14(2):9-12.
[152]池和冰,魏季和,舒杰辉等.不锈钢AOD转炉精炼过程数学模拟初探.上海金属,2007,29(4):49-58.
[153]王子中,刘达,张炳元.顶吹转炉最佳炉龄数学模型研究.冶金经济与管理,1995,(5):32-33.
[154]潘玉华,马萍.复吹转炉底吹气流数学模型研究.钢铁,1994,29(5):17-21.
[155]温宏愿,赵琦,陈延如等.光谱图像分析用于转炉终点实时预测.光电工程,2008,35(5):135-139.
[156]谢书明,孙凯,陈昌.基于RBF神经网络的转炉炼钢终点预报.沈阳工业大学学报,2006,28(4):405-408.
[157]柴天佑,谢书明,杜斌等.基于RBF神经网络的转炉炼钢终点预报.中国有色金属学报,1999,9(4):868-872.
[158]温宏愿,赵琦,陈延如.基于辐射信息分析的转炉终点控制回归模型.仪器仪表学报,2008,29(8):1633-1637.
[159]谢书明,高宪文,柴天佑.基于灰色模型的转炉炼钢终点预报研究.钢铁研究学报,1999,11(4):9-12.
[160]田民乐,刘少民.基于模糊神经网络的炼钢炉静态建模.北京科技大学学报,1998,20(2):136-139.
[161]甄云璞,冯聚和.基于神经网络的转炉冶炼终点锰、磷静态预报模型.河北理工学院学报,2007,5(2):16-19.
[162]陶钧,谢书明,柴天佑.基于遗传算法和径向基函数神经网络的转炉炼钢模型.系统仿真学报,2000,12(3):241-244.
[163]袁树明,王福明.利用人工神经网络系统提高转炉终点碳温双命中率.山东冶金,2007,29(2):63-65.
[164]汤天宇,杜德信,柳孝诚等.攀钢转炉炉龄数学模型.钢铁钒钛,1995,16(2):1-8.
[165]田建国.应用多元回归技术预测CAS终点钢水温度.中国冶金,2006,16(1):32-34.
[166]万雪峰,张贵玉,林东等.转炉吹炼过程中耗氧比模型.材料与冶金学报,2007,6(1):7-11.
[167]李伟,贺友多.转炉底吹气体过程中的三维数学模型.包头钢铁学院学报,1994,13(1):45-48.
[168]陈俊东,张彩军,冯聚和.转炉静态机理模型与节能降耗.河北理工学院学报,2007,29(1):32-35.
[169]贾凌云,高留治.转炉炼钢车间设计的基本数学模型.钢铁,1996,31(4):22-26.
[170]喻淑仁.转炉炼钢过程静态控制及其数学模型.炼钢,1995,(3):55-60.
[171]王永富,李小平,柴天佑等.转炉炼钢动态过程预设定模型的混合建模与预报.东北大学学报(自然科学报),2003,24(8):715-718.
[172]林东,赵成林,张贵玉等.复吹转炉炼钢过程脱氧碳模型.钢铁研究,2006,34(6):12-15.
[2]王诚训.炉外精炼用耐火材料.北京:冶金工业出版社,1996,3-6.
[3]邢建军,张建平.AOD炉用耐火材料的使用研究.耐火材料,1997年第一期,20-25.
[4]贾振海.氩氧炉生产中、低、微碳铬铁的新工艺[J].铁合金,2005,(2):11-16.
[5]杨汝清.智能控制工程[M].上海:上海交通大学出版社,2001,(1):36-60.
[6]王永初.智能控制理论与系统的发展评述.华侨大学学报,2004,25(1):1-4.
[7] Ling KV,DexterAL.Expert control of air conditioning. Plant.Automation ,1994,35-38.
[8]孙增圻.智能控制理论与技术.北京:清华大学出版社,1997,100-170.
[9]曲英等.炼钢学原理.北京:冶金工业出版社,1980,150-210.
[10] Regawa T,Otatani T.Development of CaO-MgO refractories and their effects on refined mechanism of extremely clean steel[A].Proceedings of Unitect:93[C].Sao Paulo,Brazil,1993.
[11] Xiang Zhongyong,GUO Qingdi.Regenerative Blast Furnace Hot Stove.Beijing:Metallurgical Industry Press,1988.
[12]欧俭平,蒋绍坚,马爱纯等.高温空气燃烧技术用于钢包烘烤的节能效果.冶金能源,2003,22(6):30-32.
[13]刘浏,佟溥翘,崔淑贤.溅渣层形成和对炉衬保护机理的研究.钢铁, 1999年11月,Vol.34,No.11.
[14] CharlesJ,Messina and John R.Paules.Paules,Steelmaking Conference Proceedings,1996:153-155.
[15]赵沛.炉外精炼及铁水预处理实用技术手册.北京:冶金工业出版社,2004:307.
[16] Aoki T.The mechanism of the back-attack phenomenon on a bottom blowing tuyere investigated in model experiments〔J〕.Tetsu-to-hagane,1990,76(11):1996-2003.
[17]郑玉贵,姚治铭,柯伟.流体力学因素对冲刷腐蚀的影响机制.腐蚀科学与防护技术,2000年01月,第12卷第1期,110-114.
[18]王以法.人工智能钻井实时专家控制系统研究.石油学报,2003,Vol.23,NO.2:84-86.
[19] Gerald Gibson.A supervisory controller for optimization of building central cooling system.ASHRAE Transactions,2001,No.2:431-438.
[20] Cai Zixing,Wang Y,Cai J.A real-time expert control system.AI in Engineering,1996,Vol.10,No.4:317-322.
[21]陶永华.新型PID控制及其应用[M].北京:机械工业出版社,2003.7:1-7.
[22]施月循,邢玉禄,王寿同等.钢铁冶金工艺学[M].沈阳:东北大学出版社,1996.
[23]戴云阁,李文秀,龙腾春..现代转炉炼钢[M].沈阳:东北大学出版社,1998.
[24]孟劲松.复吹转炉终点控制的实践和理论[J].鞍钢技术,1995,(8):7-10.
[25] Teruki Makino,Shigeru Omiya,Nobukazu Kitagawa.Automatic Operation of Converters at Mizushima Works[J].Steelmaking Conference Proceedings, 1989:67-71.
[26] Rob Boom.Sublance Technology for Oxygen Steelmaking[J].Steel Technology International,1994,(2):90-94.
[27]陶钧,柴天佑,李小平等.转炉炼钢智能控制方法及应用[J].控制理论与应用,2001,18(增刊):129-133.
[28]孟劲松.复吹转炉终点控制的实践和理论[J].鞍钢技术,1995,(8):7-10.
[29]王顺晃,杜大川,刘宏才等.顶底复吹转炉的计算机控制[J].北京科技大学学报,1992,(5):563-568.
[30]屠海,洪新.转炉炼钢终点锰磷动态控制技术的开发[J].上海金属,2002,24(2):27-30.
[31]易继楷,侯媛彬编著.智能控制技术[M].北京:北京工业大学出版社,2003.
[32]李润生,李延辉,胡学军.神经网络在冶金中的应用[J].钢铁研究,1998(2):48-50.
[33]陈国翠.我国铬铁合金生产与发展[J].铁合金,2000,(5):40-42.
[34]王雅贞,张岩,张红文编.氧气顶吹转炉炼钢工艺与设备[M].北京:冶金工业出版社,2001.
[35] Isao Tone,Nobukazu Kitagawa,Katsuo Ishida.A Fully Automated BOF Refining System at the Mizushima Works..1991 Steelmaking Conference Proceedings:375-378.
[36] N.Ramaseder,W.Pirklbauer,J.Kalisch.Slanted.Sublance System for Blowing Process Control.MPT,1993(3):42-45.
[37] W.F.Nirschel,R.P.Stone, C.J.Carr.Overview of Steelmaking Process Control Sensors for BOF[J].Ladle and Continuous Casting Tundish, Iron and Steelmaker,,2001,28:61-65.
[38]王海,陈宏伟,张丽荣.氧气顶吹转炉自动控制系统的开发与应用[J].电工技术杂志,2004,6:57-60.
[39] Ken-Iwamura, Masao FurusawaMasakazu Miyamoto.New Endpoint Control System with Auto-parameter-turning in BOF[J] . Steelmaking Conference Proceedings,1995:715-719.
[40]刘文林.ControlLogix系统在AOD炉控制系统改造中的应用[J].沈阳工程学院学报(自然科学版),2006,(1):54-57.
[41]张鉴.终点碳控制的现状和前景[J].特殊钢,1995,16(4):1-6.
[42]蒋晓放.宝钢300 t转炉复吹现状与发展[J].宝钢技术,2002,(3):1-5.
[43]房荣波,魏元,杨海峰.转炉钢水出钢温度的预测[J].鞍钢技术,2002,(4):24-26.
[44]张润宇,肖兵,张文弟.转炉钢水含碳量的估计[J].自动化学报,1993,19 (3):381-383.
[45]李少远,席裕庚.智能控制的新进展[J].控制与决策,2000,15(1):1-5 .
[46] Yun S Y, Chang K S. Dynamic Predication Using Neural Network for Automation of BOF Process in Steel Industry.I&SM,1996(8):37-42 .
[47] Hint K J.Extending the Functional Equivalence of Radial Basis Function Network and Fuzzy Inference System[J].IEEE Trans on Neural Networks, 1996,(3):776-781 .
[48] Nicolas Plcon, Frederlc Alexandre, Patrick Bresson, et al.Artlficlal Neural Nemorks for the Presetting of a Steel Temper Mill[J].IEEEEXPET ,1996,(2): 22-27 .
[49] Tao J, Wang X.Intelligent Control Method and Application for BOF Steelmaking Process[J]. Proceedings of the IFAC World Congress,2002.
[50] Robert H N.Theory of the Back-propagation Neural Network. Proc,UCNN,Washington D.C.,1989(1):593-605.
[51] Tapas Kanungo, David M, Mount, Nathan S Netanyahu, et al.A Efficient k- Means Clustering Algorithm:Analysis and Implementation[J].IEEE Tansactions on Pattern Analysis and Machine Intelligence, 2002,24(7): 881-892.
[52]飞思科技产品研发中心.Matlab6.5辅助神经网络分析与设计[M].北京:电子工业出版社,2003,1.
[53]喻淑仁.转炉炼钢过程静态控制及数学模型[J].炼钢,1995,(3):55-60.
[54]李新民,李强,田灵虎.AOD炉的工艺及设备特征.冶金设备,1995 ,(4):46-49.
[55]李强,李向东.AOD炉倾动机构的特征分析.特钢技术,1998 ,(3):37-39.
[56]刘莹,钟晓勤,瞿志豪.AOD炉倾动力矩精确计算及耳轴中心安全高度位置确定.上海应用技术学院学报,2007,7(2):87-90.
[57] Soetanto,D. Lapierre,L. Pascoal.An adaptive non-singular path-following control of dynamic wheeled robots.Decision and Control,2003.Proceedings,42nd IEEE Conferrence on, P.1765-1770,2003.
[58] Ohishi,K.Kuramochi,K.Miyazaki,T.Koide,D.Tokumaru,H. Robust feedforward tracking servo system Considering force disturbance for optical disk recording.Control Applications,2004.Proceedings of the 2004 IEEE International Conference on,P527-532,2004.
[59] Wieland,P.Meurer,T.Graichen,K.Zeitz,M. Feedforward control design under input constraints for a tubular reactor model.Decision and Control(CDC),IEEE Conference on,San Diego,CA,P3968-3973,2006.
[60] K.Shimizu.Wear of Refractories for AOD Furnace.Taikabutsu Overseas,2001,53(4):198-204.
[61] W.Cao,Y.Miyamoto.Direct Slicing from AutoCad Solid Models for Rapid Prototyping.International Jounal of Advanced Manufacturing Technology, 2003,21(10):739-742.
[62] Jiantao Lu,Sihai Qiu.The vector control based on MRAS speed sensorless induction motor drive.Intelligent Control and Automation,the Fifth word Congress,P4550-4553,2004.
[63] S.Vaez-zadeh,E.Jalali.Combined Vector Control And Direct Torque Control Method For High Performance Induction Motor,Energy Conversion&Management, 48(12): P3095-3101,2007.
[64] Shanshan Wu,Yongdong Li,Zedong Zheng.Speed Sensorless Vector Control of Induction Motor Based on Full-Order Flux Observer.5th International Power Electronics and Motion Control Conference, (4):P1892-1895,2006.
[65]钟淑荣.国外炼钢生产的发展与21世纪的技术发展[J].鞍钢技术,1998,(3):5-8.
[66]韩永东,曾朝华,魏国芳.底吹氩气系统的建模、分析与智能控制.河北冶金,2002年第5期.
[67]王海江,朱宏利,魏季和等.不锈钢的侧顶复吹AOD精练过程.上海金属,2005年9月.
[68]董锋斌,蒋军,皇金锋等.钢包精炼炉智能底吹氩控制系统.机床与液压,2006年第8期.
[69]王海川,张友平,李文超.转炉吹炼中低碳锰铁的应用分析.铁合金, 2000年第6期.
[70]董锋斌,皇金锋.PLC在钢包底吹氩系统中的应用.仪表技术与传感器, 2006年第3期.
[71]王东,包燕平,徐保美等.VOD精炼过程的水模型实验.特殊钢,2006年3月第27卷第2期.
[72]杨阿娜,刘学华,蔡开科.炼钢过程中氧的控制.钢铁研究学报,2005年6月第17卷第3期.
[73]张国栋,肖景国,商桂海等.济钢25吨转炉自动化控制系统改进.山东冶金,2003,(5):44-47.
[74] Hint K J.Extending the Functional Equivalence of Radial Basis Function Network Inference System[J].IEEE Trans on Neural Networks,1996,(3):776-781.
[75] SIMATIC Software System Software for S7-300 and S7-400 ProgramDesign[M].Nuremberg:Siemens AG,1998.
[76] Wilson EL,Karr CL,Bennett JP.An adaptive,intelligent control system for slag foaming.Applied Intelligence,2004, Vol.20, No.2,P 165-177.
[77] Y.D. Yang, I.D. Sommerville,AMcLean. SOME FUNDAMENTAL CONSIDERATIONS PERTAINING TO OXIDE MELT INTERACTIONS AND THEIR INFLUENCE ON STEEL QUALITY.Transactions of the Indian Institute of Metals,2006 ,vol.59 ,no.5,P.655-669.
[78] Bachinina,G.A.,Botman,,S.V..Results of studying the physical properties of blast-furnace slags at the Il'ich Integrated Steel Plant . Steel in translation, V.26,no.10, P.6-12.
[79] J.Y.Yen,D.Mantha,L.Evrard.Effect of composition and temperature on viscosities of lead blast furnace slags.Minerals&MetallurgicalProcessing,2006,vol.23,no.3,P.165-170.
[80] M.S.C.Y-Y ih,et al.Optimal Variable Structure Controller for DC Motor Speed Control.IEEE proc.Pt.D.1984,131(1):41-48.
[81] V.I.Vtkin.Variable Structure Systems.Present and Future,Auto. Emote Control,1983(8):15-17.
[82]刘寿昌,杨希富著.锰铁合金用Ca0渣系脱磷的热力学研究.铁合金,2001 No.4,Tot.159,P8-11.
[83]陶钧.转炉炼钢过程智能控制方法及其应用的研究「D].东北大学,2001.
[84] Wilson EL,Karr CL, Bennett JP. An adaptive, intelligent control system for slag foaming.Applied Intelligence,2004 Vol.20 No.2,P 165-177.
[85] D.k.Belashchenko,B.R.Gel'chinskii,E.V.Dyul'dina.Simulation of multicomponent blast-furnace slags.Steel in translation,2004 Vol.34 No.9,P13-17.
[86]刘玠,蒋慎言,陈大纲.炼钢生产自动化技术.冶金工艺出版社,2006.
[87]房荣波,魏元,杨海峰.转炉钢水出钢温度的预测[J].鞍钢技术,2002,(4):24-26.
[88]刘柏松.RH-MFB脱碳过程模型与工艺优化.河北理工大学硕士学位论文,2005.
[89]李顺健.基于ANN的攀钢转炉炼钢终点控制模型.电子科技大学工程硕士学位论文,2008.
[90]李清.炼钢电弧炉全程动态模型与仿真研究.上海大学博士学位论文,2003.
[91]陈昌.转炉炼钢终点控制系统建模研究.沈阳工业大学硕士学位论文,2007.
[92]赵进.自动化炼钢技术的应用与研究.辽宁科技大学工程硕士学位论文, 2007.
[93]史国敏.侧顶复吹条件下AOD转炉熔池内流体流动现象的数学和物理模拟.上海大学博士学位论文,2007.
[94]郁能文.多功能RH精炼过程的数学和物理模拟.上海大学博士学位论文, 2001.
[95]胡汉涛.钢液RH精炼非平衡脱碳过程的数学模拟.上海大学博士学位论文, 2005.
[96]李波.合成氨脱碳系统的模拟与研究.大连理工大学硕士学位论文,2001.
[97]鱼洋洋.重钢转炉复吹工艺数理模拟研究.重庆大学硕士学位论文,2006.
[98]韩传基,刘柏松,艾立群等.RH-MFB精炼过程脱碳数学模型及工艺研究.钢铁研究学报,2007,19(4):17-22.
[99]廉宇峰,尤文,赵彬等.AOD冶炼脱碳过程的非等间隔灰色优化模型研究.应用科技,2010,37(1):57-60.
[100]胡文华.AOD冶炼脱碳反应模型与计算机模拟.冶金设备,2005,(1):19-24.
[101]李士琦,江国利,路俊萍等.AOD冶炼不锈钢工艺模型的研究.特殊钢, 2009,30(1):29-31.
[102]沈春飞,蒋兴元,尹世友等.AOD精炼304不锈钢渗氮、脱氮的计算模型与应用.特殊钢,2009,30(5):28-30.
[103]李永祥,马嵩,邹宗树.利用数学模型分析复吹转炉吹炼工艺.材料与冶金学报,2007,6 (4):260-263.
[104]赵成林,马嵩.复吹转炉炼钢过程数学模拟:动力学模型.材料与冶金学报,2004,3(2):99-103.
[105]李青,马志刚,杜斌.RH脱碳模型的建立和测试.上海大学学报(自然科学版),2002,8(2):119-123.
[106]刘柏松,艾立群.宝钢RH-MFB深脱碳模型.河北理工大学学报(自然科学版),2007,29(3):27-30.
[107]姜周华,阮小江,李阳等.兑铁水条件下UHP电弧炉脱碳数学模型.东北大学学报(自然科学版),2009,30(3):388-391.
[108]林东,赵成林,张贵玉等.复吹转炉炼钢过程脱碳模型.钢铁研究,2006,34(6):12-15.
[109]徐立云,张斌,王景成等.基于动态数学模型的冶金加热炉实时仿真器的研究.控制与决策,2002,17(2):207-210.
[110]王哲.矿热炉与AOD炉双联法冶炼不锈钢的设计与研究.西安建筑科技大学硕士学位论文,2009.
[111]马志刚,谢树元,杜斌等.脱碳模型在宝钢RH精炼的应用.中国冶金,2009,19(10):1-3.
[112]伍建军.冶金过程数学模型方法与软件调试浅谈.有色金属设计与研究,2008,29(2):9-11.
[113]陈义胜,贺友多,苍大强等.一个RH处理过程的脱碳动态模型.包头钢铁学院学报,2002,21(2):108-111.
[114]孙钻.智能控制技术在转炉终点动态控制建模中的应用研究.武汉科技大学硕士学位论文.
[115]崔衡,唐德池,包燕平.中间包底吹氩水模型试验及冶金效果.钢铁钒钛,2010,31(1):36-39.
[116]鱼洋洋.重钢转炉复吹工艺数理模拟研究.重庆大学硕士学位论文,2006.
[117]黄可为,杜斌.转炉合金最小成本控制模型.控制理论应用,2003,(2):11-13.
[118]王永富,李小平等.转炉炼钢动态过程预设定模型的混合建模与预报.东北大学学报,2003,24(8):715-718.
[119]陶钧,谢书明,柴天佑.转炉炼钢控制模型的研究与展望.钢铁,1999,34(8):69-72.
[120]韩敏,黄晓清,谭明祥等.转炉炼钢熔池碳含量动态变化研究.冶金自动化,2009,33(5):28-32.
[121]黄金侠.转炉炼钢终点静态控制预测模型.天津大学硕士学位论文,2005.
[122]蔡开科.转炉冶炼低碳钢终点氧含量控制.钢铁,2009,44(5):27-31.
[123]林东,赵成林,张贵玉等.转炉冶炼脱磷过程数学模型.材料与冶金学报,2008,7(2):85-88.
[124]周远华.重钢转炉复吹的模拟研究.重庆大学工程硕士学士论文,2004.
[125]张琳.基于GA-BP混合算法的转炉终点优化控制模型.重庆大学硕士学位论文,2004,
[126]周立平.AOD转炉技术.《一重技术》,2007,(1):12-14.
[127]池和冰,魏季和,舒杰辉等.不锈钢AOD转炉精炼过程数学模拟初探.上海金属,2007,29(4):49-58.
[128]宋海鹰,桂卫华,阳春华等.基于核偏最小二乘法的动态预测模型在铜转炉吹炼中的应用.中国有色金属报,2007,17(7):1201-1206.
[129]甄云璞,冯聚和.基于神经网络的转炉冶炼终点锰、磷静态预报模型.河北理工学院学报,2007,29(2):16-19.
[130]周洪煜,王驹,陈晓锋等.基于虚拟仪器技术的炼钢转炉传动机构在线监测与故障诊断系统.计算机测量与控制,2007,15(1):14-15.
[131]张靓.济钢转炉的自动化控制系统.中国冶金,2007,17(4):48-50.
[132]夏元弟.交流变频调速在转炉倾动控制中的应用.工业计量,2007,17(4):15-17.
[133]袁树明,王福明.利用人工神经网络系统提高转炉终点碳温双命中率.山东冶金,2007,29(2):63-65.
[134]关春丽,王明春,刚占库.通钢120t转炉实现计算机自动炼钢的实践.钢铁,2007,42(5):82-86.
[135]万学武,杨文栋,汤旭等.延长炼铜转炉寿命的实践.中国有色冶金,2007,(1):40-42.
[136]曲丽萍,曲永印,白晶等.氧气顶吹转炉智能控制策略的研究.人工智能技术应用,2007,(3):24-27.
[137]万雪峰,张贵玉,林东等.转炉吹炼过程中耗氧比模型.材料与冶金学报,2007,6(1):7-11.
[138]张大勇,张彩军.转炉复吹龄同步技术的优化与实践.河南冶金,2007,15(3):40-42.
[139] P.D.Hubbeling,G.A.Oostermeijer.转炉副枪与动态控制.钢铁,2007,42(4):83-86.
[140]陈俊东,张彩军,冯聚和.转炉静态机理模型与节能降耗.河北理工学院学报,2007,29(1):32-35.
[141]闫开宇,孙健.转炉倾动机构研究.《一重技术》,2007,(2):9-10.
[142]付海涛.转炉氧气流量中温度压力补偿的实现.冶金自动化,2007,(4):64-65.
[143]盛坚,杨永军.采用高温黑体式光纤温度计在线监测感应加热真空炉液相温场得可行性.国防军工热学、流量计量与测试技术交流会论文专集,2008.
[144]谢书明,高宪文,柴天佑.基于灰色模型的转炉炼钢终点预报研究.钢铁研究学院,1999,11(4):9-12.
[145]陶钧,谢书明,柴天佑.基于遗传算法和径向基函数神经网络的转炉炼钢模型.系统仿真学报,2000,12(3):241-244.
[146]谢书明,柴天佑,陶钧.一种转炉动态终点预报的新方法.自动化学报,2001,27(1):136-139.
[147]王永富,李小平,柴天佑等.转炉炼钢动态过程预设定模型的混合建模与预报.东北大学学报(自然科学报),2003,24(8):715-718.
[148]陶钧,柴天佑,李小平等.转炉炼钢智能控制方法及应用.控制理论与应用,2001,18(S):129-133.
[149]陈忠伟,袁守谦.LD转炉冶炼的静态数学模型及实现.炼钢,2000,16(5):31-34.
[150]吴晓东,刘旭兰,郑建忠.LF精炼终点钢水温度的预报模型.炼钢,2007,23(6):43-46.
[151]武拥军,姜周华,姜茂发等.LF炉精炼过程钢水温度预报模型.钢铁研究学报,2002,14(2):9-12.
[152]池和冰,魏季和,舒杰辉等.不锈钢AOD转炉精炼过程数学模拟初探.上海金属,2007,29(4):49-58.
[153]王子中,刘达,张炳元.顶吹转炉最佳炉龄数学模型研究.冶金经济与管理,1995,(5):32-33.
[154]潘玉华,马萍.复吹转炉底吹气流数学模型研究.钢铁,1994,29(5):17-21.
[155]温宏愿,赵琦,陈延如等.光谱图像分析用于转炉终点实时预测.光电工程,2008,35(5):135-139.
[156]谢书明,孙凯,陈昌.基于RBF神经网络的转炉炼钢终点预报.沈阳工业大学学报,2006,28(4):405-408.
[157]柴天佑,谢书明,杜斌等.基于RBF神经网络的转炉炼钢终点预报.中国有色金属学报,1999,9(4):868-872.
[158]温宏愿,赵琦,陈延如.基于辐射信息分析的转炉终点控制回归模型.仪器仪表学报,2008,29(8):1633-1637.
[159]谢书明,高宪文,柴天佑.基于灰色模型的转炉炼钢终点预报研究.钢铁研究学报,1999,11(4):9-12.
[160]田民乐,刘少民.基于模糊神经网络的炼钢炉静态建模.北京科技大学学报,1998,20(2):136-139.
[161]甄云璞,冯聚和.基于神经网络的转炉冶炼终点锰、磷静态预报模型.河北理工学院学报,2007,5(2):16-19.
[162]陶钧,谢书明,柴天佑.基于遗传算法和径向基函数神经网络的转炉炼钢模型.系统仿真学报,2000,12(3):241-244.
[163]袁树明,王福明.利用人工神经网络系统提高转炉终点碳温双命中率.山东冶金,2007,29(2):63-65.
[164]汤天宇,杜德信,柳孝诚等.攀钢转炉炉龄数学模型.钢铁钒钛,1995,16(2):1-8.
[165]田建国.应用多元回归技术预测CAS终点钢水温度.中国冶金,2006,16(1):32-34.
[166]万雪峰,张贵玉,林东等.转炉吹炼过程中耗氧比模型.材料与冶金学报,2007,6(1):7-11.
[167]李伟,贺友多.转炉底吹气体过程中的三维数学模型.包头钢铁学院学报,1994,13(1):45-48.
[168]陈俊东,张彩军,冯聚和.转炉静态机理模型与节能降耗.河北理工学院学报,2007,29(1):32-35.
[169]贾凌云,高留治.转炉炼钢车间设计的基本数学模型.钢铁,1996,31(4):22-26.
[170]喻淑仁.转炉炼钢过程静态控制及其数学模型.炼钢,1995,(3):55-60.
[171]王永富,李小平,柴天佑等.转炉炼钢动态过程预设定模型的混合建模与预报.东北大学学报(自然科学报),2003,24(8):715-718.
[172]林东,赵成林,张贵玉等.复吹转炉炼钢过程脱氧碳模型.钢铁研究,2006,34(6):12-15.