基于智能算法的铅带轧制过程负荷分配系数优化

中国机械工程

基于智能算法的铅带轧制过程负荷分配系数优化

蒋澄灿;芮延年;廖黎莉;管淼;沈铭

苏州大学, 苏州,215021

出版日期:2016-06-25 发布日期:2016-06-24
基金资助:
国家高技术研究发展计划（863计划）资助项目(2012AA063506)；苏州市科技支撑项目(ss201344)

Optimization of Load Distribution for Lead with Rolling Processes Based on Intelligent Algorithm

Jiang Chengcan;Rui Yannian;Liao Lili;Guan Miao;Shen Ming

Soochow University,Suzhou,Jiangsu,215021

Online:2016-06-25 Published:2016-06-24

摘要/Abstract

摘要： 针对铅带连轧过程负荷分配优化问题，以均衡分配为目标，通过对遗传算法和变尺度（BFGS）混合优化算法的研究，提出了基于变尺度混合遗传算法(MSHGA)的铅带轧制过程负荷分配系数优化计算方法。通过仿真实验对算法的可行性进行了验证。结果表明，在保证铅带轧制板形、厚度、精度和性能指标参数不变的情况下，优化后的总轧制力较经验负荷算法的相应结果减小了10.7%。该技术方法的研究为铅带轧制节约电能提供了参考。

关键词: 铅带连轧, 智能算法, 负荷分配优化, 混合优化算法

Abstract: Aiming at equilibrium and based on genetic algorithm and BFGS algorithm, the new optimization method was called mutative scale hybrid genetic algorithm(MSHGA), which was used for load distribution of lead with rolling processes and was verified the feasibility through the simulation experiments. The experimental results show that the optimized total rolling force is down 10.7% from empirical load algorithm under the conditions of invariable parameters in rolling strip shape of lead, thickness, precision and performance indicators. The research lays a theoretical basis on save power of lead with rolling.

Key words: lead with rolling, intelligent algorithm, optimization of load distribution, hybrid optimization algorithm

中图分类号:

蒋澄灿, 芮延年, 廖黎莉, 管淼, 沈铭. 基于智能算法的铅带轧制过程负荷分配系数优化[J]. 中国机械工程.

Jiang Chengcan, Rui Yannian, Liao Lili, Guan Miao, Shen Ming. Optimization of Load Distribution for Lead with Rolling Processes Based on Intelligent Algorithm[J]. China Mechanical Engineering.

参考文献

[1]姜万录,张生. 改进的量子遗传算法在冷连轧机负荷分配中的应用研究[J]. 燕山大学学报,2013，37(1):8-14.

Jiang Wanlu, Zhang Sheng. Application Research on Imporved Quantum Genetic Algorithm for Load Distribution Optimization of Tandem Cold Rolling Mill[J]. Journal of Yanshan University, 2013，37(1):8-14.

[2]孙一康. 冷热轧板带轧机的模型与控制[M]. 北京:冶金工业出版社,2010.

[3]万馨. 基于智能算法的带钢轧制过程负荷分配技术的研究[D]. 苏州:苏州大学,2014.

[4]Nezhad A M, Shandiz R A, Jahromi A E. A Particle Swarm-BFGS Algorithm for Nonlinear Programming Problems[J]. Computers & Operations Research, 2013，40（4）:963-972.

[5]Liu Hui, Tian Hongqi, Liang Xifeng, et al. New Wind Speed Forecasting Approaches Using Fast Ensemble Empirical Model Decomposition, Genetic Algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks[J]. Renewable Energy, 2015，83:1066-1075.

[6]Altinkaya H, Orak M, Esen I. Artificial Neural Network Application for Modeling the Rail Rolling Process[J]. Expert Systems with Applications, 2014，41（16）:7135-7146.

[7]李维刚, 张健明. 带钢热连轧轧制力模式负荷分配的改进算法[J]. 宝钢技术,2012(4):6-10.

Li Weigang, Zhang Jianming. Improved Algorithm for Load Distribution in the Rolling Force Mode for Hot Strip Mills[J]. Baosteel Technology, 2012(4):6-10.

[8]张进之,吴增强. 热连轧生产过程负荷分配分析[J]. 世界钢铁,2013，13(1):48-53.

Zhang Jinzhi,Wu Zengqiang. Analysis on the Load Distribution of Hot Rolling Mill[J]. World Iron & Stell, 2013，13(1):48-53.

[9]李维刚,刘相华. 热连轧机轧制力成比例负荷分配的CLAD算法[J]. 东北大学学报(自然科学版),2012，23(3):352-356.

Li Weigang, Liu Xianghua.CLAD Algorithm of Rolling Ratio Load Distribution on Hot Strip Mill[J]. Journal of Northeastern University(Natural Science), 2012，23(3):352-356.

[10]Poursina M, Dehkordi N T, Fattahi A, et al. Application of Genetic Algorithms to Optimization of Rolling Schedules Based on Damage Mechanics[J]. Simulation Modelling Practice and Theory, 2012,22(3):61-73.

[11]Wang Pengfei, Peng Yan, Liu Hongmin, et al. Actuator Efficiency Adaptive Flatness Control Model and Its Application in 1250mm Reversible Cold Strip Mill[J]. Journal of Iron and Steel Research, International, 2013,20(6):13-20.

[12]Hirt G, Senge S. Selected Processes and Modeling Techniques for Rolled Products[J]. Procedia Engineering, 2014,81:18-27.

[13]Pericaro G A, Santos S R, Ribeiro A A, et al. HLRF-BFGS Optimization Algorithm for Structural Reliability[J]. Applied Mathematical Modelling, 2015,39(7):2025-2035.