镁砂熔炼过程全厂电能分配实时多目标优化方法研究

孔维健; 柴天佑; 丁进良; 吴志伟

doi:10.3724/SP.J.1004.2014.00051

镁砂熔炼过程全厂电能分配实时多目标优化方法研究

doi: 10.3724/SP.J.1004.2014.00051

1.
流程工业综合自动化国家重点实验室(东北大学) 沈阳 110004

基金项目:

国家重点基础研究发展计划（973计划）（2009CB320601）;国家自然科学基金（61020106003，60904079，61134006）资助

详细信息

作者简介:
丁进良博士，东北大学副教授. 主要研究方向为复杂工业过程的智能优化控制.E-mail：jlding@mail.neu.edu.cn

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出版历程
- 收稿日期: 2012-05-13
- 修回日期: 2012-10-12
- 刊出日期: 2014-01-20

A Real-time Multiobjective Electric Energy Allocation Optimization Approach for the Smelting Process of Magnesia

1.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004

Funds:

Supported by National Basic Research Program of China (973 Program) (2009CB320601) and National Natural Science Foundation of China (61020106003, 60904079, 61134006)

摘要

摘要: 镁砂熔炼过程具有多工况、群炉并行生产、高能耗等特点.在全厂供电容量约束下，为了最大化能源使用效率，需要根据全厂每台炉子的工况变化实时分配电能，实现全厂镁砂单位能耗与平均品位的多目标优化. 本文基于最小二乘支持向量机技术建立了镁砂熔炼过程全厂电能分配优化模型.根据不同工况下降低镁炉供电量对镁砂熔炼过程的影响程度，提出了基于工况优先级的电能分配策略.根据主熔工况下镁砂产量与品位指标函数的特性分析，推导出主熔工况下电能分配模型决策变量维数缩减的条件. 为了提高多目标优化算法的运行效率，设计了一种快速非支配解集构造方法，用来提高传统多目标粒子群优化算法的寻优效率. 基于标准测试问题与现场实际例子对所提出的方法进行了检验.基于现场例子的实验结果证明所提出的方法能够避免工厂出现的用电超容量情况，并且提高了全厂用电效率.
- 电熔镁炉 /
- 维数缩减 /
- 非支配解集构造 /
- 多目标优化
Abstract: The process of smelting magnesia grain is multiple-modes, parallel-operating and high energy-consuming. To maximize the energy efficiency within the constraint of power capacity, it is desired to allocate the limited amount of electric energy to a number of furnaces for optimizing the plant-wide yields and grade of magnesia. First the allocation optimization model is developed by the least squares support vector machine. The priority of operating mode and dimension reduction strategies are proposed to simplify the optimization problem. A novel ranking approach is devised to find the non-dominating set in a population efficiently. The approach is used to improve the real-time performance of traditional multi-objective particle swarms optimization. The proposed approach is validated by a factual case.
- Electric smelting magnesia furnace /
- dimension reduction /
- non-dominated set /
- multiobjective optimization

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参考文献(37)

[1]	Qian Zhi-Rong, Fan Guang-Ju. Handbook of Refractory Matter. Beijing: Metallurgical Industry Press, 1995. 25-37 (钱之荣, 范广举. 耐火材料实用手册. 北京: 冶金工业出版社, 1995. 25-37)
[2]	Gu Li-De. Special Refractory Matter. Beijing: Metallurgical Industry Press, 2000. 150 (顾立德. 特种耐火材料. 北京: 冶金工业出版社, 2000. 150)
[3]	Hu Qing-Fu. Manufacture and Application of Magnesium Compound. Beijing: Chemical Industry Press, 2004. 35-40 (胡庆福. 镁化合物生产与应用. 北京: 化学工业出版社, 2004. 35-40)
[4]	Xue Feng, Gu Gen-Hua. The energy-saving way of magnesia production. Energy Conservation, 1996, (6): 44-46(薛丰, 顾根华. 电熔镁砂生产的节能途径. 节能, 1996, (6): 44-46
[5]	Wu Yong-Jian, Zhang Li, Yue Heng, Chai Tian-You. Intelligent optimal control based on CBR for fused magnesia production. Journal of Chemical Industry and Engineering, 2008, 59(7): 1686-1690 (吴永建, 张莉, 岳恒, 柴天佑. 基于案例推理的电熔镁炉智能优化控制. 化工学报, 2008, 59(7): 1686-1690)
[6]	Wu Z W, Chai T Y, Fu J, Sun J. Hybrid intelligent optimal control of fused magnesium furnaces. In: Proceedings of the 49th IEEE Conference on Decision and Control. Atlanta, GA, USA: IEEE, 2010. 3313-3318
[7]	Wu Zhi-Wei, Chai Tian-You, Fu Jun, Yan Zhan-Wei. Intelligent setpoints control of smelting process of fused magnesium furnace. Control and Decision, 2011, 26(9): 1417-1420 (吴志伟, 柴天佑, 付俊, 闫占伟. 电熔镁炉熔炼过程的智能设定值控制. 控制与决策, 2011, 26(9): 1417-1420)
[8]	Kong W J, Chai T Y, Yang S X, Ding J J. A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing. Applied Soft Computing Journal, 2013, 13(5): 2960-2969
[9]	Wiesemann W, Kuhn D, Rustem B. Multi-resource allocation in stochastic project scheduling. Annals of Operations Research, 2012, 193(1): 193-220
[10]	Su Zhao-Pin, Jiang Jian-Guo, Liang Chang-Yong, Zhang Guo-Fu. A distributed algorithm for parallel multi-task allocation based on profit sharing learning. Acta Automatica Sinica, 2011, 37(7): 865-872
[11]	Zhou Wei, He Jian-Min, Yu De-Jian. Traffic flow hidden measure and assignment model for the uncertain direction military traffic network. Acta Automatica Sinica, 2012, 38(2): 315-320 (周伟, 何建敏, 余德建. 非定向军事路网交通流隐蔽性测度及分配模型. 自动化学报, 2012, 38(2): 315-320)
[12]	Cheng T C E, Lin B M T, Huang H L. Resource-constrained flowshop scheduling with separate resource recycling operations. Computers and Operations Research, 2012, 39(6): 1206-1212
[13]	Riera-Ledesma J, Salazar-González J J. A column generation approach for a school bus routing problem with resource constraints. Computers and Operations Research, 2013, 40(2): 566-583
[14]	Solimanpur M, Sattari H, Abazari A M. Optimum process plan selection via branch-and-bound algorithm in an automated manufacturing environment. International Journal of Operational Research, 2012, 13(3): 281-294
[15]	Chen Jie, Chen Chen, Zhang Juan, Xin Bin. Deployment optimization for point air defense based on memetic algorithm. Acta Automatica Sinica, 2010, 36(2): 242-248 (陈杰, 陈晨, 张娟, 辛斌. 基于Memetic算法的要地防空优化部署方法. 自动化学报, 2010, 36(2): 242-248)
[16]	Gong Y J, Zhang J, Chung H S H, Chen W N, Zhan Z H, Li Y, Shi Y H. An efficient resource allocation scheme using particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2012, 16(6): 801-816
[17]	Lin C M, Gen M. Multiobjective resource allocation problem by multistage decision-based hybrid genetic algorithm. Applied Mathematics and Computation, 2007, 187(2): 574-583
[18]	Yin P Y, Yu S S, Wang P P, Wang Y T. Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization. Applied Mathematics and Computation, 2007, 184(2): 407-420
[19]	Kennedy J, Eberhart R C. Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway, NJ: IEEE, 1995. 1942-1948
[20]	Eberhart R C, Shi Y H, Kennedy J. Swarm Intelligence. San Francisco: Morgan Kaufmann Publishers, 2001
[21]	Navalertporn T, Afzulpurkar N V. Optimization of tile manufacturing process using particle swarm optimization. Swarm and Evolutionary Computation, 2011, 1(2): 97-109
[22]	Coello C A C, Van Veldhuisen D A, Lamont G B. Evolutionary Algorithms for Solving Multi-Objective Problems. New York: Kluwer Academic Publishers, 2002
[23]	Coello C A C, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 256-279
[24]	Knowles J, Corne D W. Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary Computation, 2000, 8(2): 149-172
[25]	Hu Guang-Hao, Mao Zhi-Zhong, He Da-Kuo. Multi-objective PSO optimization algorithm based on two stages-guided. Control and Decision, 2010, 25(3): 404-415 (胡广浩, 毛志忠, 何大阔. 基于两阶段领导的多目标粒子群优化算法. 控制与决策, 2010, 25(3): 404-415)
[26]	Mostaghim S, Teich J. Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Indianapolis, Indiana, USA: IEEE, 2003. 26-33
[27]	Jia Shu-Jin, Du Bin, Yue Heng. Local search and hybrid diversity strategy based multi-objective particle swarm optimization algorithm. Control and Decision, 2012, 27(6): 813 -826 (贾树晋, 杜斌, 岳恒. 基于局部搜索与混合多样性策略的多目标粒子群算法. 控制与决策, 2012, 27(6): 813-826)
[28]	Zhang Y, Gong D W, Ding Z H. A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Information Science, 2012, 192: 213-227
[29]	Deb K, Pratap A, Agarwal S, Meyrivan T. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197
[30]	Ikesue A, Yoshitomi J, Shikano H. Dynamic wearing test for magnesia-carbon refractories using induction furnace. Tetsu-to-Hagané, 1991, 77(3): 391-397
[31]	Suykens J A K, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293-300
[32]	Kong W J, Cheng W J, Ding J L, Chai T Y. A reliable and efficient hybrid PSO for parameters optimization of LS-SVM in production rate prediction. In: Proceedings of the 2010 International Symposium on Computational Intelligence and Design. Hangzhou, China: IEEE, 2010. 140-143
[33]	Qin Qin, Yue Qiang, Gu Gen-Hua, Guo Mao-Xian. DC submerged-arc furnace with twin electrodes for the fused magnesia production. Journal of Northeastern University (Natural Science), 2003, 24(7): 685-687 (秦勤, 岳强, 顾根华, 郭茂先. 双电极直流电熔镁埋弧电弧炉. 东北大学学报 (自然科学版), 2003, 24(7): 685-687)
[34]	Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257-271
[35]	Deb K, Thiele L, Laumanns M, Zitzler E. Scalable Test Problems for Evolutionary Multiobjective Optimization, Technical Report TIK-Report No. 112, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, 2001
[36]	Zitzler E, Deb K, Thiele L. Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, 2000, 8(2): 173-195
[37]	Schott J R. Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization[Master dissertation], Massachusetts Institute of Technology, UK, 1995