基于生成对抗网络的高炉煤气系统调度场景生成方法  

Scheduling scenario generation method of blast furnace gas system based on generative adversarial network

作　　者：王晓雪 金锋 赵博识[3] 冯为民[4] 赵珺 王伟 WANG Xiaoxue;JIN Feng;ZHAO Boshi;FENG Weimin;ZHAO Jun;WANG Wei(Key Laboratory of Intelligent Control and Optimization for Industrial Equipment,Ministry of Education,Dalian 116024,China;School of Control Science and Engineering,Dalian University of Technology,Dalian 116024,China;Department of Energy and Environmental Protection,Maanshan Iron&Steel Co.,Ltd.,Maanshan 243003,China;Shanghai Baoneng Information and Technology Co.,Ltd.,Shanghai 200940,China)

机构地区：[1]工业装备智能控制与优化教育部重点实验室,辽宁大连116024 [2]大连理工大学控制科学与工程学院,辽宁大连116024 [3]马鞍山钢铁股份有限公司能源环保部,安徽马鞍山243003 [4]上海宝能信息科技有限公司,上海200940

出　　处：《冶金自动化》2023年第3期15-23,共9页Metallurgical Industry Automation

基　　金：国家重点研发计划项目(2017YFA0700300);国家自然科学基金项目(61833003,U1908218,62103075);中央高校基本科研业务费专项资金资助项目(DUT20RC(3)045)。

摘　　要：高炉煤气(blast furnace gas,BFG)是钢铁企业炼铁过程中产生的重要二次能源,对其使用过程进行优化调度,有助于降低企业碳排,提高经济效益。针对BFG系统典型调度场景匮乏的问题,提出了一种基于改进的生成对抗网络(generative adversarial network,GAN)的BFG系统调度场景生成方法。建立了以产消差、煤气柜柜位以及可调单元消耗流量为要素的调度场景,通过对其进行分解,采用多个生成器学习拟合不同场景要素的分布特征,以降低各要素之间相互作用引起的数据波动。同时,以Wasserstein距离作为损失函数,引入梯度惩罚(gradient penalty,GP)策略以提高模型训练过程的稳定性及收敛速度。通过对国内大型钢铁企业BFG系统实际运行数据进行试验,结果表明,生成的调度场景集符合BFG系统实际运行过程数据的统计特征及时序相关性,验证了所提方法的有效性,且基于该生成场景集的调度方案可保证煤气柜能够在安全区间内稳定运行。Blast furnace gas(BFG)is an important secondary energy generated in the iron-making process of iron and steel enterprises,and optimal scheduling of its consumption can help reduce the carbon emission and improve the economic efficiency.In view of the lack of typical scheduling scenarios for BFG system,a scheduling scenario generation method for BFG system based on improved generative adversarial network(GAN)was proposed.A scheduling scenario with generation and consumption differences,gas tank level and adjustable unit consumption flow as elements was established,and by decomposing them,multiple generators were used to learn to fit the distribution characteristics of different scenario elements to reduce the data fluctuations caused by the interactions among the elements.In addition,Wasserstein distance was used as the loss function,and a gradient penalty(GP)strategy was introduced to improve the stability and convergence speed of the model training process.Experiments were conducted with the actual operation data of BFG system in a large domestic steel enterprise,and the results show that the generated scheduling scenario set conforms to the statistical characteristics and temporal correlation of the actual operation process data of BFG system,which verifies the effectiveness of the proposed method,and the scheduling scheme based on the generated scenario set can ensure that the gas tank can operate stably within the safety interval.

关 键 词：高炉煤气系统 生成对抗网络 场景生成 优化调度 

分 类 号：TF54[冶金工程—钢铁冶金]