PDF(2019 KB)
一种基于数字孪生云平台的炼铁过程智能优化服务
Heng Zhou, Chunjie Yang, Youxian Sun
工程（英文） ›› 2021, Vol. 7 ›› Issue (9) : 1274-1281.
PDF(2019 KB)
PDF(2019 KB)
一种基于数字孪生云平台的炼铁过程智能优化服务
Intelligent Ironmaking Optimization Service on a Cloud Computing Platform by Digital Twin
工业过程多目标优化研究因算法和设备的不完善，已经大幅限制了工业过程的智能化发展。为了提升流程工业过程的操作水平，本文提出了一种基于云服务平台和分布式系统的混合智能优化框架。在这个智能优化系统中，工业实时数据首先暂存于本地服务器，经过清洗整理后上传至云数据库中；然后在可拓展的云平台上部署分布式系统用于运行智能优化算法；最后将基于深度学习和进化算法的多目标混合优化算法打包上传至云计算平台。通过将多目标优化服务运行于数字孪生云上工厂，钢铁厂高炉铁水产量增加了83.91 t·d﹣1，焦炭比降低了13.50 kg·t﹣1，硅含量平均降低了0.047%。
The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes. To improve the operational levels of the process industries, we propose a multi-objective optimization framework based on cloud services and a cloud distribution system. Real-time data from manufacturing procedures are first temporarily stored in a local database, and then transferred to the relational database in the cloud. Next, a distribution system with elastic compute power is set up for the optimization framework. Finally, a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process. With the application of this optimization service in a cloud factory, iron production was found to increase by 83.91 t∙d-1, the coke ratio decreased 13.50 kg∙t-1, and the silicon content decreased by an average of 0.047%.
Cloud factory / Blast furnace / Multi-objective optimization / Distributed computation
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