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| 1 | 工业互联网驱动的流程工业智能优化制造新模式研究展望显示文摘本文首先综述了各国工业互联网的发展愿景和流程工业运行现状,给出流程工业智能优化制造的内涵,并分析了工业互联网驱动的流程工业智能优化制造的机遇与挑战;结合工业互联网作为实现智能制造纵向集成、端到端集成和横向集成的基础设施,探讨了工业互联网驱动的流程工业智能优化制造新模式:(1)工业互联网驱动的流程制造企业智能优化制造模式,其包括:制造流程全局优化;驱动集中式企业资源计划(Enterprise resource planning,ERP)与制造执行系统(Manufacturing execution system,MES)向分散式数字孪生驱动的生产要素管理与决策一体化系统发展;驱动过程控制系统(Process control system,PCS)/MES/ERP三层结构向智能自主控制系统和人机互动与协作的管理与决策智能化系统两层结构的决策与控制一体化系统发展;(2)面向产品全生命周期的跨企业流程工业智能优化制造模式;最后,给出了实现上述流程工业智能优化制造模式的研究方向. | 柴天佑 刘强 丁进良 卢绍文 宋延杰 张艺洁 | 2022 | 中国科学:技术科学2022，52，1： | 41 |
| 2 | Dynamic simulation of gas turbines via feature similarity-based transfer learning显示文摘Since gas turbine plays a key role in electricity power generating,the requirements on the safety and reliability of this classical thermal system are becoming gradually strict.With a large amount of renewable energy being integrated into the power grid,the request of deep peak load regulation for satisfying the varying demand of users and maintaining the stability of the whole power grid leads to more unstable working conditions of gas turbines.The startup,shutdown,and load fluctuation are dominating the operating condition of gas turbines.Hence simulating and analyzing the dynamic behavior of the engines under such instable working conditions are important in improving their design,operation,and maintenance.However,conventional dynamic simulation methods based on the physic differential equations is unable to tackle the uncertainty and noise when faced with variant real-world operations.Although data-driven simulating methods,to some extent,can mitigate the problem,it is impossible to perform simulations with insufficient data.To tackle the issue,a novel transfer learning framework is proposed to transfer the knowledge from the physics equation domain to the real-world application domain to compensate for the lack of data.A strong dynamic operating data set with steep slope signals is created based on physics equations and then a feature similarity-based learning model with an encoder and a decoder is built and trained to achieve feature adaptive knowledge transferring.The simulation accuracy is significantly increased by 24.6%and the predicting error reduced by 63.6%compared with the baseline model.Moreover,compared with the other classical transfer learning modes,the method proposed has the best simulating performance on field testing data set.Furthermore,the effect study on the hyper parameters indicates that the method proposed is able to adaptively balance the weight of learning knowledge from the physical theory domain or from the real-world operation domain. | Dengji ZHOU Jiarui HAO Dawen HUANG Xingyun JIA Huisheng ZHANG | 2020 | Frontiers in Energy2020，14，4： | 1 |
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