As the steel industry expands worldwide,slag dumps with transition metals(especially chromium and vanadium)are becoming more common,posing a serious environmental threat.Understanding the properties of slags containin...As the steel industry expands worldwide,slag dumps with transition metals(especially chromium and vanadium)are becoming more common,posing a serious environmental threat.Understanding the properties of slags containing transition metal oxides,as well as how to use the slags to recover and recycle metal values,is critical.Toward this end,the University of Science and Technology Beijing(USTB)and Royal Institute of Technology(KTH)have been collaborating on slags containing transition metals for decades.The research was carried out from a fundamental viewpoint to get a better understanding of the structure of these slags and their properties,as well as industrial practices.The research focused on the three“R”s,viz.retention,recovery,and recycling.The present paper attempts to highlight some of the important achievements in these joint studies.展开更多
Railway noise barriers are an essential piece of infrastructure for reducing noise propagation.However,these barriers experience aerodynamic loads generated by high-speed trains,leading to dynamic effects that may com...Railway noise barriers are an essential piece of infrastructure for reducing noise propagation.However,these barriers experience aerodynamic loads generated by high-speed trains,leading to dynamic effects that may compromise their fatigue capacity.The most common structural design for railway noise barriers consists of vertical configurations of posts and panels.However,there have been few dynamic analyses of steel post/wood panel noise barriers under train-induced aerodynamic loads.This study used dynamic finite element analysis to assess the dynamic behavior of such noise barriers.Analysis of a 40-m-long noise barrier model and a triangular simplified load model,the latter of which effectively represented the detailed aerodynamic load,were first used to establish the model and input of the moving load during dynamic simulation.Then,the effects of different parameters on the dynamic response of the noise barrier were evaluated,including the damping ratio,the profile of the steel post,the span length of the panel,the barrier height,and the train speed.Gray relational analysis indicated that barrier height exhibited the highest correlations with the dynamic responses,followed by train speed,post profile,span length,and damping ratio.A reduction in the natural frequency and an increase in the train speed result in a higher peak response and more pronounced fluctuations between the nose and tail waves.The dynamic amplification factor(DAF)was found to be related to both the natural frequency and train speed.A model was proposed showing that the DAF significantly increases as the square of the natural frequency decreases and the cube of the train speed rises.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
要:针对当前云制造平台与企业协同调度问题研究不足,以及缺乏相关仿真系统对调度策略组合进行可视化仿真等问题,设计并开发了一个支持云制造平台-企业协同调度动态过程可视化的仿真系统。分析了系统多方面需求,提出了一个基于层次型多...要:针对当前云制造平台与企业协同调度问题研究不足,以及缺乏相关仿真系统对调度策略组合进行可视化仿真等问题,设计并开发了一个支持云制造平台-企业协同调度动态过程可视化的仿真系统。分析了系统多方面需求,提出了一个基于层次型多智能体的可扩展的平台-企业协同调度模型及系统功能架构;结合工业机器人供应链案例,考虑云制造平台层随机选择、时间最优策略以及企业层FIFO(first in first out)、SPT(shortest processing time)、EDD(earliest due date)等策略组合,构建了平台-企业协同调度仿真模型。基于Anylogic进行了系统实现,并通过多组策略组合与用例开展了仿真实验。结果表明:该系统具有制造场景可扩展、仿真过程可视化等功能特点,同时能够对平台层和企业层调度策略组合的性能进行仿真、测试及优化,从而为云制造平台-企业协同调度策略提供了一个仿真环境和验证平台。展开更多
With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process...With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.展开更多
基金Parts of the work were carried out as part of the Eco Steelmaking project funded by the Swedish Foundation for Strategic Environmental Research(MISTRA)through the Swedish Steel Producers AssociationChinese Academy of Science is acknowledged for its partial finical support through the“Transfer and commercialization of scientific and technological achievements”project(No.2020109)。
文摘As the steel industry expands worldwide,slag dumps with transition metals(especially chromium and vanadium)are becoming more common,posing a serious environmental threat.Understanding the properties of slags containing transition metal oxides,as well as how to use the slags to recover and recycle metal values,is critical.Toward this end,the University of Science and Technology Beijing(USTB)and Royal Institute of Technology(KTH)have been collaborating on slags containing transition metals for decades.The research was carried out from a fundamental viewpoint to get a better understanding of the structure of these slags and their properties,as well as industrial practices.The research focused on the three“R”s,viz.retention,recovery,and recycling.The present paper attempts to highlight some of the important achievements in these joint studies.
基金financially supported by the Swedish Transport Administration(Trafikverket)through the“Excellence Area 4”and FOI-BBT program(Grant Nos.BBT-2019-022 and BBT-TRV 2024/132497).
文摘Railway noise barriers are an essential piece of infrastructure for reducing noise propagation.However,these barriers experience aerodynamic loads generated by high-speed trains,leading to dynamic effects that may compromise their fatigue capacity.The most common structural design for railway noise barriers consists of vertical configurations of posts and panels.However,there have been few dynamic analyses of steel post/wood panel noise barriers under train-induced aerodynamic loads.This study used dynamic finite element analysis to assess the dynamic behavior of such noise barriers.Analysis of a 40-m-long noise barrier model and a triangular simplified load model,the latter of which effectively represented the detailed aerodynamic load,were first used to establish the model and input of the moving load during dynamic simulation.Then,the effects of different parameters on the dynamic response of the noise barrier were evaluated,including the damping ratio,the profile of the steel post,the span length of the panel,the barrier height,and the train speed.Gray relational analysis indicated that barrier height exhibited the highest correlations with the dynamic responses,followed by train speed,post profile,span length,and damping ratio.A reduction in the natural frequency and an increase in the train speed result in a higher peak response and more pronounced fluctuations between the nose and tail waves.The dynamic amplification factor(DAF)was found to be related to both the natural frequency and train speed.A model was proposed showing that the DAF significantly increases as the square of the natural frequency decreases and the cube of the train speed rises.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
文摘要:针对当前云制造平台与企业协同调度问题研究不足,以及缺乏相关仿真系统对调度策略组合进行可视化仿真等问题,设计并开发了一个支持云制造平台-企业协同调度动态过程可视化的仿真系统。分析了系统多方面需求,提出了一个基于层次型多智能体的可扩展的平台-企业协同调度模型及系统功能架构;结合工业机器人供应链案例,考虑云制造平台层随机选择、时间最优策略以及企业层FIFO(first in first out)、SPT(shortest processing time)、EDD(earliest due date)等策略组合,构建了平台-企业协同调度仿真模型。基于Anylogic进行了系统实现,并通过多组策略组合与用例开展了仿真实验。结果表明:该系统具有制造场景可扩展、仿真过程可视化等功能特点,同时能够对平台层和企业层调度策略组合的性能进行仿真、测试及优化,从而为云制造平台-企业协同调度策略提供了一个仿真环境和验证平台。
基金supported by the National Natural Science Foundation of China(62225302,623B2014,and 62173023).
文摘With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.
