Modeling of LF refining process:a review

With the increasing demand for energy conservation and emission reduction,more attentions have been paid to the intelligentization,greenization and low carbonization during the transformation and upgrading of steelmaking plants.Ladle furnace(LF)refining is one of the key procedures in steelmaking process and has been widely used in steelmaking plants for its high equipment matching degree,low equipment investment and outstanding refining performance.According to the main tasks of LF refining process,the modeling methods of temperature prediction model,slag-making model,alloying model,argon blowing model and model of inclusions behavior were systematically reviewed,and the advantages and disadvantages of each modeling method were summarized.In addition,the technical framework for the future has also been proposed based on existing works,including classification of raw materials,graphic representation of knowledge,introduction,upgradation and management of device/equipment,customization of steelmaking,modeling of refining process,synergy of models,intelligentization of decision-making,automation of control,and digitization of processes and operations,aiming to provide a reference for the modeling and intelligent development of LF refining process.

Local multi-model integrated soft sensor based on just-in-time learning for mechanical properties of hot strip mill process

The mechanical properties of hot rolled strip are the key index of product quality,and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process.To solve the problem that it is difficult to measure the mechanical properties of hot rolled strip in time and accurately,a soft sensor based on ensemble local modeling was proposed.Firstly,outliers of process data are removed by local outlier factor.After standardization and transformation,normal data that can be used in the model are obtained.Next,in order to avoid redundant variables participating in modeling and reducing performance of models,feature selection was applied combing the mechanism of hot rolling process and mutual information among variables.Then,features of samples were extracted by supervised local preserving projection,and a prediction model was constructed by Gaussian process regression based on just-in-time learning(JITL).Other JITL-based models,such as support vector regression and gradient boosting regression tree models,keep all variables and make up for the lost information during dimension reduction.Finally,the soft sensor was developed by integrating individual models through stacking method.Superiority and reliability of proposed soft sensors were verified by actual process data from a real hot rolling process.