The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Xinping Guan - One of the best experts on this subject based on the ideXlab platform.
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bayesian block structure sparse based t s fuzzy modeling for dynamic prediction of hot metal Silicon Content in the blast furnace
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Changchun Hua, Yana Yang, Xinping GuanAbstract:Since the hot metal Silicon Content simultaneously reflects the product quality and the thermal state of the blast furnace, its modeling is crucial and representative. In order to facilitate the realization of control, this paper proposes a Bayesian block structure sparse based Takagi–Sugeno (T–S) fuzzy modeling method, with which the main important fuzzy rules and the corresponding pivotal consequent parameters can be selected automatically to obtain a compact fuzzy model with good generalization performance. For being conjugate to the Gaussian likelihood that would lead to the associated Bayesian inference to be performed in closed form, a hierarchy of block structure sparse priori is adopted, and the variational Bayesian inference is used to solve it. The screening of model inputs and data processing appropriately consider the characteristics of the blast furnace process. The applicability and performance of the proposed method are demonstrated on no. 2 blast furnace of Liuzhou Steel in China.
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Fuzzy Classifier Design for Development Tendency of Hot Metal Silicon Content in Blast Furnace
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Junpeng Li, Yana Yang, Xinping GuanAbstract:Since the hot metal Silicon Content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal Silicon Content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal Silicon Content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms.
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wiener model identification of blast furnace ironmaking process based on laguerre filter and linear programming support vector regression
International Joint Conference on Neural Network, 2014Co-Authors: Changchun Hua, Yinggan Tang, Xinping GuanAbstract:As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems, such as the modeling and control problems. In view of these reasons, this paper is concerned with developing a Wiener model to predict the Silicon Content of blast furnace. Unlike traditional Wiener model, this paper avoids the optimization of high number of model parameters. The Wiener model here is composed of a basis filter filter expansion named Laguerre filter and a linear programming support vector regression (LP-SVR). They are used to represent the linear dynamic component and the nonlinear static element. Take the advantages that Laguerre filter can approximate linear systems with a lower model and order and LP-SVR can achieve a sparse solution, the proposed Wiener model not only improves the prediction accuracy but also reduces the computation complexity. Simulation results show that this Wiener model is suitable for the prediction of blast furnace Silicon Content.
Francis Delannay - One of the best experts on this subject based on the ideXlab platform.
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on the measurement of the nanohardness of the constitutive phases of trip assisted multiphase steels
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2002Co-Authors: Quentin Furnemont, Pascal Jacques, M Kempf, Mathias Goken, Francis DelannayAbstract:The nanohardness of the phases present in the microstructure of two TRIP (for TRansformation Induced Plasticity)-assisted multiphase steels differing by their Silicon Content was measured by nanoindentation in an atomic force microscope. It is observed that the softest phase in both steels is the ferritic matrix, followed by bainite, austenite and martensite. It is also shown that the Silicon Content of the steel grades is responsible for an increase of the hardness of the ferritic matrix due to solid solution strengthening. Finally, the influence of the preparation mode of the surface prior to the nanoindentation measurements has been investigated. An electropolishing stage after mechanical polishing is acceptable to allow valuable nanohardness measurements. (C) 2002 Elsevier Science B.V. All rights reserved.
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bainite transformation of low carbon mn si trip assisted multiphase steels influence of Silicon Content on cementite precipitation and austenite retention
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 1999Co-Authors: Pascal Jacques, E Girault, T Catlin, N Geerlofs, T A Kop, S Van Der Zwaag, Francis DelannayAbstract:Studies dealing with TRIP-assisted multiphase steels have emphasized the crucial role of the bainite transformation of Silicon-rich intercritical austenite in the achievement of a good combination of strength and ductility. The present work deals with the bainite transformation in two steels differing in their Silicon Content. It is shown that both carbon enrichment of residual austenite and cementite precipitation influences the kinetics of the bainite transformation. A minimum Silicon Content is found to be necessary in order to prevent cementite precipitation from austenite during the formation of bainitic ferrite in such a way as to allow stabilisation of austenite by carbon enrichment. (C) 1999 Elsevier Science S.A. All rights reserved.
Chuanhou Gao - One of the best experts on this subject based on the ideXlab platform.
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data driven time discrete models for dynamic prediction of the hot metal Silicon Content in the blast furnace a review
IEEE Transactions on Industrial Informatics, 2013Co-Authors: Henrik Saxen, Chuanhou Gao, Zhiwei GaoAbstract:A review of black-box models for short-term time-discrete prediction of the Silicon Content of hot metal produced in blast furnaces is presented. The review is primarily focused on work presented in journal papers, but still includes some early conference papers (published before 1990) which have a clear contribution to the field. Linear and nonlinear models are treated separately, and within each group a rough subdivision according to the model type is made. Within each subsection the models are treated (almost) chronologically, presenting the principle behind the modeling approach, the signals used and the main findings in terms of accuracy and usefulness. Finally, in the final section the approaches are discussed and some potential lines of future research are proposed. In an Appendix , a list of commonly used input and output variables in the models is presented.
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a chaos based iterated multistep predictor for blast furnace ironmaking process
Aiche Journal, 2009Co-Authors: Chuanhou Gao, Jiming Chen, Jiusun Zeng, Xueyi Liu, Youxian SunAbstract:The prediction and control of the inner thermal state of a blast furnace, represented as Silicon Content in blast furnace hot metal, pose a great challenge because of complex chemical reactions and transfer phenomena taking place in blast furnace ironmaking process. In this article, a chaos-based iterated multistep predictor is designed for predicting the Silicon Content in blast furnace hot metal collected from a pint-sized blast furnace. The reasonable agreement between the predicted values and the observed values indicates that the established high dimensional chaotic predictor can predict the evolvement of Silicon series well, which conversely render the strong indication of existing deterministic mechanism ruling the dynamics of complex blast furnace ironmaking process, i.e., a high-dimensional chaotic system is suitable for representing the blast furnace system. The results may serve as guidelines for characterizing blast furnace ironmaking process, an extremely complex but fascinating field, with chaos in the future investigation. © 2009 American Institute of Chemical Engineers AIChE J, 2009
Youxian Sun - One of the best experts on this subject based on the ideXlab platform.
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a chaos based iterated multistep predictor for blast furnace ironmaking process
Aiche Journal, 2009Co-Authors: Chuanhou Gao, Jiming Chen, Jiusun Zeng, Xueyi Liu, Youxian SunAbstract:The prediction and control of the inner thermal state of a blast furnace, represented as Silicon Content in blast furnace hot metal, pose a great challenge because of complex chemical reactions and transfer phenomena taking place in blast furnace ironmaking process. In this article, a chaos-based iterated multistep predictor is designed for predicting the Silicon Content in blast furnace hot metal collected from a pint-sized blast furnace. The reasonable agreement between the predicted values and the observed values indicates that the established high dimensional chaotic predictor can predict the evolvement of Silicon series well, which conversely render the strong indication of existing deterministic mechanism ruling the dynamics of complex blast furnace ironmaking process, i.e., a high-dimensional chaotic system is suitable for representing the blast furnace system. The results may serve as guidelines for characterizing blast furnace ironmaking process, an extremely complex but fascinating field, with chaos in the future investigation. © 2009 American Institute of Chemical Engineers AIChE J, 2009
Pascal Jacques - One of the best experts on this subject based on the ideXlab platform.
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on the measurement of the nanohardness of the constitutive phases of trip assisted multiphase steels
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2002Co-Authors: Quentin Furnemont, Pascal Jacques, M Kempf, Mathias Goken, Francis DelannayAbstract:The nanohardness of the phases present in the microstructure of two TRIP (for TRansformation Induced Plasticity)-assisted multiphase steels differing by their Silicon Content was measured by nanoindentation in an atomic force microscope. It is observed that the softest phase in both steels is the ferritic matrix, followed by bainite, austenite and martensite. It is also shown that the Silicon Content of the steel grades is responsible for an increase of the hardness of the ferritic matrix due to solid solution strengthening. Finally, the influence of the preparation mode of the surface prior to the nanoindentation measurements has been investigated. An electropolishing stage after mechanical polishing is acceptable to allow valuable nanohardness measurements. (C) 2002 Elsevier Science B.V. All rights reserved.
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bainite transformation of low carbon mn si trip assisted multiphase steels influence of Silicon Content on cementite precipitation and austenite retention
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 1999Co-Authors: Pascal Jacques, E Girault, T Catlin, N Geerlofs, T A Kop, S Van Der Zwaag, Francis DelannayAbstract:Studies dealing with TRIP-assisted multiphase steels have emphasized the crucial role of the bainite transformation of Silicon-rich intercritical austenite in the achievement of a good combination of strength and ductility. The present work deals with the bainite transformation in two steels differing in their Silicon Content. It is shown that both carbon enrichment of residual austenite and cementite precipitation influences the kinetics of the bainite transformation. A minimum Silicon Content is found to be necessary in order to prevent cementite precipitation from austenite during the formation of bainitic ferrite in such a way as to allow stabilisation of austenite by carbon enrichment. (C) 1999 Elsevier Science S.A. All rights reserved.
