The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform

Abdul Rahman Mohamed - One of the best experts on this subject based on the ideXlab platform.

  • neural networks for the identification and control of blast furnace Hot Metal Quality
    Journal of Process Control, 2000
    Co-Authors: V R Radhakrishnan, Abdul Rahman Mohamed
    Abstract:

    Abstract The operation and control of blast furnaces poses a great challenge because of the difficult measurement and control problems associated with the unit. The measurement of Hot Metal composition with respect to silica and sulfur are critical to the economic operation of blast furnaces. The measurement of the compositions require spectrographic techniques which can be performed only off line. An alternate technique for measuring these variables is a Soft Sensor based on neural networks. In the present work a neural network based model has been developed and trained relating the output variables with a set of thirty three process variables. The output variables include the quantity of the Hot Metal and slag as well as their composition with respect to all the important constituents. These process variables can be measured on-line and hence the soft sensor can be used on-line to predict the output parameters. The soft sensor has been able to predict the variables with an error less than 3%. A supervisory control system based on the neural network estimator and an expert system has been found to substantially improve the Hot Metal Quality with respect to silicon and sulfur.

V R Radhakrishnan - One of the best experts on this subject based on the ideXlab platform.

  • neural networks for the identification and control of blast furnace Hot Metal Quality
    Journal of Process Control, 2000
    Co-Authors: V R Radhakrishnan, Abdul Rahman Mohamed
    Abstract:

    Abstract The operation and control of blast furnaces poses a great challenge because of the difficult measurement and control problems associated with the unit. The measurement of Hot Metal composition with respect to silica and sulfur are critical to the economic operation of blast furnaces. The measurement of the compositions require spectrographic techniques which can be performed only off line. An alternate technique for measuring these variables is a Soft Sensor based on neural networks. In the present work a neural network based model has been developed and trained relating the output variables with a set of thirty three process variables. The output variables include the quantity of the Hot Metal and slag as well as their composition with respect to all the important constituents. These process variables can be measured on-line and hence the soft sensor can be used on-line to predict the output parameters. The soft sensor has been able to predict the variables with an error less than 3%. A supervisory control system based on the neural network estimator and an expert system has been found to substantially improve the Hot Metal Quality with respect to silicon and sulfur.

A. Medvedev - One of the best experts on this subject based on the ideXlab platform.

  • Pressure and flow control of a pulverized coal injection vessel
    IEEE Transactions on Control Systems Technology, 2000
    Co-Authors: W. Birk, A. Medvedev
    Abstract:

    This paper deals with model-based pressure and flow control of a fine coal injection vessel for the use of the blast furnace process. A control system should be in place to maintain a constant coal mass flow from the injection vessel to the blast furnace, since irregularities in the coal mass flow cause significant variations in the Hot Metal Quality. By means of system modeling, the structure and behavior of the coal injection vessel are analyzed. It is shown that by use of a model-based design, the control objectives can be reached and the control performance can be significantly improved compared to the proportional integral (PI) controllers. Alternative control strategies are discussed and compared with the conventional design. The linear quadratic Gaussian (LQG) design method is used to design a multi-input multi-output (MIMO) controller which is validated through experiments on the coal injection plant at SSAB Tunnplat, Lulea, Sweden.

Wang Long-hu - One of the best experts on this subject based on the ideXlab platform.

  • Prediction of blast furnace Hot Metal silicon content based on LSSVM optimized by GA
    Journal of Terahertz Science and Electronic Information Technology, 2020
    Co-Authors: Wang Long-hu
    Abstract:

    Hot Metal silicon content has long been used as one of the most important indices to represent the Hot Metal Quality and the thermal state of a blast furnace.In order to improve the measurement precision and attain stable operation of the blast furnace,a novel model for predicting silicon content by using Least Square Support Vector Machine(LSSVM) is presented.It adopts Genetic Algorithm(GA) to determine the optimum parameter set and therefore improves the model performance.By training and testing the operational data from blast furnace at a steel tube plant,the experimental results indicate that the proposed model can predict silicon content in Hot Metal with a maximum relative error of 5.8 % and correlation coefficient of 0.926 375,whose accuracy can be improved by 2.1% and 4.3% than that of the direct LSSVM and the feed-forward network with the same data set,respectively.

Zhang Jianliang - One of the best experts on this subject based on the ideXlab platform.

  • TMS 2014 Supplemental Proceedings - Development and Practice of Blast Furnace Physical Heat Index Model Based on the Hot Metal Silicon Content and Temperature Prediction Model
    TMS 2014: 143rd Annual Meeting & Exhibition, 2014
    Co-Authors: Dai Bing, Zhang Jianliang
    Abstract:

    Blast furnace hearth thermal state is one of the most important indexes for the evaluation of blast furnace hearth production status. By analyzing the relevant relationships among the Tc (Coke burning temperature in direct reduction zone), Tf (Theoretical combustion temperature), Hot Metal temperature and silicon content in Hot Metal, a mathematical model for the predictions of Hot Metal temperature and Hot Metal silicon content has been established, based on which the blast furnace physical heat index model was also put forward. The online calculation of the model was realized and practiced in a blast furnace production by the technologies of data acquisition, processing and programming, which built a high speed information channel of blast furnace hearth thermal state. Practice proved that the model could effectively help foremen to better understand the condition of blast furnace hearth, so that the blast furnace stability, hearth activity and Hot Metal Quality were promoted greatly.