Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces

Abdul Wahid A. Saif; Mohamed Habib; Mostafa Elshafei; Muhammad Sabih

doi:10.1109/ISIEA.2009.5356434

Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces

Abdul Wahid A. Saif, Mohamed Habib, Mostafa Elshafei, Muhammad Sabih

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Direct Reduction Iron (DRI) furnaces are used to produce iron from iron ore oxides using natural gas. The furnace takes the iron ore in the form of spherical pellets and a mixture of hydrogen and carbon monoxide and produces reduced iron. Accurate estimation of the quality of the reduced iron is essential for proper control and efficient operation of the DRI furnaces. In order to understand the various factors influencing the quality of the produced iron a mathematical model from the literature was utilized for the calculation of the solid and gas flow characteristics inside the DRI furnace. The model presents the differential equations governing the variations of the substance and energy exchange inside the shaft furnace. The objective of this work is to determine the influences of the various operating parameters on the performance of the DRI furnace. In addition to the mathematical model, investigation is carried out to develop a Neural Network model for on-line estimation of the quality of the reduced iron product based on the available process measurements.

Original language	English
Title of host publication	2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings
Pages	383-388
Number of pages	6
DOIs	https://doi.org/10.1109/ISIEA.2009.5356434
State	Published - 16 Dec 2009

Publication series

Name	2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings
Volume	1

Keywords

Iron reduction
Neural network
Soft sensors

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Electrical and Electronic Engineering

Access to Document

10.1109/ISIEA.2009.5356434

Cite this

Saif, A. W. A., Habib, M., Elshafei, M., & Sabih, M. (2009). Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces. In 2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings (pp. 383-388). Article 5356434 (2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings; Vol. 1). https://doi.org/10.1109/ISIEA.2009.5356434

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title = "Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces",

abstract = "Direct Reduction Iron (DRI) furnaces are used to produce iron from iron ore oxides using natural gas. The furnace takes the iron ore in the form of spherical pellets and a mixture of hydrogen and carbon monoxide and produces reduced iron. Accurate estimation of the quality of the reduced iron is essential for proper control and efficient operation of the DRI furnaces. In order to understand the various factors influencing the quality of the produced iron a mathematical model from the literature was utilized for the calculation of the solid and gas flow characteristics inside the DRI furnace. The model presents the differential equations governing the variations of the substance and energy exchange inside the shaft furnace. The objective of this work is to determine the influences of the various operating parameters on the performance of the DRI furnace. In addition to the mathematical model, investigation is carried out to develop a Neural Network model for on-line estimation of the quality of the reduced iron product based on the available process measurements.",

keywords = "Iron reduction, Neural network, Soft sensors",

author = "Saif, {Abdul Wahid A.} and Mohamed Habib and Mostafa Elshafei and Muhammad Sabih",

year = "2009",

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Saif, AWA , Habib, M, Elshafei, M & Sabih, M 2009, Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces. in 2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings., 5356434, 2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings, vol. 1, pp. 383-388. https://doi.org/10.1109/ISIEA.2009.5356434

Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces. / Saif, Abdul Wahid A.; Habib, Mohamed; Elshafei, Mostafa et al.
2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings. 2009. p. 383-388 5356434 (2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2009/12/16

Y1 - 2009/12/16

N2 - Direct Reduction Iron (DRI) furnaces are used to produce iron from iron ore oxides using natural gas. The furnace takes the iron ore in the form of spherical pellets and a mixture of hydrogen and carbon monoxide and produces reduced iron. Accurate estimation of the quality of the reduced iron is essential for proper control and efficient operation of the DRI furnaces. In order to understand the various factors influencing the quality of the produced iron a mathematical model from the literature was utilized for the calculation of the solid and gas flow characteristics inside the DRI furnace. The model presents the differential equations governing the variations of the substance and energy exchange inside the shaft furnace. The objective of this work is to determine the influences of the various operating parameters on the performance of the DRI furnace. In addition to the mathematical model, investigation is carried out to develop a Neural Network model for on-line estimation of the quality of the reduced iron product based on the available process measurements.

AB - Direct Reduction Iron (DRI) furnaces are used to produce iron from iron ore oxides using natural gas. The furnace takes the iron ore in the form of spherical pellets and a mixture of hydrogen and carbon monoxide and produces reduced iron. Accurate estimation of the quality of the reduced iron is essential for proper control and efficient operation of the DRI furnaces. In order to understand the various factors influencing the quality of the produced iron a mathematical model from the literature was utilized for the calculation of the solid and gas flow characteristics inside the DRI furnace. The model presents the differential equations governing the variations of the substance and energy exchange inside the shaft furnace. The objective of this work is to determine the influences of the various operating parameters on the performance of the DRI furnace. In addition to the mathematical model, investigation is carried out to develop a Neural Network model for on-line estimation of the quality of the reduced iron product based on the available process measurements.

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Saif AWA , Habib M, Elshafei M, Sabih M. Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces. In 2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings. 2009. p. 383-388. 5356434. (2009 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2009 - Proceedings). doi: 10.1109/ISIEA.2009.5356434

Intelligent sensor for predicting the quality of reduced iron in direct reduction furnaces

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Keywords

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