Machine Learning for Blast Furnace Productivity Improvement at Jindal Steel and Power Angul

2021 ◽  
Author(s):  
A. Fadnis ◽  
A. Gandhi ◽  
R. Jaiswal ◽  
R. Mishra ◽  
S. Mishra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blast Furnace ◽  
Furnace Productivity ◽  
Productivity Improvement

Analysis of Operation Performance of Blast Furnace With Machine Learning Methods

2019 ◽  
pp. 242-269
Author(s):  
Kuo-Wei Hsu ◽  
Yung-Chang Ko
Keyword(s):  
Machine Learning ◽  
Blast Furnace ◽  
Domain Knowledge ◽  
Black Box ◽  
Operation Performance ◽  
Learning Methods ◽  
Fundamental Feature ◽  
Technical Performance ◽  
Machine Learning Methods ◽  
Multiple Reactions

Although its theoretical foundation is well understood by researchers, a blast furnace is like a black box in practice because its behavior is not always as expected. It is a complex reactor where multiple reactions and multiple phases are involved, and the operation heavily relies on the operators' experience. In order to help the operators gain insights into the operation, the authors do not use traditional metallurgy models but instead use machine learning methods to analyze the data associated with the operation performance of a blast furnace. They analyze the variables that are connected to the economic and technical performance indices by combining domain knowledge and results obtained from two fundamental feature selection methods, and they propose a classification algorithm to train classifiers for the prediction of the operation performance. The findings could assist the operators in reviewing as well as improving the guideline for the operation.

A Study on High-Grade Iron ore Beneficiation to Reduce Alumina for Enhanced Blast Furnace Productivity

2020 ◽  
Vol 5 (3) ◽  
pp. 573-579
Author(s):  
Bhawesh Chandra Jha ◽  
Anand Sharma ◽  
Gopal Verma ◽  
Jyotirmaya Sahoo
Keyword(s):  
Blast Furnace ◽  
Iron Ore ◽  
Furnace Productivity ◽  
High Grade

scholarly journals Application of optical basicity to viscosity of high alumina blast furnace slags

2010 ◽  
Vol 46 (1) ◽  
pp. 41-49 ◽  
Author(s):  
D. Ghosh ◽  
V.A. Krishnamurthy ◽  
S.R. Sankaranarayanan
Keyword(s):  
Blast Furnace ◽  
Chemical Composition ◽  
Physical Property ◽  
Optical Basicity ◽  
Alumina Content ◽  
High Alumina ◽  
Furnace Productivity ◽  
Slag Viscosity ◽  
Limited Information ◽  
Weight Percentage

The composition and properties of blast furnace slags greatly affect the furnace productivity and the quality of hot metal produced. Viscosity is an important physical property of slags, strongly influenced by the chemical composition, structure and the temperature. Experimental measurement of slag viscosity requires high temperature equipment and is time consuming. Therefore, chemical parameters are used to identify trends in viscosity as function of chemical composition. Limited information is available for High Alumina Blast Furnace Slags, since much of the open literature deals with Low Alumina Slags, with alumina content less than 15 weight percentage. High Alumina slags (alumina content in the range of 15% to 30%) are predominantly encountered in Indian Blast Furnaces. It appears that these slags have higher viscosity and lower sulphide capacity than the low alumina slags. The effect of chemical composition / ionic structure on viscosity has been interpreted in this work, using the chemical parameter of optical basicity. Data reported in the literature have been used, along with the values of liquidus temperature, for high alumina slags. Three slag systems, i.e., CaO-Al2O3-SiO2, CaO-Al2O3-SiO2-MgO and CaO-Al2O3-SiO2-MgO-TiO2 have been considered in this work. The trends observed are discussed in the paper.

Influence of technological parameters on blast-furnace productivity

2011 ◽  
Vol 41 (12) ◽  
pp. 999-1005
Author(s):  
S. A. Anishchenko ◽  
D. Yu. Fedorenko ◽  
V. P. Kravchenko
Keyword(s):  
Blast Furnace ◽  
Furnace Productivity ◽  
Technological Parameters

Control of the fuel consumption and blast furnace productivity using models

1993 ◽  
Vol 90 (3) ◽  
pp. 355-362
Author(s):  
K.H. Peters ◽  
W. Kowalski ◽  
E. Beppler
Keyword(s):  
Blast Furnace ◽  
Fuel Consumption ◽  
Furnace Productivity

scholarly journals New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization

2021 ◽  
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Blast Furnace ◽  
Conceptual Framework ◽  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Key Factors ◽  
Steel Company ◽  
Steel Manufacturing ◽  
New Business

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

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