This document presents a research project that aims to develop a mathematical model to minimize CO2 emissions from blast furnaces. It begins with an introduction that describes blast furnaces as major sources of CO2 emissions in steel production. A literature review finds that existing models are often oversimplified and do not account for all process variables impacting emissions. The research gap is a lack of comprehensive and dynamic models. The objective is to reduce CO2 using mathematical modeling and increase efficiency. Experimental work will use industry data. The timeline and references are also presented.

1. Minimizing CO2 emission in a blast
furnace: A mathematical modeling
approach
By
ANISHAY KUMAR SINHA
01UG19050005
Under the Guidance of
DR RAJESH JHA
ASSOCIATE PROFESSOR

2. CONTENTS
 Introduction
 Literature survey
 Research Gap
 Objective
 Experimental work
 Time Line for Future Work
 Reference

3. INTRODUCTION
• The steel industry is a significant contributor to global
greenhouse gas emissions, with blast furnaces being a
major source of CO2 emissions. The reduction of CO2
emissions from blast furnaces is crucial for mitigating the
impact of climate change. To achieve this goal, there is a
need for advanced modeling and control strategies that
can help to optimize blast furnace operations and
minimize CO2 emissions.

4. • In recent years, mathematical modeling
approaches have gained significant attention as a
tool for understanding and optimizing blast
furnace processes. These models can capture the
complex physical, chemical, and thermodynamic
processes that occur in blast furnaces, providing a
detailed understanding of the process dynamics
and identifying opportunities for improving
performance.

5. • In this context, this research aims to develop a
comprehensive mathematical model for blast furnaces
that considers all the relevant physical, chemical, and
thermodynamic processes. The model will be used to
investigate the impact of different operating conditions
and strategies on CO2 emissions and other performance
metrics. The results of this study will provide insights into
the optimal operating conditions for blast furnaces, with
a focus on minimizing CO2 emissions while maintaining
or improving performance.

6. This figure shows that the blast furnace
has the maximum CO2 emissions.

7. This figure shows that a blast furnace can
produce the maximum amount of hot metal
compared to other processes.

8. LITERATURE SURVEY
SL NO YEAR TITLE AUTHOR FINDINGS
1 2016 Optimization of
Ironmaking Process for
Reducing CO2 Emissions
in the Integrated Steel
Works, ISIJ International,
Vol. 46, No. 12, pp. 1736–
1744
Ariyama, Tatsuro
and Sato,
Michitaka
From this, it has been found that the
maximum emission of CO2 is from the
blast furnace. Hence it is necessary to
reduce the emission and to optimize the
ironmaking process.
2 2004 Hot metal production in
the blast furnace from an
ecological point of view,
Presented at the 2nd
International Meeting on
Ironmaking and 1st
International Symposium
on Iron Ore, Vitoria, Brazil
Schmöle, Peter and
Lüngen, Hans Bodo
From this, it has been found that
currently, we are not in a position to
replace the blast furnace. As of now also
Blast Furnace produces the maximum hot
metal
4

9. SL NO YEAR TITLE AUTHOR FINDINGS
3 2007 A genetic algorithm based
multi objective neural net
applied to noisy blast
furnace data
Pettersson,F.,
Saxen,H.,
Chakraborti,N
Using mathematical modelling we can
reduces the CO2 emissions from the
blast furnace
4 2014 Multi-Objective Genetic
Algorithms and Genetic
Programming Models for
Minimizing Input Carbon
Rates in a Blast Furnace
Compared with a
Conventional Analytic
Approach. steel research
Jha, R., Sen, P.K. and
Chakraborti, N.
Using mathematical modelling we can
reduces the CO2 emissions from the blast
furnace
4
LITERATURE SURVEY

10. SL NO YEAR TITLE AUTHOR FINDINGS
3 2007 Data-Driven Optimization
of Blast Furnace Iron
Making Process Using
Evolutionary Deep
Learning.
Mahanta, B.K., Jha,
R., Chakraborti, N
LITERATURE SURVEY

11. RESEARCH GAP
• A potential research gap for minimizing CO2 emissions in
blast furnaces using a mathematical modeling approach
is the lack of comprehensive and accurate models that
consider all the complex physical, chemical, and
thermodynamic processes that occur in blast furnaces.
While there have been many attempts to develop models
for blast furnaces, these models are often oversimplified
or do not include all the necessary variables and
parameters that affect CO2 emissions.

12. • Another research gap is the lack of real-time monitoring
and control systems that can adjust the operating
parameters of the blast furnace to minimize CO2
emissions. While some monitoring systems exist, they are
often based on static models and do not consider the
dynamic nature of the process.

13. Objective
• To minimize the Co2 emissions from Blast Furnaces using
Mathematical Modelling and increasing their efficiency.

14. Experimental work
• This is industrial work and we are working on data
obtained from industries.

15. Time line for future work

16. References
• Ariyama, Tatsuro and Sato, Michitaka, (2006),
Optimization of Ironmaking Process for Reducing CO2
Emissions in the Integrated Steel Works, ISIJ
International, Vol. 46, No. 12, pp. 1736–1744.
• Schmöle, Peter and Lüngen, Hans Bodo, (2004), Hot
metal production in the blast furnace from an ecological
point of view, Presented at the 2nd International Meeting
on Ironmaking and 1st International Symposium on Iron
Ore, Vitoria, Brazil

17. • Pettersson,F., Saxen,H., Chakraborti,N., (2007) A genetic
algorithm based multiobjective neural net applied to
noisy blast furnace data, Applied Soft Computing, vol-7,
pp. 387-397.
• Jha, R., Sen, P.K. and Chakraborti, N. (2014), Multi-
Objective Genetic Algorithms and Genetic Programming
Models for Minimizing Input Carbon Rates in a Blast
Furnace Compared with a Conventional Analytic
Approach. steel research int., 85: 219-232.

18. • Mahanta, B.K., Jha, R., Chakraborti, N. (2022). Data-
Driven Optimization of Blast Furnace Iron Making Process
Using Evolutionary Deep Learning. In: Datta, S., Davim,
J.P. (eds) Machine Learning in Industry. Management and
Industrial Engineering. Springer, Cham