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Model based optimization tool (EAF_OPT) for enhancing Energy Efficiency, Productivity and Yield of Electric Arc Furnaces

Primary Information

Domain

Advanced Materials

Project No.

8014

Sanction and Project Initiation

Sanction No: 3-18/2015-T.S-I(VO)

Sanction Date: 19/12/2016

Project Initiation date: 08/02/2017

Project Duration: 36

Partner Ministry/Agency/Industry

Ministry of Steel

 

Role of partner:Partial funding and review of the progress. Project progress reports are shared with the partner ministry periodically. A separate briefing session is being planned in the month of December to provide detailed update on progress.

 

Support from partner:Nearly Fifty percent of the budget comes from the partner ministry. It is a very important project for the medium scale steel mills and the partner ministry is eagerly looking forward for the outcome of this project

Principal Investigator

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Amarendra K Singh
IIT Kanpur

Host Institute

Co-PIs

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Dipak Mazumdar
IIT Kanpur

 

Scope and Objectives

1. Develop a dynamic thermo-chemical model for optimization EAF operation
2. Perform laboratory experiments on melting and refining
3. Collect experimental data on 5-ton furnace for model testing
4. Collect plant data from different units to calibrate and test the model
5. Develop a user friendly software tool for Indian Steel Mills for determining optimum charge-mix for improved efficiency, productivity and yield

Deliverables

The outcome of this project will be EAF_OPT software tool. This will be the first major indigenous tool for Indian Steelmakers accounting for the complex physics of EAF operation. The model will be fully tested with a range of raw materials used by Indian steel mills. It will have an interactive user interface that can be utilized by shop floor engineers and operators. The following deliverables will be available at the end of this project: 1) EAF_OPT Software Tool (A tool for optimizing EAF productivity, energy efficiency and yield under dynamically changing input conditions) 2) User Manual(Step-by -step installation procedure, customization procedure and Utilization procedure) 3) Report containing benchmark tests and case studies 4) Training manual and training program (Short term training program will be organized for users from industries) Software will be made available to academic institutions for teaching purposes. System patent will be obtained where possible.

 

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Videos

https://drive.google.com/open?id=1IRzPv5-25AXnxygRc8YE84FxSw5deqiF

Scientific Output

Dynamic EAF model: Heating and melting in an electric arc furnace (EAF) was studied with the help of a dynamic mathematical model. An in-depth literature review in the area of dynamic and static mathematical modelling of heating and melting of steel in EAF was carried out. This review provided the framework on which the current work was carried out. The model accounts for melting of steel scrap and other raw materials in an electric arc furnace (EAF) and is governed by complex physico-chemical processes. The model had three parts: static heat and mass balance, dynamic heat and mass balance and the transient model for estimation of radiation exchanges amongst various zones. The inter-connection between different parts of the model is illustrated in Figure 1. In the model all three types of heat transfers, i.e., conduction, convection and radiation, are considered in each of the assumed zones of the EAF. Radiation heat transfer plays a significant role in the melting operation and the same is accounted properly to improve the accuracy of the model. The model was numerically validated with data from literature. As a part of this work, an industrial plant campaign as undertaken and the model was used to analyze the industrial data. The composition of steel scrap and liquid steel, furnace capacity and shape, electric power consumption, additions like coke and flux, oxygen lancing rate, tapping time, number of charges and bucket capacity etc. were collected from the plant. The model was used to simulate the 20-tons furnace and predicted melting time with reasonable accuracy. The change of mass of scrap, liquid steel, sloid slag and liquid slag with respect to time in the 20 ton furnace is shown in Figure 2. Thermodynamic Modeling: Thermodynamic compositions of the metal and slag under different operating conditions play a major role towards the final grade of steel produced. Also, thermodynamic data are required for the study of slag foaming practice by injecting oxygen or carbon. It is desirable to improve efficiency and productivity while protecting the furnace wall from damage due to radiation. For all of this, there is a need for reliable thermodynamic data. Such database should operate with an extended region of operation of industrial EAF. In this work, thermodynamic calculations are being performed using FACTSAGE 7.0. Few representative calculations on isothermal saturation data in multi-component slag system are shown in Figure 3. This has relevance on design of slag chemistry and refractory lining. Detailed thermodynamic calculations are planned to design slag chemistry path with the progress of melting and refining. A thermodynamic model on deoxidation of liquid steel has been made, which can be used to calculate the amount of deoxidizers required to attain a desired amount of oxygen level in the final refined steel. Here we have considered Fe-Al-Si-O system where we have calculated the amount of Silicon and Aluminium deoxidizers. The model was validated with the literature as well as with FactSage 7.2 Thermodynamic software (using FSstel, FToxid, FactPS databases) and it has a good agreement except for low mole fraction of silica (SiO2) in final slag. Alloy addition (CFD simulation results): Alloys and other additives in secondary steelmaking are required to get the desired steel composition, quality and it also helps in getting cleaner steel. These additives are added to improve tensile strength, hardenability, wear resistance, corrosion resistance, and magnetic properties of steel. In the new era of competitive market, steel producers are struggling for efficient and good quality of steel. Thus, time required for melting and/or dissolution of alloy additives is important as it will add to the minimum time necessary for the bath for refining and next steps of processing. Alloying elements are classified on the basis of their melting points being lower (class I) or higher (class II) compared to the liquidus temperature of steel. Class I includes silicon, manganese, aluminium, while the other class includes chromium niobium, vanadium, tungsten, and molybdenum. The focus of this current investigation is to get time of shell formation and shell thickness around the spherical particles, which are added for various purposes. The model has been validated using Taniguchi et. al. (TANIGUCHI, Shigeji, Munekazu OHMI, and Shinji Ishiura. "A hot model study on the effect of gas injection upon the melting rate of solid sphere in a liquid bath." Transactions of the Iron and Steel Institute of Japan 23.7 (1983): 571-577) as shown in Figure 4. CFD simulations of EAF were performed to study the mixing characteristics induced by the local temperature gradients in the slag and molten steel regions. Few representative results are shown in Figures 7 and 8. CFD simulations are being carried out to understand the radiation exchanges amongst complex configurations, post combustion, and design for optimum burner locations.

 

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Results and outcome till date

Dynamic EAF model: Heating and melting in an electric arc furnace (EAF) was studied with the help of a dynamic mathematical model. An in-depth literature review in the area of dynamic and static mathematical modelling of heating and melting of steel in EAF was carried out. This review provided the framework on which the current work was carried out. The model accounts for melting of steel scrap and other raw materials in an electric arc furnace (EAF) and is governed by complex physico-chemical processes. The model had three parts: static heat and mass balance, dynamic heat and mass balance and the transient model for estimation of radiation exchanges amongst various zones. The inter-connection between different parts of the model is illustrated in Figure. In the model all three types of heat transfers, i.e., conduction, convection and radiation, are considered in each of the assumed zones of the EAF. Radiation heat transfer plays a significant role in the melting operation and the same is accounted properly to improve the accuracy of the model. The model was numerically validated with data from literature. As a part of this work, an industrial plant campaign is undertaken and the model was used to analyze the industrial data. The composition of steel scrap and liquid steel, furnace capacity and shape, electric power consumption, additions like coke and flux, oxygen lancing rate, tapping time, number of charges and bucket capacity etc. were collected from the plant. The model was used to simulate the 20-tons furnace and predicted melting time with reasonable accuracy. The change of mass of scrap, liquid steel, sloid slag and liquid slag with respect to time in the 20 ton furnace is shown in Figure. Thermodynamic Modeling: Thermodynamic compositions of the metal and slag under different operating conditions play a major role towards the final grade of steel produced. Also, thermodynamic data are required for the study of slag foaming practice by injecting oxygen or carbon. It is desirable to improve efficiency and productivity while protecting the furnace wall from damage due to radiation. For all of this, there is a need for reliable thermodynamic data. Such database should operate with an extended region of operation of industrial EAF. In this work, thermodynamic calculations are being performed using FACTSAGE 7.0. Few representative calculations on isothermal saturation data in multi-component slag system are shown in Figure . This has relevance on design of slag chemistry and refractory lining. Detailed thermodynamic calculations are planned to design slag chemistry path with the progress of melting and refining. A thermodynamic model on deoxidation of liquid steel has been made, which can be used to calculate the amount of deoxidizers required to attain a desired amount of oxygen level in the final refined steel. Here we have considered Fe-Al-Si-O system where we have calculated the amount of Silicon and Aluminium deoxidizers. The model was validated with the literature as well as with FactSage 7.2 Thermodynamic software (using FSstel, FToxid, FactPS databases) and it has a good agreement except for low mole fraction of silica (SiO2) in final slag. Alloy addition (CFD simulation results): Alloys and other additives in secondary steelmaking are required to get the desired steel composition, quality and it also helps in getting cleaner steel. These additives are added to improve tensile strength, hardenability, wear resistance, corrosion resistance, and magnetic properties of steel. In the new era of competitive market, steel producers are struggling for efficient and good quality of steel. Thus, time required for melting and/or dissolution of alloy additives is important as it will add to the minimum time necessary for the bath for refining and next steps of processing. Alloying elements are classified on the basis of their melting points being lower (class I) or higher (class II) compared to the liquidus temperature of steel. Class I includes silicon, manganese, aluminium, while the other class includes chromium niobium, vanadium, tungsten, and molybdenum. The focus of this current investigation is to get time of shell formation and shell thickness around the spherical particles, which are added for various purposes. The model has been validated using Taniguchi et. al. (TANIGUCHI, Shigeji, Munekazu OHMI, and Shinji Ishiura. "A hot model study on the effect of gas injection upon the melting rate of solid sphere in a liquid bath." Transactions of the Iron and Steel Institute of Japan 23.7 (1983): 571-577) as shown in Figure . CFD simulations of EAF were performed to study the mixing characteristics induced by the local temperature gradients in the slag and molten steel regions. Few representative results are shown in Figures below. CFD simulations are being carried out to understand the radiation exchanges amongst complex configurations, post combustion, and design for optimum burner locations. CFD analysis of Post Combustion in EAF:- Post combustion in EAF has led to enhancement of energy efficiency. In the current project. CFD based model of post combustion is implemented on ANSYS Fluent, and is in the process of getting integrated with other modules of EAF_OPT. The post combustion model is based on the conservation of mass, momentum, energy and species and accounts for the high temperature reactions. The input to the model are Decarburisation rate in the furnace, Oxygen stream flow rate, position of oxygen nozzle, Reaction mechanism, and Reaction kinetics. And the model provides the output as Furnace Temperature, Flue gas final composition, Velocity profile inside the furnace, Energy utilisation from enthalpy data. Additionally it provides the rate of oxidation of the electrodes.

 

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Societal benefit and impact anticipated

Electric arc furnace (EAF) route accounts for nearly 25% of steel produced in India. Good quality scraps are costly in India. Non-availability of cheaper scrap forces high use of other raw materials for Indian manufacturer. However, addition of HBI/DRI in the raw material leads to high electricity consumption. Since power tariff too is costly in India, addition of more HBI leads to cost escalation of the product. In order for Indian manufacturer to remain competitive, efficient operation of EAF is a must. Unfortunately, use of multiple raw materials of different quality and in various proportions makes the EAF operation very inefficient on account of lack of understanding of complex phenomena. As a result, Indian EAF units are less efficient. A comprehensive model based tool, similar to the one being developed in this project, will allow Indian plants to use optimum charge-mix and power profile and will make the operation more efficient and productive. Lets put this idea into some numbers. India produced nearly 23 million tonnes of steel through EAF route in 2014-2015. On an average, 500 kWh/ton of energy was consumed. If one assumes 10% savings through optimization of EAF operation using this tool, a total 1150 million kWh of energy savings, and the equivalent reduction in carbon footprint, will result. The comprehensive dynamic simulator being developed will be available in open domain and will benefit mainly the small and medium scale industrial players.

Next steps

1) Extension of dynamic melting module to include varieties of raw materials, dynamic slag foaming, etc.
2) Thermodynamic calculations for slag path design.
3) CFD calculations for design of post combustion module.
4) Plant campaign for model testing. 5) Robust GUI development.

Publications and reports

Anshuman Sinha and Amarendra Kumar Singh, CFD Analysis of Post Combustion in EAF, Poster accepted in NMD-ATM Conference, Nov. 2019, Thiruvananthapuram, Kerala, India
Niharika Dalbehera, Abhishek Arya, Sumanta Maji, and Amarendra K. Singh, Mathematical Modelling of Thermo-Chemical Reactions in Electric Arc Furnace (EAF), Poster accepted in NMD-ATM Conference, Nov. 2019, Thiruvananthapuram, Kerala, India
Amarendra Kumar Singh, Effect of natural convection on formation and melting of shell around low melting point additives in steel Oral Presentation in 178th ISIJ (Iron & Steel Institute of Japan) Meeting, Sept. 11-13, 2019, Okayama University, Okayama City, Japan
Sumanta Maji, Theoretical and Experimental Studies of Stainless Steelmaking in EAF, Poster Presentation in 178th ISIJ (Iron & Steel Institute of Japan) Meeting, Sept. 11-13, 2019, Okayama University, Okayama City, Japan

Patents

Under preparation

Scholars and Project Staff

Manpower appointed:

Dr. Dinesh Nath, Sr. Project Scientist
Mr. V. Ravi, Sr. Project Engineer
Dr. Sminu Bhaskaran, Sr. Project Scientist (past employee)
Mr. Abhishek Arya, Project Engineer
Mr. Ketan Jain, Project Associate (past employee)

Students engaged:
Mr. Sumanta Maji, PhD student (Fourth year)
Mr. Zainul Abedin, PhD student (Second year)
Mr. Zewudu W.A., MTech student (Passed out)
Mr. Rajesh Dey, MTech student (Passed out)
Mr. Shivam Singh, MTech student (Second year)
Mr. Durgesh Shukla, MTech student (Second year)
Mr. Anushman Sinha, BT-MT student (Fifth year)

Challenges faced

Off the shelf equipment were not suitable for the experiments planned and we had to customize our laboratory scale EAF and vacuum refining furnaces. In the case of Vacuum Induction Furnace, none of the bids were first met the technical specifications. Entire process was repeated again. In the case of EAF, we could get only one bid when the tender was advertised and hence we had to increase the tender duration. All these resulted in delay in setting up of experimental facilities.

Other information

Apart from the regular PhD and MTech students, many under graduate students from both inside and outside the institute are being trained as summer interns in this project

Financial Information

  • Total sanction: Rs. 39192000

  • Amount received: Rs. 30502500

  • Amount utilised for Equipment: Rs. 8753596

  • Amount utilised for Manpower: Rs. 3125206

  • Amount utilised for Consumables: Rs. 4017178

  • Amount utilised for Contingency: Rs. 414736

  • Amount utilised for Travel: Rs. 249928

  • Amount utilised for Other Expenses: 25000

  • Amount utilised for Overheads: Rs. 5088000

Equipment and facilities

 

1) Vacuum Induction Melting Furnace with the capacity of processing 10 kg scrap material (under manufacturing)
2) 50 kVA - 10 kg scrap melting Electric Arc Furnace (under manufacturing)
3) Ansys software for CFD simulations
4) Factsage steel database for thermodynamic calculations
5) Computational lab infrastructure