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09:00-09:45 Session 12: Plenary Talk-4 (Shigeru Ueda)
[Plenary talk] Fundamentals Study of Slag Property Control for CO2 Emission Reduction in Iron and Steelmaking

ABSTRACT. Crude steel production is increasing, while CO2 emissions from the steel industry needs to be reduced, and there is a movement to achieve net-zero CO2 emissions by 2050.The steelmaking industry is being required to shift from coal-based steelmaking to steelmaking using renewable energy. This report introduces the progress of research and development in Japan on blast furnace ironmaking using hydrogen, low-carbon operations, and steelmaking by electric arc furnaces with the direct reduction iron (DRI). Substitution of the blast furnace ironmaking process by large electric furnaces and improving the efficiency of DRI refining will be issues going forward. Refining reactions need to be designed to correspond to changes in the process, and in non-equilibrium processes such as refining, control of reaction rates and transport phenomena is necessary. Although research on the physical properties of slag has been widely conducted, slag has been treated as a liquid. In order to adapt to increasingly complex processes, studies on the reactivity among solid-gas-liquid and the physical properties of those fluids have been attracted attention. In this report, the method and concept of measuring the apparent viscosity of solid-liquid multiphase fluids are introduced.

10:00-11:30 Session 13A: Fundamentals of Ironmaking and Steelmaking
[Invited talk] Simultaneous Analysis of Soluble and Insoluble Oxygen Contents in Steel: Concept, Thermodynamic Basis, and Applications to Al-killed Steel
PRESENTER: Youn-Bae Kang

ABSTRACT. A simple but promising method for simultaneous analysis of two types of O in steel specimens was developed in the present study using Inert Gas Fusion Infrared Absorption Method. By utilizing different carbothermic reaction temperatures for each type of O, the chemically dissolved O (soluble O) was first separated from the steel specimen at a low reaction temperature, while the physically dispersed O (insoluble O in the form of oxide inclusion) was separated at a higher reaction temperature, thereby being a “two-stage” isothermal heating mode. A series of CALPHAD-type thermodynamic calculations were carried out to estimate the temperature for the separation of each oxide inclusion (Al2O3, FetO, SiO2, CaO, and MgO) from the soluble O. Soluble and insoluble O contents ([S. O] and [I. O], respectively) in Al-killed steels of various C contents (0 to ~2 mass pct.) were simultaneously measured using this "two-stage" method, where the majority of the inclusions was alumina. The measured O contents were validated by a few independent methods. Considerable portion of total O content ([T. O] = [S. O] + [I. O]) was the [S. O], which was higher than the equilibrium O content of the known Al deoxidation equilibria. Therefore, the supersaturation in the Al deoxidation was confirmed.

[Invited talk] Microscopic reaction mechanism of the CaO-based slag formation in the steelmaking process

ABSTRACT. The CaO-based steelmaking flux has been widely applied in most BF-BOF and EAF steelmaking processes. Rapid slag formation from fluxing agents is necessary to lower energy consumption and increase the productivity of the steelmaking process. Therefore, the understanding of the slag formation mechanisms has been paid much attention to, and various physicochemical properties of the CaO-FeO-SiO2 slags, as well as the slag formation behavior, have been intensively studied so far. Among various studies, our group has mainly focused on the dissolution mechanism of solid CaO into the molten CaO-FeO-SiO2 slags at hot metal dephosphorization temperature and clarified the formation mechanism of the solid-liquid interfacial structure. In the series of our study, the microscopic interfacial dissolution behavior of solid CaO into the molten CaO-FeO-SiO-P2O5 slag at 1573 K has been observed in the present study. Varying P2O5 content in the molten slag has significantly changed the microscopic interfacial structure composed of the CaO-SiO2-P2O5 solid solution and liquid slag, resulting in a considerable influence on the dissolution rate of solid CaO.

[Invited talk] Hot stage engineering of BOF slag with Al2O3 additions

ABSTRACT. Annually, about 10 million tonnes of Basic Oxygen Furnace (BOF) slag is produced in Europe and worldwide it accounts for 160-240 million tonnes. The recycling and reutilization of this slag is one of the key elements in the transition to a sustainable steel industry. To valorize BOF slag, in particular as value-added products, hot stage slag engineering is required to stabilize the slag by avoiding the presence of free lime and magnesia. For this purpose, SiO2 and Al2O3 are possible additions which can effectively modify the mineralogy of the solidified slag. Within this study, we investigated the hot stage engineering process in particular for the addition of Al2O3 with respect to its dissolution behavior, its effect on the viscosity and the solidification behavior of the modified slag.

Hematite Ore Decomposition at Particle Scale
PRESENTER: Raja Ankit Anand

ABSTRACT. It is well established that pure hematite (Fe₂O₃) decomposes to magnetite (Fe₃O₄) in the air at a temperature of 1390°C. TGA experiments were conducted in the air using NETZSCH 449 F1 simultaneous analyzer to find the decomposition temperature of laboratory-grade Fe₂O₃ powder which was found to be 1356°C. The mass change for the decomposition was 3.3% which is in line with the stoichiometric calculations. Subsequently, experiments were also conducted using hematite ore concentrate obtained from Hospet, Karnataka. Interestingly the decomposition temperature was found to be 1150°C, more than 200°C lower than that for the pure Fe₂O₃. The decomposed samples were also analyzed using XRD and peaks corresponding to Fe₃O₄, and SiO2 were found. The decomposed sample was again oxidized at 750°C in the air to obtain the Fe₂O₃ phase back. Interestingly when this oxidized sample was thermally treated again at a high temperature in presence of air, no decomposition was observed till 1350°C. This is an interesting result that can have large implications for the induration of magnetite and hematite pellets. Further investigations are in progress to understand this interesting phenomenon.

Si-O deoxidation equilibria in liquid steel
PRESENTER: Sanjay Pindar

ABSTRACT. In the present study, high-temperature experiments have been carried out to establish Si-O equilibria in liquid steel under an inert atmosphere. The oxygen content in steel melt has been estimated using thermodynamic analysis and is validated with experimentally determined oxygen content in steel samples using combustion (LECO) analysis. O content estimated using WIPF truncated to second order and Darken's formalism is in good agreement with experimental results. Cross-interaction parameters estimated in this study are as follows: WIPF: ε_O^Si=ε_Si^O=-11.77, ρ_O^Si=-21.60; and Darken's formalism: ε_O^Si=ε_Si^O=-13.31. Spherical SiO2 inclusions formed on Si-deoxidation.

10:00-11:30 Session 13B: Decarbonization
RHF-EAF A Sustainable Route of Steelmaking: An Exergy Analysis
PRESENTER: Gour Gopal Roy

ABSTRACT. A sustainable steelmaking process is characterized by efficient resource utilization with minimal energy losses, which in turn may be correlated to process exergy efficiency. RHF (Rotary hearth Furnace) is based on dual fuel, where it uses coal for reduction and natural gas (a low carbon fuel) for heat generation. Thus, an RHF-EAF process is expected to emit lower CO2 per ton of crude steel for dual fuel input in RHF and scrap input in EAF. Exergy analysis and CO2 emission of dual-fuel RHF-EAF process have been studied and compared with coal-based processes like BF-BOF and COREX-BOF. Two variants of RHF producing iron nugget (ITmk3 process) or DRI (FASTMET process) are considered separately. Exergy indices of RHF-EAF processes have been found to improve significantly by scrap addition and hot transfer of DRI to EAF. Under such conditions, exergy indices of the RHF-EAF processes are found to be superior to the coal-based steelmaking processes like BF-BOF and COREX-BOF processes. Net CO2 emission through RHF-EAF processes is also found to be comparable, or lower than the BF-BOF process and always lower than other coal-based steelmaking processes like COREX-EAF and SL/RN-EAF.

[Invited talk] Methodology for Developing Strategic Roadmap for Reducing Energy Consumption and CO2 Emission for Iron and Steel Plants
PRESENTER: Sunil Kumar

ABSTRACT. To address climate change the steel industry is increasingly focusing on the reduction of energy consumption as well as Green-House Gas (GHG) emissions. A methodology that incorporates a sound technical element to the assessment of improvement opportunities, was developed to create strategic roadmap for reducing CO2 Emission and Energy Consumption. The methodology is bottom-up, and is applied in much more detail to the specific operations of the iron and steel industry. The methodology was adopted at several operating iron and steel plants (integrated plants and mini-mills) to generate Levelized Cost Curves (LCC) which formed the basis of the strategic roadmaps that were developed. This paper describes the key points of the methodology which helps identify and quantify potential energy savings and CO2 abatement within the iron and steel plants for short term (1-5 years), medium term (5-10 years) and long term (>10 years).

Towards Green Steel Production for Indian Context

ABSTRACT. Global warming is the biggest threat for human life and CO2 is the responsible pollutants of recent warming trends. Commitment was made in Paris Agreement to reduce the greenhouse emission by 80 % by 2050 compared to 1990. Steel industry contributes 5-7 % of total CO2 emission in the world and must have to find innovative solutions to reduce significantly. Specifically, CO2 emission from iron making process emits 70-75 % of total steel works. As ~72 % of steel is produced using blast furnace (BF) route, BF research and technology development needs be more concentrated for CO2 mitigation. One of the major constraints to mitigate this CO2 problem for Indian steelmakers is the deteriorating iron ore quality. Most importantly, increasing alumina and silica content of iron ore significantly makes the ironmaking value chain less energy efficient and thereby increases the cost of steel production and emit higher CO2. The paper emphasizes different technologies for Indian context for greener steel making. Aligning to COP-26 commitment, the paper highlighted the different options to decarbonize the steel sectors – such hydrocarbon injection in blast furnace, replacing carbon intensive asset by greener assets, CCU options for Indian steel industry. Based on CO2 footprint and energy intensity, suitable option will be proposed keeping in view Indian challenges of iron ore quality and green electricity availability.

Continuing the Decarbonizing Initiatives using Induction Furnaces for a Sustainable Clean Steel Production
PRESENTER: Prakash Chaubal

ABSTRACT. The secondary steel production industry currently produces CO2 emissions contributing to almost ten percent of the global emissions. While the continuously rising demand for steel will put pressure on the steel industry, Induction Furnaces reduce CO2 emissions per ton of steel produced. This paper will review the secondary steel production process starting with the scrap yard and ending with the rolled finished products illustrating the various processes and how induction furnaces have helped reduce the carbon foot print since the 1990’s into the future. The editorial covers a case study of the largest Induction Steel Melt Shop in the world, with Micro Steel Mill producing over one million tons of steel per annum.

10:00-11:30 Session 13C: Digitalization in steel industry-2
Efficient Method of Suggesting Alternate Grades using Machine Learning for Steelmaking Plant
PRESENTER: Aditya Nema

ABSTRACT. Quick and efficient decision making is vital to the productivity and quality of Steelmaking process. On daily basis, planning and sequencing of different kinds of grades is done in a Steelmaking shop. These grades are designed based on the chemistry of steel and other processing norms that ultimately reflect in the mechanical and aesthetic properties of the final product. But whenever a batch fails to meet the chemistry requirement of the target grade, it must be re-graded in such a way that it does not create huge chemical transitions in running sequence and it has customer order. Manual decisions of re-grading are not efficient as they are time consuming and many times fail to meet the order and transition criteria. A novel method for re-grading has been developed and deployed at the LD#2 Steelmaking shop of Tata Steel Jamshedpur, India that involves machine learning and search optimization tools to suggest suitable alternate grades that are fitted in terms of order availability, defect generation due to grade mixing (transition loss) and cost margin. This automated system has capability of reproducing the results in a very short interval (30sec) and is helping improve the productivity of plant and quality of the steel.

Optimization of the blast furnace ironmaking process using machine learning and genetic algorithms

ABSTRACT. The blast furnace is a multiphase counter-current packed bed reactor that converts iron-bearing materials such as lumps, sinter and pellets into metallic iron (hot metal) using metallurgical coke and pulverized coal. The quality of input materials has a significant impact on blast furnace operation and hot metal quality, and the economics of the steel plant. Operators are therefore mandated to optimize the blast furnace operation in the face of everchanging raw material quality. However, given the large number of furnace parameters, it is not practical for operators to identify the optimal settings required for efficient and safe operation based on their experience alone. In the present work, model-based process optimization of the blast furnace using machine learning models is demonstrated. A multi-objective optimization problem for maximizing the furnace productivity and minimizing the fuel rate with constraints on hot metal silicon and temperature is formulated and solved using genetic algorithms. Pareto-optimal solutions along with optimal settings for key manipulated variables are obtained. Initial results indicate opportunity for improving productivity and fuel rate by 3-5% in the steady state. The optimal settings can be used to operate the furnace efficiently for a given quality of raw materials.

Sticker type breakout detection using machine learning

ABSTRACT. One of the major operational problems encountered in continuous slab casting of steel is the phenomenon called sticker breakout wherein the shell of the moving semi solid slab sticks to the mold and then ruptures causing liquid steel to splash out leading to consequent operational delay and machine damage. Continuous monitoring of the mould wall temperatures by means of embedded sensors like thermocouples and capturing from therein the signature of stickers is an effective way of detecting the incipient stickers and taking immediate corrective actions. While many approaches including those based on heuristics are in use to identify the sticker signatures, a Machine Learning (ML) based model has been developed in the current work which gives much superior performance. Inputs to the model are temperatures from the thermocouples embedded in the mold wall at different heights, operating parameters like speed, mold level and steel chemistry. Model was developed using Xtreme Gradient Boosting (XGB) algorithm and validated with two to three years' dataset. During the validation, all the true stickers were caught and only very few false alarms were generated. Hence the algorithm is capable of completely preventing sticker breakouts while at the same time minimizes the unwanted slowdowns

Application of Ordinal Logistic Regression for Quality Improvement of Continuously Cast Steel Billets
PRESENTER: Saad Al-Motham

ABSTRACT. Steel continuous casting is a delicate process where many process parameters contribute to billet quality. Process control is essential for achieving billet quality requirements. In case a deviation in quality arises, it may be difficult to pinpoint the process parameter that is the root cause of the issue. Moreover, billet quality is assessed qualitatively, which makes it difficult to use traditional statistical analysis techniques to analyze the results and correlate the quality outcome with the responsible corresponding process parameter. Ordinal logistic regression is a statistical analysis technique that enables the correlation of qualitative outcomes with quantitative variables. In this paper, the technique is applied in an investigation where relatively high and mixed degrees of center segregation, dendritic growth, and internal cracking were observed in high carbon steel billets. Quality outcomes were evaluated qualitatively (center segregation, dendritic growth, central porosity, internal cracks etc). Ordinal logistic regression was applied to assess the correlation between these qualitative outcomes and quantitative process parameters (superheat, casting speed, primary and secondary cooling, chemical composition etc). The statistical significance of the correlation was then quantified and assessed, thus enabling identifying the root causes and devising recommendations to remedy the quality issues

Key Elements of Hot Strip Mill Automation to achieve Critical to Quality Parameters in Hot Rolled Steel

ABSTRACT. An advanced automation concept coupled with hot rolling not only increased production but also product quality. The Mill automation plays the critical role to achieve required dimensional tolerances, Shape, Surface and Mechanical properties. JSW Vijayanagar is an Integrated Steel Plant having Level-4 automation for Production (SAP) and Quality (IPQMS). This paper focus on the flow of customer specific requirements to the production units and how the critical to quality parameters are monitored and controlled. For example to get the required microstructure and properties in hot rolled coils, there is a need of temperature control during roughing mill, finishing mill and ROT cooling. The level-2 system in HSM mill is self-learning model for temperature stability, it is further supported by digital process control and Level-3 planning of production. This paper summarize the linkage of CTQ parameters with production process and automation control systems through QFD (Quality Function Deployment).

11:45-13:00 Session 14A: Process modeling
Descriptive modelling of the HIsarna process
PRESENTER: Dharm Jeet Gavel

ABSTRACT. HIsarna is an innovative ironmaking technology, which pursues significant reduction in the CO2 emissions during iron production. In the HIsarna process, coal and iron ore fines are utilised directly. Hence, coal coking and iron ore agglomeration (pellet and sinter) are not required before smelting in the HIsarna process. HIsarna consists of two main parts: the Cyclone Convertor Furnace (CCF) and the Smelting Reduction Vessel (SRV). The CCF is located on the top of the SRV. The iron ore fines and oxygen are simultaneously injected in the CCF. After partial reduction and melting, the liquid ore drops into the SRV. In order to continue the smelting reactions, the coal fines are injected at the slag-metal interface in the SRV. For the process understanding and improving control tools, a seven zone descriptive model is developed. In the present paper, theories of the seven zones and their relationship with the HIsarna process data are discussed.

A thermodynamic based process model for predicting the slag path in Blast furnace operation along with validation from plant data

ABSTRACT. In the present work, we propose a thermodynamic based process model to ascertain the change in burden and slag composition across the various zones in Blast Furnace (BF). The model is divided into various thermodynamic zones or unit reactors capable of handling all the potential chemical reactions pertaining to that zone. The model is based on ChemApp thermodynamic library, dynamically linked to FactSage databases. The model aids in optimizing the process parameters such as minimum coke consumption and the total blast gas composition. The effect of input compositions in the blast furnace as iron ore, pellets or sinters, upon the intermediate and final slag composition can be adjudged with the aid of this model. The final hot metal and slag composition along with the exit gas volume and composition predicted from this model are compared with various industrial BF plant data operating across different conditions.

Some examples of applications of commercial thermodynamic packages for steelmaking research

ABSTRACT. Steelmaking involves a complex combination of processes governed by fluid flow, heat transfer and mass transfer. To the date, there is no reliable model that can combine these transport processes at micro scale for volumes involved under industrial conditions. Macroscopic models have been developed in the past for basic oxygen furnace and ladle refining furnace operations. Typically, these models involve simultaneous solution of flux equations with free energy minimization. In last decade or so, effective equilibrium reaction zone model coupled with commercial thermodynamic package such as FactSage has become very popular to model secondary steelmaking processes. Such a model greatly simplifies the calculation and have been shown to be very reliable when augmented with measured chemical compositions, temperatures etc. In this presentation, application of such models will be demonstrated through various examples from primary and secondary steelmaking.

Role of ladle glaze on inclusion composition in low carbon aluminum killed steel

ABSTRACT. The ladle slag forms a glaze on the refractory lining in contact with liquid metal during casting. For the following heats, the molten glaze may act as a facilitator for liquid steel-refractory (MgO-C) interactions. The role of such a liquid film in-between refractory and liquid steel has been demonstrated under laboratory scale conditions elsewhere. In this work, exhaustive steel sampling has been carried out for a selected low carbon aluminum killed (LCAK) steel throughout ladle life. The chemical composition of steel samples was measured using OES, and total oxygen was measured using LECO. Furthermore, inclusion analysis of steel samples was carried out using SEM-EDS. The thickness and composition of the glaze layer on the ladle refractory were also analyzed using SEM-EDS. Spinel inclusions were consistently observed in these steel samples indicating magnesium transfer due to steel-slag and/or steel-refractory reactions. Some calcium oxide containing inclusions were also observed. A kinetic model is developed using FactSage macro to understand the role and contribution of glaze on inclusion composition. However, the refractory life could not be included in the model based on current experimental observation.

11:45-13:00 Session 14B: Data analytics
A soft sensor for measuring width in the Hot Strip Mill
PRESENTER: Pushkar Verma

ABSTRACT. In the steelmaking value chain, the hot strip mill would receive slabs from a continuous slab caster as input and would reheat them to their rolling temperature in reheating furnace. Subsequently, the heated slabs would be rolled through a series of rolling mill stands. Dimensional parameters such as width, thickness, etc. have paramount importance to the end customer. Hence, a series of gauges are installed all throughout the rolling table for their accurate measurement. They are also essential inputs for the Level-2 process models for precise dimensional control. Failure of even a single gauge for a short duration is not permissible. However, in real-life scenarios, sensor failure is not uncommon. Providing a redundant set of gauges is an expensive solution. This article describes a soft sensor that aims to predict the coil's width profile throughout the coil length by employing machine learning techniques. The model takes inputs such as transfer bar thickness, width, temperature, carbon equivalent, etc., and predicts the coil width profile at the exit of the finishing mill. The root mean square error value of the best fit model is 2 mm across the product mix. This model is successfully implemented in Tata Steel’s Kalinganagar plant.

Sampling Strategy & Prediction of Bubble Sauter Mean Diameter in a Continuous Casting Mold using Machine Learning

ABSTRACT. Argon gas injection during slab casting is a common practice to counter SEN clogging phenomena. Bubble characteristics determine the probability of bubble-driven defects such as slivers and blisters. 1:2 scaled water model studies were performed with the help of an advanced high-speed-high-resolution camera shadowgraph imaging technique. Bubble Sauter mean diameter (SMD) were calculated using Trainable Weka segmentation in the ImageJ platform for casting processing conditions such as gas flow rate, liquid flow rate and mould width. A predictive model was developed by using an artificial neural network (ANN) to optimize the sampling strategy of the SMDs. The model performance was optimized based on the root means square error (RMSE) and coefficient of multiple determination (R2). The model accuracy significantly improved by implementing bootstrapping aggregation with 5-fold cross-validation.

Blast furnace heat loss root cause diagnosis using deep learning
PRESENTER: Pooja Sabnani

ABSTRACT. Blast furnace efficiency is mainly determined by the amount of energy utilized in iron making process. Loss of energy, due to heat loss through the wall is one of the key parameters which leads to disruption in furnace operations and affects productivity. Higher heat loss also increases fuel demand, thus raising the cost of hot metal. It is thus required for heat loss in the furnace to be in the desirable limits. There are various parameters related to raw material quality and process that has impact on heat loss. Individually analyzing all these metrics and determining the root cause after diagnosis takes a great amount of time and efforts, and the rate of inaccuracy is high. Further, blast furnace process being extremely energy intensive involves complex chemical reactions adding to the complex relationships between parameters. This paper describes AI based Blast Furnace anomaly detection tool developed in Tata Steel for automatically identifying the relevant parameters deviating from its normal pattern. The idea is to infuse deep learning with domain knowhow to identify responsible factors. The tool uses autoencoder technique to detect anomalies in parameters by considering their complex relation and reconstructing the parameters. With incorporation of process experts’ knowledge, an alert is sent to the respective owners. The plant operators can then take corrective action and maintain furnace stability.

Optimization of Coke oven heating using Machine Learning Approach

ABSTRACT. Metallurgical coke is a key feed material in Blast Furnace iron making wherein it acts as a fuel, reductant and mechanical support to the burden for stable furnace operation. Coke is made by baking metallurgical coal in a battery of ovens which are made of high temperature refractory. The thermal stability of the oven operation is of utmost importance, given the susceptibility of refractories to thermal shocks. Critical monitoring parameter for the same is the Cross-Battery Temperature (CBT) which is manually measured, and input heat energy is regulated based on the same. In the current work, ML based soft sensor is developed to predict CBT on a continuous basis so that heat input control of the battery can be made more effective and near real-time. Model is developed with historic operational data using Xtreme Gradient Boosting (XGB) Algorithm. The computational approach has enabled reliable soft sensing (prediction) of the CBT for both top charged and stamp charged ovens. The model simulation is done with the current operational data and the predicted values are found to be in good agreement with the actual CBT. Thereafter, the predicted CBT values are used for optimizing the fuel input to the oven.

11:45-13:00 Session 14C: Towards sustainable steel production
Carbon flow and CO2 emission for induction furnace steelmaking

ABSTRACT. Since last few years due to global climate change study of CO2 emission in every sector has become very important. Steel sector contributes to about 7.2% of global CO2 emission. The steel is manufactured through BF-BOF route and Electric furnace route. Further, electric steelmaking is divided in electric arc furnace steelmaking and induction furnace steelmaking. Ample data is available for BF-BOF and EAF route, but for induction furnace steelmaking data is not available. Induction furnace uses steel scrap and coal based DRI for steelmaking. Considering these factors, the purpose of present study is to generate the data of CO2 emission for induction furnace steelmaking. Comparison is of induction furnace data is also done with the data of arc furnace steelmaking and BF-BOF steelmaking.

A novel process of producing liquid iron using microwaves

ABSTRACT. The Steel industry is facing escalating pressure to reduce CO2 emissions. In blast furnace (BF) process, iron ore lumps are crushed and turned into sinter or pellets. Separately, coal is carbonized and converted into coke. The sinter / pellets and coke are then charged into BF, where burning of coke and coal generate gases which reduce sinter / pellets to produce liquid iron. In BF, coal and coke are used which generate a large amount of greenhouse gases (GHG). To minimize these emissions, Industrial Microwave Research Centre of Pradeep Metals Limited, Navi-Mumbai has developed an iron-making process, where a novel route has been explored based on rapid interactions of iron ore and coal with microwaves. The process uses coal to fulfil chemical demand of carbon for direct reduction of iron oxides and microwaves to fulfil thermal demand for the same, thereby leading to a substantial decrease in GHG emissions. The process uses powdery iron ore which is not favoured in BF. The pig iron so produced is almost rust free and has low levels of Si, P and S, which are favourable for steelmaking. The tapping of liquids could be successfully tried, obtaining iron pieces weighing up to 8.15 kg. This paper presents the efforts made for establishing a prototype plant together with the results of some selected trials.

Improvement of Hole Expansion Ratio in Hot Rolled 590MPa Steel by Reengineering the Microstructure

ABSTRACT. There is increase demand for 590MPa strength hot rolled grade steel with good hole expansion ratio (>70%). Since the grade is used in multiple automotive parts, it is also critical to achieve formability, bendability, phosphatability and fatigue. Typically HSLA based 590 grades offer lower HER (approx. 50%), while ferrite bainite type grade offer 90% HER along with some compromise on shape and surface. There is a need of new design to optimize properties, shape, surface and fatigue life. This paper will focus on microstructure engineering to get single phase ferritic steel strengthen with nano precipitation. Hot rolling microstructure simulation has been done using HSMM software and validated with Plant scale trial. The TMCP rolling required for this development ensures that roughing rolling happens at temperature above Tnr temperature of the steel, to achieve complete recrystallization. Nano precipitation of TiC is the key to achieve the high strength in this grade. By this, high temperature processing, strip shape is taken care and lower Si level has helped to get good surface quality. Final product shows single phase ferritic microstructure with ultrafine grains (finer than ASTM 14) along with nano precipitates of micro-alloy elements. The grade has been approved by major automotive users and has successfully replaced lower strength steel components for weight reduction and fuel efficiency.

Converting Steel Slag into Construction Sand
PRESENTER: Dhiren Panda

ABSTRACT. Steel making process generates 180 – 220 kgs of total slag for every tonne of steel produced and most of this is dumped which is a serious environmental concern. These slags cannot be used in cement making due to low hydraulic properties. Through lab scale studies, a new processing methodology has been developed to convert the crushed steel slag into fine aggregate suitable for replacing river sand. In this process the slag is subjected to mild attrition in a vertical shaft impactor to control the size and change its shape from angular to rounded. Process is controlled by the feed rate and rotor speed. The product is further subjected to air classifier for separation of ultra-fine fractions (< 75 microns) where the smaller particles are carried over in the air stream. The developed sand matches to IS-383 Zone –II and is found useful for all construction purposes with both OPC and PSC cement. Concrete cube tests confirmed higher strength for steel slag sand compared to river sand due to higher density. The classifier fines generated as by-product of this process, can be used as additives in concrete admixtures, as silicon fertilizer and soil conditioner. Based on the developed process, an 800 T/day world’s first steel slag sand plant has been commissioned at JSW Vijayanagar works.

Reducing Oxidized Bath in Energy Optimizing Furnace

ABSTRACT. Energy Optimizing Furnace (EOF) is an oxygen steel making process with the input charge mix of hot metal 80 to 85% and 15 to 20 % scrap. The oxygen level is very important role in primary tap steel so we decide to predict the oxygen level from liquid steel bath. First step planned for oxygen ppm measurement done with the celox measurement which is used for measure the oxygen level from the liquid steel bath by inserting the thermocouple tips into the bath. Further study carried out for oxygen measurement and whichever parameters can be affect the oxygen ppm level. Finally arrived the tapping carbon level mostly impacting on the oxygen ppm in liquid steel bath. The model was created based on the trial taken by celox measurement and statistical model which is used to predict the oxygen level from the liquid steel to avoid the over oxidation of steel bath, Quality of the steel and improving the alloys addition recovery for secondary steel making process.

14:00-14:45 Session 15: Plenary Talk-5 (Rong Zhu)
[Plenary talk, virtual mode] CO2 capture and utilization in steel making process
PRESENTER: Haijuan Wang

ABSTRACT. CO2 is considered as the “resource”, captured from and applied in the iron and steel making processes in China for around 20 years. After the capture of CO2, it is applied in the blast furnace(BF) process, pre-treatment of hot metal, converter process for carbon steel and stainless steel making, electric arc furnace (EAF) process, secondary refining including ladle furnace (LF) and RH process for removing inclusions and gases respectively, as well as the continuous casting process, where CO2 is used as shielding gas. Up to now, this idea has been industrialized in many steel plants. The results obtained from the industrial production indicated that, CO2 utilization could receive different positive effects in each process, for example, when it is used in converter, it could decrease the dust generation, intensify the dephosphorization efficiency and improve the retention of chromium.

15:00-16:00 Session 16: Panel Discussion-2: Decarbonization of steel industry
Decarbonization of Steel Industries

ABSTRACT. India has become the second largest steel producer in the world and also looking forward to doubling its steel production by 2030. At the same time, India is also committed to reduce emissions intensity of its GDP by 45 percent by 2030, from 2005 level. This is like a double-edged sword. India needs to have a clear roadmap towards achieving these goals through deliberations. Through this panel discussion, we are hoping that we will be able to contribute towards the roadmap. Of course, these deliberations are applicable to the world steel fraternity as well.