Furnace Operation Research Articles

Operating Key Factor Analysis of a Rotary Kiln Using a Predictive Model and Shapley Additive Explanations

The global smelting business of nickel using rotary kilns and electric furnaces is expanding due to the growth of the secondary battery market. Efficient operation of electric furnaces requires consistent calcine temperature in rotary kilns. Direct measurement of calcine temperature in rotary kilns presents challenges due to inaccuracies and operational limitations, and while AI predictions are feasible, reliance on them without understanding influencing factors is risky. To address this challenge, various algorithms including XGBoost, LightGBM, CatBoost, and GRU were employed for calcine temperature prediction, with CatBoost achieving the best performance in terms of MAPE and MLSE. The influential factors on calcine temperature were identified using SHAP from XAI in the context of the CatBoost model. SHAP effectively assesses model impacts, accounting for variable interdependencies, and offers visualization in high-dimensional contexts. Given the correlation and dimensionality of variables predicting calcine temperature, SHAP was preferred over Feature Importance or PDP for the analysis. By incorporating seven out of twenty operational factors like burner fuel and reductant feed rate, combustion conditions inside of the rotary kiln and RPM, the calcine temperature increased from 840 °C in 2023 to 910 °C by October 2024, concurrently reducing the electricity unit consumption of the electric furnace by 7.8%. Enhancements to the CatBoost algorithm will enable the provision of guidance values after optimizing key variables. It is expected that managing the rotary kiln’s calcine temperature according to the predictive model’s guidance values will allow for autonomous operation of the rotary kiln through inputting guidance values to the PLC.

MAKING QUALITY AGGLOMERATES USING LEAN IRON ORES FOR SUSTAINABLE IRON-MAKING

For sustainable iron making and hassle-free operation of large blast furnaces (BF) and direct reduced iron (DRI) units, a more prepared burden in the form of good quality agglomerates is required. However, the situation is becoming more and more challenging owing to the faster depletion of high-grade iron ore, increased fines generation due to mechanized mining, wide variations in chemistry & mineral characteristics, high content of gangue minerals, and poor liberation characteristics. The growing demand for steel vis-à-vis iron ores has necessitated the utilizationof low-grade iron ores for steel production. The challenges lie in the economic extraction of lean ores and their conversion into quality agglomerates for use in iron making. The utilization of low-grade iron ores has assumed greater significance for the optimal utilization of natural resources and the sustainability of the steel industry. Thebeneficiated concentrate of BHQ iron ore from the Sandur Schist belt is used for the pellets. Green green pellets with 1.21 kg/pellet GCS and drop strength of 12 drop number were producedat an optimum binder of 1.2% and moisture of 9.5%. Induration of green pellets at a firing temperature of 1300 °C has resulted in adequate pellet properties of 237 kg/pellet of CCS, 91.87% TI, 7.41% RDI, and 22.56% porosity. An attempt is made to produce pellets suitable for iron making by optimizing various raw materials and operating parameters.This will reduce dependency on high-grade ores and effectively utilize low-grade iron ore to produce good-qualityiron-making pellets.

High-fidelity modelling of unburnt coal flow in an industry-scale blast furnace using a hybrid CFD-DEM method

Solid fuels, such as coal or biochar, can be injected into a blast furnace for low-carbon ironmaking. However, unburnt coal or biochar powders may accumulate in the coke bed, potentially reducing bed permeability and compromising furnace stability. Current CFD-DEM methods struggle to simulate systems with significant size differences between coke particles and coal or biochar powders, where the diameter ratio dck/dcl is 100–200 times. In this work, a novel multi-resolution hybrid CFD-DEM model is developed to simulate gas-unburnt powders-coke particles flow dynamics within and around the raceway with high fidelity. The model’s accuracy is validated by comparing the simulated evolution of the raceway cavity shape with experimental results. Subsequently, the hybrid model is used to simulate unburnt powder flow through the raceway and the adjacent coke bed (dck/dcl = 100), comparing its performance with conventional smoothed CFD-DEM models. The effects of gas inlet velocity and powder wettability are also analysed. Results show that the hybrid CFD-DEM model effectively simulates detailed pore fluid flow in the coke bed, which the smoothed model fails to capture, demonstrating the hybrid model’s superiority. Increasing gas inlet velocity enlarges the raceway cavity, intensifies high-speed pore flows, and accelerates powder transport into the coke bed. Additionally, higher cohesion energy density (kCED) reduces powder penetration, aligns the peak holdup position and penetration angle, and decreases permeability at key probe positions. This work provides an effective and efficient numerical tool to help understand and optimise the injection operation in blast furnaces.

Modelling of blast furnace ironmaking process based on digital twin

AbstractThe blast furnace ironmaking process is a crucial step in the steel industry. Effectively modelling the blast furnaces is significant in ensuring smooth operation and accelerating the digitalisation transformation of blast furnaces. The authors focus on the mechanism modelling of the blast furnace operation process, using a digital twin model development platform to simulate the main reaction processes inside the blast furnaces. Under on‐site production conditions, Si, Mn and Ti contents of molten iron and the corresponding indicators for model accuracy are calculated. For Si, Mn, and Ti content, the Root Mean Square Error values are 0.0678, 0.0108 and 0.0093 for dataset 1, while 0.0933, 0.0120 and 0.0111 for dataset 2, respectively, indicating that the model has a small simulation error and high accuracy. By comparing the simulation results with accurate laboratory results, the model is validated to have satisfactory simulation reliability and compensates for the existing shortcomings in the blast furnace mechanism modelling field.

Integrating Process Simulation and Life Cycle Assessment for Enhanced Process Efficiency and Reduced Environmental Impact in Ferromanganese Production

Process simulation was integrated with life cycle assessment to evaluate process efficiency and environmental impact of the production of manganese ferroalloys for various production modes and different ore mineralogies. Utilizing HSC Sim, the model was designed to evaluate the production process with or without a pretreatment step, where results for simulated cases using a four-zone ferromanganese furnace model were exported to openLCA for a complete life cycle assessment. Two main production scenarios were simulated: a closed ferromanganese furnace running on the duplex method and an open furnace running on the discard slag approach. The closed furnace scenario achieved a 17.5% reduction in energy consumption and a 16.2% decrease in direct CO2 emissions with CO-rich off-gas pretreatment. The open furnace scenario showed an 11.2% reduction in energy consumption with thermal solar energy pretreatment but no change in CO2 emissions.

Real-time detection of coke particles in blast furnace operations using machine learning: Case of a steel plant in India

The efficiency of blast furnace operations in a steel plant relies on the quality of raw materials, including coke. The particle size distribution (PSD) of coke plays a vital role in ensuring the efficiency of the blast furnace. Conventional methods of measuring average particle size are time-consuming and labour-intensive leading to delayed operations. To address the issue, various studies have used different techniques for real-time measurement of coke particle size, including laser diffraction, acoustic sounding, X-ray computed tomography, and computer vision. While these methods have shown successful applications, there have been limitations in their lower prediction accuracy and detection speed. Therefore, our study proposes a practical approach that leverages the integration of computer vision algorithms to enhance accuracy and detection speed. The proposed methodology is compared with the conventional method of measuring particle size in the lab at a blast furnace. Our results reveal that the YOLOv8 algorithm outperforms the conventional method, providing efficient detection of 5.419 frames per second and with a mean absolute error of [Formula: see text]1.77 mm within the lab results. YOLOv8 can perform object segmentation providing much more precise polygon masks of the coke particles instead of simply providing rectangular dimensions using traditional object detection, as explored in previous studies. This approach enables real-time monitoring of PSD and timely alerts to the onsite team, significantly improving the efficiency of blast furnace operations.

Mathematical model of molten iron and slag flow in the hearth

The stable hearth discharge is crucial for the operation of a blast furnace (BF). This study established a mathematical model for hearth discharge based on Bernoulli's principle, which considers the blast pressure, kinetic energy of molten iron and slag, gravitational potential energy, the pressure loss of molten iron and slag flowing through the deadman and taphole. It is found that the sharp increase of molten iron and slag flow is probably due to the fracture of the mud, while the sharp decrease of molten iron and slag flow is probably due to the blockage of the taphole by coke. This paper also studies the effect of operating parameters on the hearth discharge. With the increase of taphole plug time, the production rate and slag viscosity, the tap-end time will increase. With the increase of deadman voidage, the tap-end time will be shortened. The correctness of the mathematical model was verified by the actual flow rate of slag and molten iron.

Evaluation of Some Uncertainties in the Modeling of Blast Furnace Hydrogen Injection

While blast furnaces remain the dominant ironmaking technology, there is growing interest in using hydrogen to partially replace coke and pulverized coal to reduce CO2 emissions in this hard‐to‐decarbonize process. However, significant discrepancies in modeling the tuyere injection of hydrogen are noted in the literature, which can challenge the scale‐up from modeling predictions and pilot trials to actual blast furnace operations. To address this, the impact of uncertainties in the assumptions of the temperature in the thermal reserve zone and the ratio of H2 and CO utilization on the results of blast furnace process modeling are evaluated using a 1D steady‐state zonal model. The study indicates that variations in the assumed value of thermal reserve zone temperature significantly impact the estimation of optimal hydrogen injection rate, coke replacement ratio, and direct CO2 emissions reductions. In contrast, the same parameters are much less affected by the variations in the assumed value of the proportion between H2 and CO utilization ratios, which, however, affect the top gas composition. A need for further research to determine the range of the difference in temperatures of gas and solid in the thermal reserve zone tolerable for smooth blast furnace operation is highlighted.

Development of Low Carbon Blast Furnace Operation Technology by using Experimental Blast Furnace

Development of Low Carbon Blast Furnace Operation Technology by using Experimental Blast Furnace

TO THE ISSUE OF NON-PROJECT FUEL COMBUSTION

Nowadays, the issues of ecology and sustainable development are becoming more and more urgent, and the scientific community is actively looking for ways to more efficient and environmentally friendly use of renewable energy sources. One of such directions is the development and improvement of coal combustion technologies in aerodynamic furnaces. This paper offers a detailed analysis of this topic, looking at both theoretical aspects and practical applications. Aerodynamic furnaces are specialised devices designed to burn solid fuels such as coal with maximum efficiency and minimum environmental impact. The basic principle of operation of such furnaces is to create optimum conditions for complete combustion of the fuel, which is achieved through carefully designed aerodynamics and process control. The operation of the boiler on the coal of Karazhyra deposit is considered. This coal belongs to non-project fuel and improvement of its combustion processes is required to reduce harmful emissions and increase combustion efficiency. For the energy sector of the Republic of Kazakhstan, the conducted research is important, and the results obtained can be used to regulate and improve the operation of similar equipment, which contributes to improving environmental performance and overall efficiency of the country's energy systems.

Thermal deviation mechanisms for coupled heat transfer between the combustion side and the furnace tube side in the tubular heating furnace

Temperature deviation significantly affects the safe operation of tubular heating furnaces. This is attributed to the inevitable temperature difference between the flue gas and the working medium on the tube side. To date, little research has considered the dynamic heat transfer between the furnace body and the tubes in tube furnaces, and even less attention has been paid to the thermal deviation within tube furnaces. To optimize the operational parameters of the heating furnace, a comprehensive heat transfer model is established in this paper to numerically couple the heat transfer between the combustion side and the working medium on the tube side in tube furnaces. A hydrogenation feed heating furnace with a processing capacity of 600,000 tons per year in a chemical enterprise is selected as the subject of study. Using Fluent software, on the basis of coupled simulation, a non-uniformity coefficient of temperature distribution is introduced to characterize the thermal deviation within the furnace. The impact of the thermal load and hydrogen doping in the fuel gas on the thermal deviation within the furnace is analyzed. The numerical results demonstrate that with an increase in thermal load, the flame structure in the furnace gradually becomes concentrated, the average temperature of the flue gas increases, and the outlet temperature of the furnace chamber increases by 19%, with the thermal efficiency decreasing by 5.11%; at the same time, the overall non-uniformity coefficient of the temperature distribution in the furnace fluctuates irregularly, indicating a nonlinear relationship between the thermal load and the thermal deviation within the furnace. With an increase in the hydrogen content in the fuel gas, the flame structure in the furnace becomes more dispersed, the average temperature of the flue gas increases, but the combustion efficiency of the heating furnace exhibits irregular fluctuations; the thermal deviation within the furnace initially increases and then decreases, with a lower non-uniformity coefficient of temperature at 50% hydrogen content, which is 2.48% lower than at 40% hydrogen content. Under both conditions, the thermal deviation primarily leads to localized hot spots in the radiant furnace tubes and uneven temperature distribution in the convection section, with a minimal impact on the thermal efficiency of the heating furnace.

Know-How of the Effective Use of Carbon Electrodes with a through Axial Hole in the Smelting of Silicon Metal

This article describes elements of the know-how of using carbon electrodes produced using the technology of molding around a rod when smelting silicon metal. Application of our know-how will dramatically increase the competitiveness of silicon metal production. Experts’ concerns regarding the use of such electrodes were that such electrodes have a through axial hole. This significantly reduces the mechanical strength of such electrodes, which can presumably lead to problems associated with the breakage of the working side of the electrode, which is immersed in the smelting space of the furnace under the charge layer. Industrial testing of such electrodes was carried out in a 30 MVA furnace of “Tau-Ken Temir” LLP. During testing, we used an approach previously developed by our team for working with a furnace in the process of smelting silicon metal. In particular, we used an interval between top treatments of about 30 min and adhered to the principles of balanced smelting, i.e., provided a balance between the intensity of the uniform supply of the charge into the furnace and the current active electrical power. Industrial testing carried out over four weeks confirmed the stability of the operation of cheaper carbon electrodes with a through axial hole. The recovery of silicon into finished products was also improved to 88–89% and the specific energy consumption was reduced to 11.2–12.1 MWh/t of silicon metal from the initial value 14,752 MWh/t. Thus, we received additional evidence for the effectiveness of our approach in furnace operating compared to an approach based on the ultimate provision of gas and permeability of the furnace top due to excessively intense processing of the top and an uncontrolled, uneven supply of charge to the furnace.

Energy-saving measures for the operation of industrial furnaces at a gas processing plant

Energy-saving measures for the operation of industrial furnaces at a gas processing plant

A study of options to enhance the operation of a H2 direct reduction shaft furnace using a two-dimensional model

With the intent to explore techno-economically feasible options for improving the performance of the hydrogen (H2) -operated direct reduction shaft furnace for iron ores, a two-dimensional computational fluid dynamics model was applied to assess the potential of a new top gas recycling system featuring dual-row injection. A special numerical routine was developed to bridge data communication during the iterative calculation, where the composition of the gas injected in the upper (tuyere) row varies with the conditions. The results showed that a poor gas utilization is inevitable for a (conventional) single-row recycling system since it cannot properly address the strong heat demand of the process. For dual-row top gas recycling with a reduced lower-row feed rate, the in-furnace thermochemical state is inappropriate if the upper-row feed rate is low, but is gradually improved as the upper-row feed rate increases, since more thermal energy is supplied to the part of the furnace where it is needed. With an appropriate configuration of the upper- and lower-row feed rates, the overall furnace performance can be substantially improved particularly with respect to gas utilization degree. The findings of this work are expected to aid the future design of more efficient operation of H2-based direct reduction of iron ores.

Temperature prediction of submerged arc furnace in ironmaking industry based on residual spatial-temporal convolutional neural network

The submerged arc furnace is widely regarded as one of the most promising ore smelting technologies. However, the real-time monitoring of the multiple physical fields including electric, thermal, and mas, through computational fluid dynamics demands significant computational resources. This paper introduces the spatial-temporal convolutional neural network algorithm to address this challenge. Initially, the influences of various working conditions on these physical fields are analyzed. Subsequently, a prediction model is developed based on the coupling of these multiple physical fields model. The spatial-temporal convolutional neural network algorithm is then employed to elucidate the main parameter distributions, enabling the automatic real-time detection of temperature variation trends and providing a theoretical foundation for intelligent furnace operation. The findings indicate that the electric field is the predominant factor causing non-uniform heat distribution, with localized overheating primarily occurring at the electrode ends. The application of the proposed model facilitates dynamic prediction of the temperature distribution, establishing relationships between historical and future time steps as well as local and global temperature variations. The reliability of the temperature prediction model is confirmed, with the model achieving an accuracy of 99.76 %, surpassing the 98.18 % accuracy of the traditional multi-layer perceptron model.

CFD-DEM study on the raceway transport phenomena and thermo-chemical behaviors in a three-dimensional blast furnace: Effects of process parameters

An in-depth exploration of the kinetic behaviors of the raceway can provide practical insights for optimizing blast furnace (BF) operations. In this study, the transport phenomena and thermo-chemical behaviors in the raceway of a three-dimensional BF were simulated from particulate scale by using the discrete element method-computational fluid dynamics (DEM-CFD) approach. The effects of process parameters (blast velocity, oxygen concentration, blast temperature, coke size and distribution, tuyere insertion depth, and tuyere inclination) on coke combustion characteristics (mass fraction distributions of gas species and reaction kinetics rate) and thermo-chemical behaviors (particle volume fraction, raceway size, mass loss, and coke temperature) were investigated. Meanwhile, microstructures of coke bed were analyzed, and correlations were established for raceway size prediction. The results indicate that the mass fraction distributions of O2 and CO are similar, both showing balloon-like shapes. The distributions of both the CO2 mass fraction and the kinetic rates of reactions exhibit annular shapes. The raceway size increases or even overlaps with increasing blast velocity/oxygen concentration/PSD standard deviation of coke and decreasing blast temperature/coke size. Too large or too small tuyere insertion depth or tuyere inclination is not conducive to the formation of the raceway. Besides, the mass loss, temperature, and microstructures also vary with the process parameters. RSM model can well predict the raceway depth, width, and height. This study extends the particle-scale model by considering transport phenomena towards the global perspective of a real BF, and the obtained new findings on the complex reacting behaviors in the raceway will provide better insights into the optimization of the BF process.

Development and application of closed-loop-oriented intelligent control system for blast furnace air volume addition or reduction

Adjusting the upper and lower parts is an important means to ensure stable operation of blast furnaces. It is a common operation method to adjust the smelting state of the blast furnace by adjusting the air volume. Most of the domestic blast furnace air volume increase and decrease operations are confirmed by manual experience, and it is difficult to realize intelligent automatic control. This paper presents an intelligent control system for regulating air volume in blast furnaces, based on operational logic, expert knowledge, fundamental theories, and other relevant information. The system enables automatic analysis of the blast furnace's state and intelligent reasoning of the foreman's operational methods, laying the foundation for achieving closed-loop control. The system unifies the control philosophy and stabilizes the control methods for regulating air volume in blast furnaces. This achieves standardization of operation and proceduralization of cause analysis. The system enhances adaptability by creating an information feedback mechanism, increasing acceptance among blast furnace operators, and continuously resolving various anomalies in practical applications. The system currently maintains a true hit rate of over 95%, enabling effective reduction of operational misjudgments, intelligent control of airflow adjustment, and promotion of stable and smooth blast furnace operation.

The Development of Simulation and Optimisation Tools with an Intuitive User Interface to Improve the Operation of Electric Arc Furnaces

The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.

Modeling and Comparison Study of Industrial AC-Arcs

Electric arcs are a necessary heat source in many industrial processes that take place in Submerged Arc Furnaces (SAFs). Arcs exhibit non-linear electrical characteristics and behave in a complex manner. Therefore, an improved understanding of their behavior enables better control of furnace operation. Modeling of industrial arcs is a multiphysics process that involves simultaneously solving several coupled physical phenomena, such as electromagnetics, fluid dynamics, and heat transfer, including a radiative heat transfer from the plasma arc. Coupling fluid dynamics and electromagnetics is known as Magnetohydrodynamics (MHD). For practical applications, however, there are also simpler approaches to arc modeling, either based on simplified physical principles or empirical behavior. In this paper, a combined Cassie–Mayr model (CMM) and a channel arc model (CAM) are implemented and coupled with a submerged arc furnace electrical circuit model. The complete circuit model parameters such as resistances and inductances are estimated using modeling of a full size furnace, and then, actual measurements from a SAF are used to validate the models by comparing current and voltage waveform. Both models are then used to estimate harmonic distortion in a SAF for different arc current ratios, which should help operators to estimate the arc current in real time thus be able to lower and raise the electrode to keep operating conditions constant.

Research on the failure mechanism of blast furnace tuyere based on experiment and numerical simulation results

The tuyere, an integral component of a blast furnace, plays a vital role in conveying energy and ensuring stable furnace operation. Given the tuyere’s exposure to a complex working environment within the blast furnace, it is prone to breakage and failure. Thus, investigating the causes of tuyere failure is crucial. This study employs computational fluid dynamics alongside experimental characterization to conduct a numerical simulation of the blast furnace tuyere. The research focuses on comparing the velocity and temperature fields of the tuyere under various cooling conditions to analyze the reasons behind its breakage. The findings reveal that the accumulation of alkali metal elements such as Zn, K, and Na on the tuyere’s surface significantly hampers its longevity. Moreover, the cooling water’s flow rate, inlet temperature, and scale thickness emerge as critical factors influencing the tuyere’s cooling efficiency. Notably, each increase of 30 L/min in the inlet flow rate results in a decrease of approximately 3–5 K in the tuyere’s maximum temperature. For every 2 K rise in the inlet water temperature, the tuyere’s maximum temperature increases by 2–3 K. Furthermore, an exponential increase in the tuyere’s maximum temperature is observed with each 0.1 mm increment in scale thickness. When the scale thickness reaches 0.4 mm, the tuyere’s maximum temperature rises to 545.07 K, an increase of 18.49 %. Thus, this study offers novel insights into the causative factors of tuyere failure, contributing significantly to the efficient utilization of energy within the blast furnace and the optimization of the production process.