Confronting the urgent need for decarbonization in iron ore sintering under global warming, this study develops an innovative strategy employing natural gas injection to obtain partial replacement of coke breeze. Under constant total heat input, natural gas is introduced between 5 and 20 min into the sintering process, achieving thermal substitution rates ranging from 15 to 30%. The results demonstrate that increasing the natural gas substitution ratio improves granulation performance and enhances bed permeability. At the optimal substitution level of 15%, notable enhancements are observed relative to the baseline with no substitution. Specifically, sintering speed increases by 1.63 mm per minute, yield improves by 3.11%, the tumbler index rises by 5.68%, productivity increases by 0.14 t·(m 2 h) −1 , and solid fuel consumption decreases by 12.87 kg t −1 . Mineralogical analysis indicates a 2.08% increase in calcium ferrite content and a 0.55% reduction in silicate phases at the 15% substitution level. Metallurgical properties are also significantly improved, the RDI +3.15 index increases by 2.72%, the reducibility index increases by 5.70%, and better melting and dripping characteristics are achieved. Further optimization of injection parameters is expected to support the contribution of this technology to decarbonizing the steel industry.

Iron ore serves as the fundamental feedstock for blast furnace operations, and its quality is constitutionally affected by the temperature of the mixture during the sintering process. To enhance the early prediction and regulation of the mixture temperature, this study proposes an intelligent 3D prediction model for iron ore temperature based on the Temporal Fusion Transformer (TFT). This model effectively combines advanced multi-horizon forecasting capabilities with temporal dynamic interpretability, while expanding the observation framework into a three-dimensional space through simulation outcomes. Simultaneously, the study focuses on the fluctuation patterns of the major chemical components in sintering materials and their influence on iron ore temperature through the Variational Autoencoder-Temporal Convolutional Networks (VAE-TCN) model. The TFT model, developed using historical sintering data, achieves an R^{2} = 0.8572 and RMSE = 4.7568 for one-step-ahead prediction of the sinter temperature spatial distribution, based on a dataset split of 90% training, 5% validation, and 5% testing. Compared with Transformer and Long Short-Term Memory (LSTM) networks, the TFT model demonstrates superior performance, reducing RMSE by 0.805 and 2.9937, respectively. In practical applications, the TFT model offers valuable guidance for real-time temperature monitoring during iron ore sintering operations.

Soft-measuring method of iron ore sintering process using transient model

The research focuses on determining main factors that affect quality granulation of iron ore sinter mix. Granulation is a crucial stage in sintering technology, since strength, size distribution, and gas permeability of granules directly influence bed resistance, flame front stability, and overall productivity the sintering machine. Based on both experimental studies and theoretical analysis, role of moisture content, drum operating parameters, and pore structure characteristics has been clarified. It has been demonstrated that preliminary granulation of fine fractions (dust, sludges, lime, and other secondary raw materials) significantly improves the uniformity granule structure. Capillary absorption tests showed that concentrates, ores, and waste products differ considerably in water uptake, which is mainly due to variations in pore radius. According to the Washburn equation, smaller pores limit penetration depth and reduce moisture distribution, whereas larger pores accelerate water movement and ensure better wetting of the charge. Calculated data indicate that at pore sizes of 5 μm the penetration depth does not exceed 2 mm within 10 s, while at 20 – 40 μm moisture spreads several millimeters during the same period. These results confirm the necessity of pre-granulating fine wastes with poor wettability in order to stabilize the structure of the sinter mix. Optimization of granulation parameters has been carried out. The most favorable regime corresponds to a moisture content of 7.5 – 9.0%, drum speed of 12 – 14 rpm, and fill degree of 22 – 26 %. Under these conditions, strong and uniform granules with a dominant fraction of 3 – 6 mm are formed. The fraction below 1 mm decreases from more than 40% to approximately 24%, which considerably lowers dust generation during sintering. The obtained results also prove that the use of preliminary granulation makes it possible to introduce up to 25 – 30% of secondary materials without deterioration of granule stability. At the same time, dust emissions are reduced by 5 – 10% and the environmental performance of sintering is improved. The novelty of this work consists in establishing the relationship between moisture absorption mechanisms and the stability of granules, and in proving the possibility of increasing the share of secondary raw materials in the charge while simultaneously reducing the ecological footprint of agglomeration.

Application of high-alumina type calcium ferrite: A new strategy of mineral phase regulation instead of chemical composition regulation in iron ore sintering

Study on the melting characteristics of iron ore sintering mixtures using spent carbide slag and white mud as calcium-based fluxes

Advances in Detection and Control of Chemical Composition in Iron Ore Sintering Process

This study investigated a novel method of recovering energy from iron ore sinter using solid iron oxide heat transfer materials. Traditionally, air is passed through the sinter either in an open conveyor or a sealed vessel to recover energy. The bed materials used were a magnetite concentrate, hematite ore, goethite–hematite ore and sinter fines. A shortwave thermal camera and quartz reactor were used measure infrared radiation from the process. The thermal imaging was combined with image analysis techniques to visualise the transfer of thermal energy through the system. The results showed that energy moved rapidly through the system with peak heating rates of 18 °C/min at a lump sinter temperature of 600 °C. The ratio of heating rate to cooling rate was as high as 8.6:1.0, indicating efficient retention of energy by the bed materials. The bed composition, determined by X-ray fluorescence and X-ray diffraction was used to calculate the heat capacity based on pure material properties. The resultant energy balance determined thermal efficiency to be between 32 and 46% for the sinter fines and hematite–goethite ore, resulting in predicted fuel savings of up to 9.4kg/tonne with similar heat utilisations to the air recovery process. Thermal imaging combined with Brunauer–Emmett–Teller surface area measurements and scanning electron microscopy analysis experimentally replicated mathematical heat transfer model predictions that a smaller total pore volume resulted in less thermally resistive bed. Image analysis illustrated the breaking of the heat front between the less resistive solid and more resistive air in porous beds versus even conduction of heat through a dense bed. The oxide distribution in the bed materials impacted heat transfer, as at a lump temperature of 500 °C was controlled by hydrated oxide content whereas at 600 °C Fe2O3 was the more dominant driver.

A Preliminary Study on Reduction Degradation of Iron Ore Sinter Using Sinter Analogue and X-ray Micro-computed Tomography (MCT)

Substantial CO emissions in sintering flue gas exacerbate environmental impacts and fuel wastage, thereby impeding clean sintering initiatives. This article systematically reviews the generation mechanisms, influencing factors, and reduction technologies of CO emissions. First, CO emission characteristics in sintering flue gas are characterized through industrial surveys. Second, building on previous research results, kinetic calculations and validation experiments identify the two CO emission sources with causes: high‐temperature (>1070 °C) and low‐temperature preheating zones (600–900 °C). Third, the influencing factors of CO emissions are investigated from the perspectives of iron ore, flux, fuel, and pelletizing process. Finally, the research progress and future development direction of sintering CO emission reduction technologies are summarized. Future research prioritizes “high‐O2, low‐wind, low‐reactivity” coordination and intelligent control for deep CO reduction.

Reduction of Iron Ore Sinter by Hydrogen for Sustainable Ironmaking

Sustainable integration of red mud in iron ore sinter manufacturing via composite agglomeration process (CAP): Multiscale consolidation mechanisms and pilot-scale industrial validation

Iron ore sintering is carried out under negative downdraft suction in a packed bed system. Normal operation results in non-uniform temperature distribution in sintered bed resulting in the generation of sinter return fines (−5 mm) which reduces the process yield. In the present work, a novel approach of pulsating suction through the generation of palpitate downdraft suction flow and its effect on sinter bed and synthesized sinter properties is explored. Pilot pot sinter experiments involving varied pulsation magnitudes through variation in valve closure (30%, 60%, 100%) for a specified time are performed. An increment in the pulsation magnitude results in a higher pressure drop (50–200 mm) occurring due to alteration in local flow dynamics. The pulsation mechanism of the downdraft suction leads to the broadening of the flame and increased sinter bed temperature for a prolonged duration. At 100% value closure better sinter properties are realized due to improved heat transfer and gas–solid reaction kinetics. Enhancement in the sinter properties like tumbler index from 69.42% to 75.82%, reduction degradation index from 24% to 19%, reducibility index from 74.25% to 76.22% is attained. In addition, overall sinter return fines are reduced from 22.3% to 22.12%. Mineralogical investigation of the pulse sintered product reveals that the increased formation of silico-ferrite of calcium and aluminum (SFCA and SFCA-I) improved the sinter properties.

Calorific Optimization for Improving Productivity and Energy Efficiency in Iron Ore Sintering

Abnormal operating modes in the iron ore sintering process often lead to reduced productivity and inferior sinter quality. The timely early warning of such modes is therefore essential in maintaining stable production and ensuring product quality. To this end, we develop an early warning approach that integrates cross-sectional image features from the discharge end. First, an edge detection-based scheme is designed to isolate and analyze the red fire layer in the image. Second, a random forest feature importance ranking is employed to select process variables. Third, a Bayesian neural network is trained on both selected process variables and visual features extracted from the red fire layer to construct the early warning model. Finally, the burn-through point is adopted as the classification criterion, and experiments are carried out on raw data collected from an industrial plant. The results demonstrate that the proposed method enables the accurate early detection of abnormal operating modes, achieving accuracy of 94.07%, and thus holds strong potential for industrial application.

Iron ore sintering is a critical process in steelmaking, where the produced sinter is the main raw material for blast furnace ironmaking. The quality and yield of sinter ore directly affect the cost and efficiency of iron and steel production. Accurately predicting the burn-through point (BTP) temperature is of paramount importance for controlling quality and yield. Traditional BTP temperature prediction only utilizes data from bellows, neglecting the information contained in sinter images. This study combines color temperature information extracted from the cross-sectional frame at the discharge end with bellows data. Due to the non-stationarity of the BTP temperature, a hybrid prediction model of the BTP temperature integrating bidirectional long short-term memory and extreme gradient boosting is presented. By combining the advantages of deep learning and tree ensemble learning, a hybrid prediction model of the BTP temperature is established using the color temperature information in the cross-sectional frame at the discharge end and time-series data. Experiments were conducted with the actual running data in an iron and steel enterprise and show that the proposed method has higher accuracy than existing methods, achieving an approximately 4.3% improvement in prediction accuracy. The proposed method can provide an effective reference for decision-making and for the optimization of operating parameters in the sintering process.

Emission characteristics and removal efficiencies of SO2, NOx, particulate matter, and dioxins in iron ore sintering after ultra-low emission transformation

Carbon consumption dynamic modeling is essential for energy saving, emission reduction, and green manufacturing of iron ore sintering process. This article proposes a novel adaptive weighted broad echo state learning system (AWBESLS) for carbon consumption dynamic prediction in the sintering process by integrating adaptive weights and a reservoir with echo state characteristics. Different from previous studies, the AWBESLS adaptively matches a weight to each production data to overcome the effects of anomalous data in production data and utilizes an echo state network (ESN) for catching the dynamic state in sintering process. Carbon consumption experiments using actual production data reveal the effectiveness of the AWBESLS and compare it with some state-of-the-art methods. The results show that the AWBESLS is superior to other methods in improving the prediction performance with lowest prediction error. In summary, the AWBESLS is an effective and applicable technique for dynamic modeling of the sintering process that is easily applicable for the modeling of other manufacturing processes.

<p>Improving the quality of iron ore sintering and adding specific components has positive effects on blast furnace productivity. Optimizing basicity in the blast furnace charge is one way to improve all indices of production processes. Adjusting basicity aims to achieve the formation of new phase compounds that are favourable for the metallurgical and mineralogical sinter properties. The chemical analyses are insufficient for controlling the phase transition of multicomponent systems, as it is necessary to know the structure of all constituents. For that reason, X-ray diffraction is used for identifying minerals in sinter. Also, the physico-mechanical properties of sinter are investigated. Based on experimental results, the optimal basicity of limonite ore from mine "Omarska" Prijedor is determined.</p>

To address the challenge of assessing carbon emissions in the iron ore sintering process, characterized by dynamic complexity, strong temporal correlations, and multi-stage coupling, this study innovatively introduces the Hidden Markov Model (HMM) into the field of carbon emission analysis. We propose a method for process stage identification and carbon emission modeling based on a Gaussian Hidden Markov Model (Gaussian HMM). The model defines the four stages of the sintering process as hidden states and uses six-dimensional flue gas monitoring data (temperature, SO2 concentration, NO concentration, NOx concentration, O2 content, CO concentration) as the observation sequence, with Gaussian distributions describing the emission characteristics of each stage. The research employs the Random Forest algorithm to impute missing values and correct outliers in the raw data, followed by standardization to eliminate scale differences. Model parameters are initialized using Maximum Likelihood Estimation (MLE) and iteratively optimized via the Forward-Backward and Baum-Welch algorithms to enhance the model's fitting capability for complex temporal data. The Viterbi algorithm dynamically decodes the hidden state sequence, enabling an online "predict-until-cooling" monitoring strategy. This strategy accurately determines the optimal cooling timing to balance combustion efficiency with the reaction endpoint. This approach prevents incomplete iron ore combustion and low raw material utilization, while simultaneously reducing emissions of harmful gases and greenhouse gases, thereby achieving the goal of lowering carbon emissions. It provides technical support for the refined management of carbon emissions.