Search Results (169)

Steel is an important product in many engineering sectors; however, steelmaking remains one of the largest ${\mathrm{CO}}_2$ emitters. Therefore, new governmental policies drive the steelmaking industry toward a cleaner and more sustainable operation such as the gas-based direct reduction–electric arc furnace process. To become carbon neutral, utilizing more scrap is one of the feasible solutions to achieve this goal. Addressing knowledge gaps regarding scrap heterogeneity (size, shape, and composition) is essential to evaluate the effects of increased scrap ratios in basic oxygen furnace (BOF) operations. This review systematically examines heat and mass transfer correlations relevant to scrap melting in BOF steelmaking, with a focus on low Prandtl number fluids (thick thermal boundary layer) and dense particulate systems. Notably, a majority of these correlations are designed for fluids with high Prandtl numbers. Even for the ones tailored for low Prandtl, they lack the introduction of the porosity effect which alters the melting behavior in such high temperature systems. The review is divided into two parts. First, it surveys heat transfer correlations for single elements (rods, spheres, and prisms) under natural and forced convection, emphasizing their role in predicting melting rates and estimating maximum shell size. Second, it introduces three numerical modeling approaches, highlighting that the computational fluid dynamics–discrete element method (CFD–DEM) offers flexibility in modeling diverse scrap geometries and contact interactions while being computationally less demanding than particle-resolved direct numerical simulation (PR-DNS). Nevertheless, the review identifies a critical gap: no current CFD–DEM framework simultaneously captures shell formation (particle growth) and non-isotropic scrap melting (particle shrinkage), underscoring the need for improved multiphase models to enhance BOF operation. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. Full article

With the deepening of degradation, the stability and reliability of the degrading system usually becomes poor, which may lead to random jumps occurring in the degradation path. A non-homogeneous jump diffusion process model is introduced to more accurately capture this type of degradation. In this paper, the proposed degradation model is translated into a state–space model, and then the Monte Carlo simulation of the state dynamic model based on particle filtering is employed for predicting the degradation evolution and estimating the remaining useful life (RUL). In addition, a general model identification approach is presented based on maximization likelihood estimation (MLE), and an iterative model identification approach is provided based on the expectation maximization (EM) algorithm. Finally, the practical value and effectiveness of the proposed method are validated using real-world degradation data from temperature sensors on a blast furnace wall. The results demonstrate that our approach provides a more accurate and robust RUL estimation compared to CNN and LSTM methods, offering a significant contribution to enhancing predictive maintenance strategies and operational safety for systems with complex, non-monotonic degradation patterns. Full article

Combustion modeling using computational fluid dynamics (CFD) offers detailed insights into the flame structure and thermo-chemical processes. Furthermore, it has been extensively used in the past to optimize industrial furnaces. Despite the increasing computational power, the prediction of the reaction kinetics in flames is still related to high calculation times, which is a major drawback for large-scale combustion systems. To speed-up the simulation, artificial neural networks (ANNs) were applied in this study to calculate the chemical source terms in the flame instead of using a chemistry solver. Since one ANN may lack accuracy for the entire input feature space (temperature, species concentrations), the space is sub-divided into four regions/ANNs. The ANNs were tested for different fuel mixtures, degrees of turbulence, and air-fuel/oxy-fuel combustion. It was found that the shape of the flame and its position were well predicted in all cases with regard to the temperature and CO. However, at low temperature levels (<800 K), in some cases, the ANNs under-predicted the source terms. Additionally, in oxy-fuel combustion, the temperature was too high. Nevertheless, an overall high accuracy and a speed-up factor for all simulations of 12 was observed, which makes the approach suitable for large-scale furnaces. Full article

Natural magnetite ore is commonly used to produce oxidized pellets as the raw material for blast furnace ironmaking. The drying of green pellets significantly affects the quality of oxidized pellets. However, the drying process in the traveling grate cannot be directly analyzed. To address this issue, in this study the influences of the drying medium temperature, medium velocity, and pellet diameter on the moisture removal, as well as the drying kinetics of the natural magnetite oxidized pellets were investigated. Orthogonal experimental results indicated that the drying medium temperature had the most significant effect on the drying rate, followed by the medium velocity, while the interaction between the pellet diameter and temperature had a minor influence. Drying kinetic model fitting revealed that the drying process followed a modified Page model (III). Model validation demonstrated that the experimental measurements closely aligned with the theoretical predictions, confirming that the Page model (III) accurately predicted the effects of the drying temperature and medium velocity on the pellet moisture content. Higher drying temperatures further improved the prediction accuracy. The findings provide valuable insights for analyzing and optimizing the drying process of the natural magnetite oxidized pellets in the industrial traveling grate systems. Full article

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. Full article

A thorough understanding of fuel behaviour is essential for designing and operating thermochemical systems. Thermogravimetric analysis (TGA) is among the most widely used fuel characterization methods, offering parameters like reactivity and ignition temperature, and enabling comprehensive fuel behaviour assessment through combined indices. This study critically examines the applicability of TGA-based indices for predicting coal performance in industrial processes such as gasification and combustion, where devolatilization, ignition, and burnout stages are key. TGA-derived data are compared with results from established methods, including drop tube furnace (DTF), pulse ignition (PI), and entrained flow reactor (EFR) tests. Findings indicate that the Volatile Matter Release Index (D₂) effectively predicts DTF behaviour (R² = 0.938, max residuals: 4.1 pp), proving useful for fast devolatilization analysis. The Flammability Index (C₁) and Ignition Index (C₃) correlate well with PI results (R² = 0.927 and 0.931, max residuals: 53.3a °C), making them reliable ignition indicators. While TGA tools showed limited accuracy in burnout prediction, the proposed Modified Burnout Characteristic Index (B_1′) achieved reasonable performance (R² = 0.734, max residuals: 0.062%∙°C⁻¹). Overall, selected TGA-based indices offer strong predictive potential for key thermochemical conversion stages. Full article

The blast furnace (BF) and basic oxygen route account for approximately 70% of the global steel production and create 1.8 tons of CO₂ per ton of steel, produced primarily due to the use of coke and pulverized coal (PC) at the BF. With global pressure to reduce CO₂ emissions, optimization of BF operation is crucial, which is possible through optimizing fuel consumption, and improving process stability. Understanding the complex combustion and flow dynamics in the raceway region is essential for enhancing reducing agent utilization. Modeling plays a key role in predicting these behaviors and providing insights into the process; however, validation of these models is crucial for their reliability but difficult in the complex and hostile BF raceway region. In this study, a validated raceway model developed at Swerim was used to evaluate four different cases, namely R1 (Reference), R2 (Low oxygen to blast), R3 (High blast moisture), and R4 (High PC) using an injection coal from SSAB Oxelösund. During actual experiments, the temperature distribution in the raceway was measured using a thermovision camera (TVC) to validate the CFD simulation results. The combined use aims to cross-validate the results simultaneously to establish a reliable framework for future parametric studies of raceway behavior under varying operational conditions using CFD simulations The results indicated that it is possible to measure the temperature within the raceway region using TVC at depths indicated to be 0.5–0.7 m, when not obscured by the coal plume, or <0.5 m, when obscured. TVC measurements are clearly quantitatively affected when obscured, indicated by considerably lower temperatures in the order of 200 °C between similar process conditions. A decrease of O₂ injection results in an extended raceway region as the conditions become less chemically favorable for combustion due to a lower reactant content offsetting the ignition point and reducing the reaction rate in the raceway. An increased moisture content in the blast results in a reduced size of the race-way region as energy is consumed as latent energy and cracks water. An increase in PC rate results in a larger/wider raceway region, as more PC is devolatilized and combusted early on, resulting in larger gas volumes expanding the raceway region outwards, perpendicular to the injection. Full article

Industrial plants now stream thousands of temperature, pressure, flow rate, and composition measurements at minute-level intervals. These multi-sensor records often contain variable transport or residence time delays that hinder accurate disturbance analysis. This study applies lag-specific transfer entropy (LSTE) to historical sensor logs to identify the instrument that first deviates from normal operation and the time required for that deviation to appear at downstream points. A self-prediction optimization step removes each sensor’s own information storage, after which LSTE is computed at candidate lags and tested against time-shifted surrogates for statistical significance. The method is benchmarked on a nonlinear simulation, the Tennessee Eastman plant, a three-phase separator test rig, and a full-scale blast furnace line. Across all cases, LSTE locates the disturbance origin and reports propagation times that match known process physics, while significantly reducing false links compared to classical transfer entropy. Full article

The aim of this work is to investigate the effect of curing temperature and time on the development of compressive strength in geopolymer mortars produced using ground granulated blast-furnace slag (GGBFS) and fly ash (FA). Considering curing circumstances, both the activation energy and the reference temperature could be used properly to build a reliable anticipated model for predicting the compressive strength of geopolymer-based products (mortar and concrete) using maturity-based techniques. In this study, the compressive strength development of geopolymer mortar made from (FA) and (GGBFS) under varying curing conditions. The mortar was prepared using an alkali solution of sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃) in a 1:1 ratio, with NaOH molarity of 12. Specimens were cast following ASTM C109 standards, with a binder/sand ratio of 1:2.75, and compacted for full densification. FA-based mortar was cured at 40 °C, 80 °C, and 120 °C, while GGBFS-based mortar was cured at 5 °C, 15 °C, and 40 °C for durations of 0.5 to 32 days. Compressive strength was evaluated at each curing period, and data were analyzed using ASTM C1074 procedures alongside a computational model to determine the best-fit datum temperature and activation energy. The Nurse-Saul maturity method and Arrhenius equation were applied to estimate the equivalent age and maturity index of each mix. A predictive model was developed for geopolymer concrete prepared at an alkali-to-binder ratio of 0.45 and NaOH molarity of 12. The final equation demonstrated high accuracy, offering a reliable tool for predicting geopolymer strength under diverse curing conditions and providing valuable insights for optimizing geopolymer concrete formulations. Full article

Fire safety design for steel beams is crucial in the construction of steel structures. However, there remains a significant gap in the fire resistance testing of insulated steel beams. This study focuses on full-scale experimental research examining the fire resistance performance of steel beams with varying fire protection methods, cross-sectional dimensions, and heating curves. During the tests, the furnace temperature, specimen temperature, and deflection at mid-span were measured. The test results indicated that specimens mainly failed in lateral–torsional buckling. Additionally, a markedly non-uniform temperature distribution was observed across the cross-section, and the predictions made by GB 51249-2017 were found to be unsafe. The use of fiber cement board for fire protection may be ineffective, as it tends to become brittle at elevated temperatures, making it susceptible to breakage and detachment when the beams begin to bend. Furthermore, due to potential creep deformation, specimens subjected to longer heating durations exhibited lower critical temperatures compared to those with shorter heating durations. Finally, the design method outlined in BS EN 1993-1-2 and ANSI/AISC 360-22 was evaluated against the test results, indicating an accurate prediction of these methods for specimens with shorter heating durations, but an unconservative prediction for specimens with longer heating durations due to ignorance of creep deformation. Full article

Hot metal is produced in a blast furnace. Subsequently, the hot metal is loaded from the blast furnace into a torpedo car and transported to the ladle, where the desulfurization process of the hot metal is realized. After desulfurization, the hot metal is poured from the ladle into the oxygen converter. The temperature of the hot metal has an impact on the steelmaking process realized in the oxygen converter. The complex model presented in the article calculates the temperature drop of the hot metal in the torpedo car and the ladle. Predicting the hot metal temperature behavior allows for determining the length of time the hot metal transport requires and thus initiating steelmaking at its required hot metal temperature. This model, based on heat transfer by conduction, convection, radiation, heat accumulation, and chemical reactions, also allows for the monitoring of the hot metal temperature drop in the torpedo car and the ladle, the analysis of the influence of the linings in terms of heat accumulation, the investigation of the desulfurization process in the ladle, and the optimization torpedo and ladle selection in terms of the accumulated heat in the lining for their entry into the hot metal transport process. An absolute and relative error calculation was used to verify the proposed model. Full article

Ammonia combustion has garnered increasing attention due to its potential as a carbon-free fuel. Globally swirling flow in a rectangular furnace generates flameless conditions by high flue gas recirculation. The reverse air injection (RAI) technique enabled stable swirling flameless combustion of pure ammonia without auxiliary methods. Experiments with pure ammonia combustion in a swirling flameless furnace demonstrated an operable equivalence ratio (ER) range of 0.3–1.05, extending conventional flammability limits of pure ammonia as a fuel. NO emissions were reduced by 40% compared to conventional combustion, with peak concentrations of 1245 ppm at ER = 0.71 and near-zero emissions at ER = 1.05. Notably, flameless combustion exhibited lower temperature sensitivity in NO formation; however, the ER has a serious effect. Developing a simplified reaction model for ammonia combustion is crucial for computational fluid dynamics (CFD) research. A reduced kinetic mechanism comprising 36 reactions and 16 chemical species was introduced, specifically designed for efficient and precise modeling of pure ammonia flameless combustion. Combustion simulation using the eddy dissipation concept (EDC) approach confirmed the mechanism’s predictive capability, maintaining acceptable accuracy across the operating conditions. Full article

Modern aero-engines need alloys that sustain both strength and ductility at high temperatures. However, conventional titanium alloys face inherent trade-offs between strength and ductility. In situ TiB-reinforced titanium matrix composites could fill this gap, but their texture evolution and hot-working mechanics are still poorly understood. In this study, TiB-reinforced IMI834 titanium matrix composites were synthesized using in situ technology in a remelting furnace. Meanwhile, the evolution of microstructure and texture in the hot-rolled titanium matrix composites was examined through both Abaqus simulations and experimental observations. Results indicate that dynamic recrystallization occurred in the microstructure of the composites at a deformation level of 95%. Due to the specific orientation relationship between the TiB whiskers and Ti matrix, the hot-rolled composites developed a pronounced [11-20]_Ti // rolling direction fiber texture. TiB whiskers rotated toward the rolling direction, enhancing the intensity of the [11-20]_Ti // rolling direction fiber texture, consistent with the predictions from numerical simulations. Tensile tests revealed that the combined effects of grain refinement and the rotation of TiB whiskers along the rolling direction increased the yield strength of the hot-rolled composite to 1153 MPa, while simultaneously raising the elongation to 10%. Full article

High-temperature thermal images enable monitoring and controlling processes in metal, semiconductors, and ceramic manufacturing but also monitor activities of volcanoes or contrasting wildfires. Infrared thermal cameras require knowledge of the emissivity coefficient, while multispectral pyrometers provide fast and accurate temperature measurements with limited spatial resolution. Bayer-pattern cameras offer a compromise by capturing multiple spectral bands with high spatial resolution. However, temperature estimation from color remains challenging due to spectral overlaps among the color filters in the Bayer pattern, and a widely accepted calibration method is still missing. In this paper, the quantum efficiency of an imaging system including the camera sensor, lens, and filters is inferred from a sequence of images acquired by looking at a black body source between 700 °C and 1100 °C. The physical model of the camera, based on the Planck law and the optimized quantum efficiency, allows the calculation of the Planckian locus in the color space of the camera. A regression neural network, trained on a synthetic dataset representing the Planckian locus, predicts temperature pixel by pixel in the 700 °C to 3500 °C range from live images. Experiments done with a color camera, a multispectral camera, and a furnace for heat treatment of metals as ground truth show that our calibration procedure leads to temperature prediction with accuracy and precision of a few tens of Celsius degrees in the calibration temperature range. Tests on a temperature-calibrated halogen bulb prove good generalization capability to a wider temperature range while being robust to noise. Full article