Search Results (280)

Energy-intensive foundries require methods to explore trade-offs among delivery performance, production horizon, electricity use, and cost when industrial data are incomplete. This paper presents an offline, process-level digital twin case study for the melting and casting area of a Danish cast-iron foundry. A multi-agent simulation represents production orders, enterprise resource planning and manufacturing execution system functions, induction furnaces, holding furnaces, crane-based transfer of molten metal, vertical moulding lines, the operating calendar, and electricity cost accounting for the induction furnaces. The model is assessed through boundary definition, assumption registration, implementation checks, material flow plausibility, a diagnostic comparison of furnace temperature, controlled scenario experiments, and local sensitivity analysis. These activities support internal consistency and bounded interpretation but do not constitute independent operational validation of the full production system. In the simulated 200-order monthly case, First-Come-First-Served and Earliest Deadline First complete the same 288,620 pieces and 5482.00 t. Earliest Deadline First increases the simulated on-time completion rate from 87.5% to 100%, while makespan, model-estimated electricity use by induction furnaces, and model-estimated electricity cost increase by 7.52%, 0.58%, and 3.42%, respectively. The case indicates that deadline-oriented sequencing may improve delivery performance but lead to a longer production horizon and higher energy use and cost within the defined model boundary. The contribution is an auditable foundry-specific modelling workflow that links heterogeneous data conditions to modelling choices, supporting evidence, and interpretation limits. The model is therefore intended for preliminary offline scenario exploration rather than validated operational decision support. Full article

Gas-assisted discharge of granular materials plays a critical role in shaft furnaces, moving-bed reactors, and other industrial multiphase systems, where interstitial gas flow strongly influences arch stability and may induce progressive flow blockage. Existing analytical models generally neglect aerodynamic gas–particle interactions, whereas CFD–DEM simulations provide high predictive accuracy at the expense of substantial computational cost. To bridge this gap, the present study develops and validates a physically based Torque-Balance Model for predicting gas-assisted granular discharge, arch stability, and flow blockage. A comprehensive experimental investigation was performed using a quasi-two-dimensional transparent apparatus and a thermally stabilized shaft model operated under controlled conditions. Gas-assisted discharge was examined for different gas-flow directions, gas properties, outlet geometries, and particulate materials using hydrogen, helium, and air. High-speed imaging together with gravimetric measurements enabled detailed characterization of discharge regimes and arch evolution. The proposed analytical framework explicitly incorporates interparticle mechanical interactions, aerodynamic drag, outlet geometry, and gas-pressure effects within a unified torque-balance formulation. The model describes successive stages of the discharge process, including stable discharge, transition to blockage, and complete flow suppression, while maintaining computational efficiency suitable for engineering calculations. Experimental results demonstrated that gas-flow direction governs arch stability and discharge behavior. Co-current gas flow promoted repeated arch collapse and enhanced discharge, whereas counter-current flow progressively stabilized the granular arch and ultimately produced complete flow blockage. Validation against the complete experimental database demonstrated excellent agreement between theoretical predictions and experimental observations, yielding an average prediction error below 10%, a maximum deviation within ±20%, and a coefficient of determination of R² = 0.96. The proposed Torque-Balance Model provides a computationally efficient and physically interpretable engineering framework that bridges the gap between simplified empirical correlations and computationally intensive CFD–DEM simulations and can be applied to the prediction and optimization of gas-assisted granular discharge in industrial multiphase systems. Full article

To clarify the effects of biomass char injection position on combustion characteristics and NO formation, a three-dimensional CFD model of a 300 MW tangentially fired pulverized coal boiler was developed using ANSYS Fluent. The Realizable k-ε model, discrete phase model, species transport model, and P-1 radiation model were employed to describe the flow, combustion, heat transfer, and NO formation processes. The model was validated against field measurements under pure pulverized coal combustion conditions. Based on the validated model, five co-firing cases were designed by injecting biomass char through primary air nozzles located at five different elevations in the burner zone. The results show that biomass char injection position significantly affects furnace flow organization, heat release distribution, carbon conversion, and NO formation, while maintaining the overall tangential swirling structure. Case C achieved the highest outlet velocity of 10.42 m/s, which was 9.1% higher than that of Case E, and exhibited the lowest CO concentration, indicating improved carbon conversion performance. However, the intensified oxidation environment in Case C promoted fuel-N conversion and resulted in the highest NO concentration of 313 ppm. Case B achieved the highest outlet temperature of 1429 K, which was 6.8% higher than that of Case E. In comparison, Case D maintained a similar CO₂ mole fraction of 0.1546 to Case C, which was 0.1545, while reducing the NO concentration from 313 ppm in Case C to 145 ppm, corresponding to a reduction of 53.7%. Therefore, Case D achieved the most favorable balance between carbon conversion and NO emission control under the investigated conditions. This study demonstrates that biomass char injection position is an important operational parameter for optimizing biomass utilization and reducing NO emissions in tangentially fired pulverized coal boilers. Full article

Slab reheating furnaces are among the most energy-intensive units in hot-rolling production lines, and their operating states directly affect fuel consumption, temperature uniformity, and production cost. High energy consumption conditions often arise from coupled changes in reheating time, furnace-temperature regulation, combustion status, and production rhythm, making them difficult to identify using fixed energy thresholds or manual experience alone. This study proposes an energy-efficiency-constrained DDPM-ConvTransformer method for data-driven high-energy-consumption anomaly diagnosis in slab reheating furnaces. Continuous industrial records are converted into fixed-length production windows, and a denoising diffusion probabilistic model is used to learn the multivariate temporal distribution of normal operating conditions. A convolutional module and a Transformer encoder are embedded in the denoising network to capture local thermal-process fluctuations and long-range temporal dependencies. During training, a specific-energy auxiliary constraint guides the shared representation toward operating patterns associated with energy efficiency. During inference, window-level anomaly scores are obtained from diffusion denoising errors, and the decision threshold is determined from validation-set score quantiles. Using 7476 industrial production records for model development and evaluation, the test-set results show that the detected abnormal windows have 47.83% higher specific energy consumption and 62.17% higher total energy consumption than normal windows, with Cohen’s d values of 1.27 and 1.60, respectively. Compared with PCA, Isolation Forest, autoencoder-based models, an energy-constrained Transformer autoencoder, and diffusion-based baselines, the proposed method produces stronger post hoc energy-consumption differences, while computational cost analysis further clarifies its practical trade-off for offline diagnosis and periodic screening. Group analysis, typical-window diagnosis, and perturbation validation further support its applicability for energy-efficiency diagnosis and high-consumption operating-condition screening in slab reheating furnaces. Full article

Furnace tube rupture is a critical safety and reliability issue in high-temperature industrial systems, often leading to unplanned shutdowns, severe economic losses, and safety incidents. Conventional monitoring approaches based on threshold alarms and single-variable diagnostics are frequently inadequate for detecting early degradation under complex multivariable operating conditions. This study proposes a multiclass predictive maintenance framework using a decision forest algorithm to predict furnace tube rupture severity from industrial operational data. The dataset comprised 10,957 observations and 56 furnace operating parameters collected from industrial historian systems. Following preprocessing and Pearson correlation-based feature selection, 19 significant parameters were retained for model development. The proposed framework integrates data preprocessing, feature selection, hyperparameter optimization, and validation using unseen operational data from 2021. The results demonstrate that the model effectively captures rupture-risk trends and provides early warning signals more than 14 days before rupture events, with high-risk classifications exceeding 70% predictive probability. Unlike conventional binary classification methods, the proposed multiclass framework enables risk-based maintenance decision-making, including targeted inspections, load reduction, and scheduled shutdown planning. The findings, based on validation against a single industrial furnace system, highlight the effectiveness of ensemble machine learning techniques in improving predictive maintenance, operational safety, and reliability engineering for this class of industrial furnace; broader generalization across furnace configurations and sites remains to be confirmed through multi-installation validation. Full article

Steel production involves energy-intensive thermal processes, where improvements in heat management can contribute to enhanced efficiency and reduced environmental impact. This study analyses the cooling and heating processes of steel billets in small-to-medium-sized steelworks, with the aim of identifying potential energy, time, and cost savings. Currently, billets leaving the casting process cool down freely in an open-air storage area from approximately their casting temperature to ambient conditions, and they are subsequently reheated to 1265 °C before rolling. A numerical model based on the finite difference alternating-direction implicit (ADI) method has been developed in MATLAB R 2025a to simulate these processes. The numerical implementation was verified against the analytical lumped-capacitance solution under the assumption of uniform billet temperature during slow cooling. For loading times of 15–30 min, the billets retained temperatures of 1112–806 °C after 15 days of insulated storage. Compared with reheating from 25 °C, the predicted heat savings were 672–936 MJ per billet, corresponding to fuel savings of 600–836 kWh_LHV, gross fuel-cost savings of EUR 18–25 per billet, and reheating-time reductions of 17.7–33.3 min. An improvement scenario was then analysed, consisting of placing 36 billets from each casting batch into an insulated container to reduce heat losses after casting. The results show that with reasonably well-insulated containers, billet temperatures can be maintained above 800 °C for up to 15 days. This increase in the inlet temperature to the reheating furnace reduces both the required energy and processing time. These results suggest that relatively simple thermal management strategies can improve energy efficiency and reduce operational demand in steel production, supporting more sustainable industrial processes. Full article

The transition towards climate-neutral metallurgical production requires a broader transformation than the simple substitution of fossil-fuel combustion with electrical heating. While process electrification is a fundamental step towards reducing greenhouse gas emissions, achieving truly sustainable high-temperature processing also depends on the ability to maintain tightly controlled reaction environments, minimise thermal losses, and maximise the efficient use of process gases. These factors become increasingly important as the industry moves towards hydrogen-assisted processing routes and greater integration of renewable energy sources. By controlling heat transfer and gas composition, a stable processing environment can be maintained in which temperature, and gases’ partial pressure, can be accurately regulated throughout the treatment cycle. This study introduces the engineering concept of an airtight electrified indirect-fired rotary furnace, developed as a new process for efficient calcination, and also, hydrogen-based reduction processes. To assess the applicability of the proposed reactor concept, a bench-scale experimental campaign was carried out using two representative metallurgical processes: magnesite calcination and hydrogen-assisted reduction of lateritic ores. Throughout the testing campaign, the reactor maintained stable thermal conditions and a well-controlled process atmosphere, while the integrated monitoring system enabled continuous observation of temperature evolution and gas composition. The calcination trials achieved conversion efficiencies above 98%, whereas the hydrogen-reduction experiments successfully promoted the transformation of iron and nickel oxide phases into their metallic state. The results demonstrate that the integration of indirect electrical heating with airtight reactor operation provides a robust platform for hydrogen-assisted thermal processing. The proposed architecture improves atmosphere control and process efficiency while offering a scalable solution for the future implementation of electrified, low-carbon metallurgical technologies. Full article

Exhaust gas temperature is a critical indicator of combustion efficiency and waste heat recovery in regenerative aluminum smelting furnaces, directly governing the energy intensity and carbon footprint of secondary aluminum production—a cornerstone of the circular economy. However, accurate prediction is challenged by pronounced long-range thermal lag and dynamically evolving spatial dependencies among process variables during operational transitions, which hinder real-time process optimization for energy savings. To address these challenges and advance sustainable manufacturing, a novel prediction model termed ChronoClassGAT (Chronological Class-aware Graph Attention Network) is proposed in this paper. The model integrates three key components: (1) a Temporal Convolutional Autoencoder (TCN-AE) with a Gaussian Mixture Model (GMM) for unsupervised identification of distinct operating regimes, providing categorical priors for dynamic graph construction; (2) a multi-graph fusion mechanism that builds operating-condition-specific spatial structures, enabling a Graph Attention Network (GAT) to adaptively model evolving inter-variable dependencies; and (3) a ChronoSwish activation function that modulates LSTM-based temporal encoding with time-aware periodic and switching signals, enhancing responsiveness to transient dynamics. Validated on real-world industrial datasets, ChronoClassGAT achieves superior prediction accuracy (RMSE of 2.2712, MAE of 1.8023, and $R^2$ of 0.9985) over state-of-the-art baselines. By enabling precise and robust exhaust gas temperature forecasting, our framework provides the decision-support intelligence needed for optimizing regenerator switching, minimizing thermal losses, and reducing fuel consumption, thereby contributing significantly to the operational energy efficiency and environmental sustainability of the energy-intensive non-ferrous metal industry. Full article

Clarifying combustion-space behavior is essential for operating large-tonnage natural gas-fired float glass furnaces with complex single-furnace dual-cooler layouts. In this study, a three-dimensional computational fluid dynamics model of the gas-phase combustion space of a 1300 t/d natural gas-fired float glass furnace was developed and validated using crown-temperature measurements, with a maximum relative error of 3.3%. The effects of burner inclination angle (β = 5°, 10°, and 15°) and excess air ratio (α = 1.0–1.20) on temperature distribution, flame morphology, and flue-gas recirculation were investigated. The results show that β = 5° produces a more horizontally extended natural-gas jet, enhances contact with preheated air, and forms a wider high-temperature region, with a maximum temperature of 2512 K. Increasing the excess air ratio improves combustion completeness and enlarges the high-temperature region; however, further increasing α from 1.15 to 1.20 provides only marginal thermal benefits while increasing sensible heat loss through the exhaust gas. Among the investigated operating conditions, β = 5° and α = 1.15 achieve the lowest outlet flue-gas specific enthalpy of 755 KJ/Kg. Full article

The escalating demand for sustainable metallurgical practices necessitates innovative approaches to manganese production. The smelting–aluminothermic reduction of hydrogen pre-reduced manganese ores in a direct current (DC) arc furnace offers a resilient and sustainable trajectory for optimizing manganese recovery efficiencies while minimizing waste generation under low-carbon operating conditions. This study presents a comparison of smelting–aluminothermic reduction of two Mn ores pre-reduced with hydrogen using two distinct approaches, namely, a packed-bed vertical retort and a plasma rotary furnace. A 200 kW DC arc furnace was used for smelting. The scope of this assessment integrates technical, environmental, and operational metrics of smelting–aluminothermic reduction. For partial process energy estimation, the considered metrics are power stability metrics, specific energy requirement, and load factor/power-on time. The metrics considered for material are reductant efficiency, elemental accountability, elemental recovery, elemental deportment, and slag-to-metal ratio. For process sustainability, refractory and electrode consumption were considered. The environmental indicators considered include CO₂-equivalent emissions per ton of product, dust and particulate emissions, NO_x/SO_x emissions. This research provides critical insights into the viability and environmental advantages of hydrogen pre-reduction coupled with smelting–aluminothermic reduction for cleaner manganese production. Full article

The oxygen blast furnace (OBF) represents a key pathway toward deep decarbonization in ironmaking. The main difference between it and the traditional blast furnace is that the atmosphere in the furnace has high reduction potential and little nitrogen content. However, the burden metallurgical performance under CO-H₂-rich, N₂-lean atmospheres remains unclear. This study investigated the isothermal and non-isothermal reduction and softening–melting behavior of sinter and pellets. As CO increased from 59.2 to 79.2 vol%, the reduction degree rose from ~27% to ~90% within 180 min, with a rate index of 0.72–0.75%·min⁻¹. The apparent activation energies of sinter and pellets were 113.0 kJ·mol⁻¹ and 57.9 kJ·mol⁻¹, respectively, indicating that the gas–solid interfacial chemical reaction is the rate-controlling step. Under non-isothermal conditions, the reduction start temperatures of both sinter and pellets decreased, and complete reduction was achieved at 1200 °C. Microstructural analysis revealed a four-stage structural evolution pathway. The softening–melting behavior indicated that oxygen-enriched conditions decrease the softening start temperature, whereas the melting start temperature increases due to the thickening of the metallic iron shell. The maximum pressure difference of the cohesive zone was reduced by 32.9%, and the air permeability was significantly improved. This study reveals the mechanism of charge reaction and structure evolution under OBF conditions and provides a theoretical basis for charge optimization and OBF operation control. Full article

This study investigates the degradation and failure mechanisms of AISI 310S stainless steel conveyor belt wires operating under cyclic conditions up to 900 °C in a continuous carburizing furnace. Microstructural evolution and mechanical responses after service exposure were evaluated using optical microscopy, scanning electron microscopy (SEM) equipped with energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and Vickers microhardness testing. Results indicate that initial exposure led to $\sigma$-phase nucleation and the formation of a protective Cr₂O₃-SiO₂ oxide scale. However, prolonged service led to scale degradation driven by Na-containing residues from pre-cleaning agents, which reacted to form Na₂SiO₃ and NaAlSiO₄ phases. This degradation accelerated the growth of non-protective iron oxides (Fe₂O₃, Fe₃O₄). Simultaneously, the $\sigma$-phase decomposed into massive, continuous M₂₃C₆ and M₇C₃ carbide networks along grain boundaries, inducing severe chromium sensitization. Consequently, the matrix embrittled significantly, with Vickers hardness increasing from 150 HV to 290–340 HV. Fracture analysis confirmed that brittle intergranular cracking initiated at these carbide networks, oxide inclusions, and matrix pores. Ultimately, the synergistic effects of oxide scale degradation, extensive carbide precipitation, and grain boundary depletion caused the premature catastrophic failure of the conveyor mesh under cyclic operational stress. Full article

In recent years, a sharp increase in coal-based power generation has been observed in a number of countries. Coal-fired thermal power plants remain the main source of harmful emissions in the energy sector of many countries, including Kazakhstan. This creates a strong need for the development of effective methods to reduce pollutant emissions at thermal power plants. The aim of the present study is to perform a numerical investigation of the effectiveness of staged combustion technology with secondary air injection (Over-Fire Air, OFA) applied to three boilers—PK-39, BKZ-160, and BKZ-75—which differ in design, capacity, and furnace configuration. CFD modeling was carried out using the FLOREAN package, adapted to the conditions of the Kazakh energy sector, which relies on high-ash coal (more than 40%) for coal-based power generation. Model validation was performed against experimental data obtained from operating thermal power plants. It was found that air injection through OFA injectors intensifies turbulent mixing, reduces peak temperatures in the main combustion zone, and ensures a more uniform distribution of heat release along the furnace height, thereby suppressing thermal NOx formation. It is shown that the spatial structure of NO concentration fields at the furnace outlet strongly depends on the design features of each boiler. The results demonstrate the high efficiency of staged combustion technology in reducing nitrogen oxide emissions and improving the environmental performance of pulverized-coal boiler units. The obtained results can be used in the design of new and the modernization of existing thermal power plants utilizing coal-based power generation. Full article

This study develops a two-dimensional computational fluid dynamics model for the reactive zone of an industrial-scale shaft furnace operated under ENERGIRON ZR (zero-reforming) conditions, where carbon in direct reduced iron (DRI) is explicitly distinguished into combined carbon in Fe₃C and free carbon in graphitic form. The model is validated against available plant data and then applied to investigate the effects of reducing gas temperature, gas composition, and gas feed rate on reduction, carburization, and carbon partitioning. The results show that in situ reforming, iron oxide reduction, and carburization are strongly coupled near the gas inlet. Increasing the reducing gas temperature from 1273 K to 1373 K raises the metallization degree from 0.9426 to 1.000 and the total carbon mass fraction from 0.01722 to 0.04938, while decreasing the combined carbon fraction from 97.6% to 86.3% because of enhanced Fe₃C decomposition. The effect of CH₄ content is temperature-dependent: at 1273 K and 1323 K, increasing CH₄ from 15% to 25% decreases both metallization and total carbon because intensified endothermic reforming lowers the in-furnace thermal level, whereas at 1373 K the total carbon changes from 0.04849 to 0.04938 and then to 0.04179, reflecting a shift in the controlling factor from CH₄ availability to thermal limitation. Increasing gas feed rate from 1400 Nm³/t-pellet to 1600 Nm³/t-pellet improves both reduction and beneficial carburization, with the total carbon mass fraction increasing from 0.02360 to 0.04047, while the combined carbon fraction decreases slightly from 93.9% to 92.5%. The predicted carbon partitioning results also show qualitative agreement with the limited industrial data, particularly the decreasing combined carbon fraction with increasing total carbon content in DRI. Full article

Radiant tube burners (RTBs) are widely used in industrial heat-treatment furnaces, yet the coupled effects of hydrogen co-firing and exhaust gas recirculation (EGR) on their thermal fields remain insufficiently understood. This study presents a three-dimensional CFD analysis of 28 operating conditions, spanning hydrogen fractions from 0 to 100% and EGR rates from 0 to 20% at a fixed excess air ratio of 10%. The model employs the eddy dissipation concept with a reduced two-step methane mechanism, detailed hydrogen kinetics, and a Discrete Ordinates radiation model with a weighted-sum-of-gray-gases approach. All cases exhibit splitting flames: hydrogen enrichment intrinsically raises the laminar flame speed above the flame morphological transition threshold, while in pure methane, radiative preheating increases the flame speed by 29%, eliminating the triangular flame mode. The volumetric temperature uniformity index peaks near 30% H₂, whereas EGR improves uniformity in hydrogen-rich cases but slightly degrades it in methane-rich conditions. Surface temperature uniformity is maximized at 20% EGR due to near-wall thermal blanketing. Thermal efficiency increases with hydrogen fraction, from 59.1% at 0% H₂ without EGR to 68.6% at 100% H₂ with 10% EGR, while higher EGR suppresses peak temperatures. These findings provide guidance for balancing energy efficiency and temperature uniformity in hydrogen-ready RTBs. Full article