Furnace Process Research Articles

Effect of the Specific Features of the Torch Aerodynamics on the Integrated Efficiency of the Multifuel Boiler

This scientific paper gives the literature review emphasizing that the overwhelming majority of the operated boilers are equipped with vortex burners. These burners are characterized by an increased NOx formation, low operation reliability of screen tubes and convective heating surfaces, small maneuverability range and insufficient economical indicators. The defining role of the aerodynamic organization of the furnace processes affecting the operation efficiency of the boilers was shown for the case of the combustion of the energy fuels of a different type. Hence, the problems of an increased reliability, maneuverability, environmental safety and economical operation of the boilers are rather topical. The above problems faced by Ukraine today have been essentially exacerbated due to the fact that most boilers of the thermal power plants have a depleted resource and the production of new boilers (especially high power boilers) was actually stopped. The use of vortex burners with the traditional counter layout on the two opposite furnace walls is peculiar for the overwhelming majority of modern high power boilers in Ukraine and abroad. In this case, the central zone of the furnace is designed to provide the collision of the burner torches of opposite walls and a high-temperature burning core is created. In the case of gas –oil boilers, the availability of the high-temperature core results in an intensified formation of the thermal nitrogen oxides and a decreased operation reliability of the metal of furnace screens. The torch throw onto the side furnace walls during the combustion of fuel oil results in an essential decrease of the operation reliability of the screen tubes due to a high-temperature hydrogen sulfide corrosion. This paper highlights the issues of the effect of the specific features of the torch aerodynamics on the integrated efficiency of the boiler operation and the optimization of the layouts for the combustion of energy fuels in the multifurnace boilers for further reconstruction of the boilers to reduce the emission of harmful substances, increase reliability and economicity of their operation and enlarge the steam load carrying range.

Evolution of biocarbon strength and structure during gasification in CO2 containing gas atmosphere

This work focuses on the properties of hydrolysis lignin biocarbons with a perspective on utilizing the biocarbons in pyrometallurgical processes. Even if the blast furnace and basic oxygen furnace (BF-BOF) process route was replaced by emerging technologies with lower CO2 emissions in the future, the need for carbonaceous materials in the iron and steel making industry will still exist. Most of these applications do not require as high standards for the properties of carbonaceous materials as BF but the requirements are still similar to those for BF. The most important properties of carbonaceous materials are the mechanical strength and suitable reactivity. In the case of biocarbon, the apparent density is also considered important. The reactivity and strength properties are investigated with isothermal reactivity tests and compression strength tests for the non-gasified and pre-gasified biocarbon and reference coke samples. The mass loss rate of coke gasification (-0.069%/min) was considerably lower than that of least reactive biocarbon L1200 (-0.18%/min). Regarding the compression strength of the samples, the strength of coke dropped by 56.44% for the samples of pre-gasification level of 50% compared to non-gasified samples while the drop was only 40.68% for the L1200 biocarbon samples. The level of gasification was found to have direct correlation with pore area percentage with R2 value 0.92 in case of L1200 and 0.98 in case of coke. Further, the pore area percentage correlated with the compression strength with R2 of 0.93 in case of L1200 and 0.98 in case of coke.

Root cause diagnosis for complex industrial process faults via spatiotemporal coalescent based time series prediction and optimized Granger causality

It is essential to conduct root cause diagnosis (RCD) to guarantee the safety of operations of chemical processes and suppress fault deterioration, yet RCD has encountered challenges in the context of the increasing complexity of industrial processes. Granger causality (GC) analysis is one of the most commonly used methods to construct process causal maps and identify root causes. However, its use is subject to a number of limitations for dynamic nonlinear industrial processes. Therefore, an optimized GC analysis is proposed to perform RCD in complex industrial processes based on the spatiotemporal coalescent-based prediction model (SCPM) and causality verification algorithm (CVA). The time series prediction model SCPM is presented as an alternative to GC’s conventional autoregressive model in order to avoid spurious regressive facing nonlinear processes, which stacks dilated convolutional neural networks (DCNN) and bidirectional gated recurrent unit (BiGRU) to coalesce time series nonlinear couplings and long-term dependence information. Moreover, considering data that may deviate from normal distribution after the fault occurs, a nonparametric causality test method CVA based on the Wilcoxon signed-rank test (WSRT) is constructed combined with SCPM to eliminate false causality discovery. Empirical results on the Tennessee Eastman process and the blast furnace process demonstrate the effectiveness of the proposed RCD method.

The development of the blast furnace metallurgy of the iron in Slovakia

Easily available sources of iron ore, the abundance of hydropower, and wood for charcoal were the prerequisites for the development of iron metallurgy in Slovakia. Since the 16th century, the bloomery process used in iron metallurgy has been gradually replaced by the blast furnace process. This new process was more economical, but it required extremely high investments. The steam engine replaced the unreliable water power in the blowing process, and coke replaced charcoal. Especially due to economic problems, the first blast furnaces in Slovakia started to be built about two hundred years later than in Western Europe. Despite the promising start, the installation of steam engines was lagging. A major drawback was the lack of road and rail networks combined with the absence of local coking coal. The introductory part of the paper compares chronologically the individual stages of the development of blast furnace production in Slovakia and abroad. Next, the paper presents comprehensive results of the analysis of samples of the slag found at 59 sites of extinct ironworks with a blast furnace, localised until now. The samples were analysed by metallographic, chemical, spectral (secondary and trace elements), and X-ray methods. In the past, these data were published with territorial restrictions and mostly in a language other than Slovak. The contents of secondary and trace elements in slag from individual sites using cluster analysis and also metallic iron content in slags categorised according to owners of the blast furnaces and the slag basicity are compared.

Smart Design of Cz-Ge Crystal Growth Furnace and Process

The aim of this study was to evaluate the potential of the machine learning technique of decision trees to understand the relationships among furnace design, process parameters, crystal quality, and yield in the case of the Czochralski growth of germanium. The ultimate goal was to provide the range of optimal values of 13 input parameters and the ranking of their importance in relation to their impact on three output parameters relevant to process economy and crystal quality. Training data were provided by CFD modelling. The variety of data was ensured by the Design of Experiments method. The results showed that the process parameters, particularly the pulling rate, had a substantially greater impact on the crystal quality and yield than the design parameters of the furnace hot zone. Of the latter, only the crucible size, the axial position of the side heater, and the material properties of the radiation shield were relevant.

Numerical Study of Furnace Processes during Combustion of Off-Design Coals in a 220 t/h Boiler

Numerical Study of Furnace Processes during Combustion of Off-Design Coals in a 220 t/h Boiler

Thermal reactivity and flowability of pulverized coal blending with iron-bearing dust injection in blast furnace process

Pulverized coal (PC) blending with iron-bearing dust (IBD) injection technology in blast furnace (BF) iron-making process was proposed to valorize the IBD. Three aspects containing the co-flow property of PC and IBD, thermal reactivity and corresponding kinetics during the PC combustion process, as well as thermal variation and unburned PC distribution in BF were the main focuses of the present work. It has found that the Carr-flowability indexes of PC and IBD appear little difference. Their blends can be smoothly transported when adding portion of the IBD is less than 6%. In addition, comprehensive combustibility index (S), ignition index (Ci), and burnout index (Db) of the PC are improved when the mass fraction of the IBD enhances from 0% to 3%, and 6%, which proves that blending with the IBD can accelerate the PC combustion process. Moreover, the combustion activation energy (E) decreases after adding the IBD, which supports the improved thermal reactivity. Also, the accumulation of unburned PC in BF presents a decline tendency, which may promote the permeability of BF and help enhancing the pulverized coal injection dosage.

THE ESTIMATION OF THE EFFECT OF COKE, OBTAINED WITH PARTICIPATION OF VOLUME-MODIFYING ADDITIVES, ON THE TECHNICAL AND ECONOMIC INDICATORS OF BLUST FURNACE SMELTING

The article deals with the effect of the coke resistance index CSR (Coke Strength after Reaction) on the technical and economic indicators of the blast furnace process. It is shown that in the production of metallurgical coke using volume-modifying additives in the amount of 0.25% by weight of the coal load, its strength improves, in particular, index CSR. As a volume-modifying additives, α-Al2O3 electrocorundum and α-SiC carborundum with a particle size of <45 μm and vibromilenum α-SiC carborundum with a particle size <12 μm were used.
 Due to the introduction of fine powders of electrocorundum and carborundum into the coal charge, the maximum increase in the coke post-reaction strength index (CSR) by 7.6% was obtained.
 The influence of changes in individual indicators of coke quality on its consumption and productivity of the blast furnace was assessed in accordance with the guidance document of the metallurgical division of Metinvest Holding LLC. This document contains a detailed methodology for use in analyzing fluctuations in specific coke consumption and throughput of blast furnaces influenced by changes in blast furnace process parameters (by factor analysis). This technique is based on quantitative relationships between changing parameters (factors) and specific coke consumption (blast furnace productivity).
 There has been made an expert technical and economic assessment of the feasibility of introducing 0.25% fine powdered α-Al2O3 and α-SiC powders into the mixture from the point of view of blast-furnace production with pulverized fuel injection technology. The results show that, despite the increase in the cost of coke with the introduction of volume-modifying additives into the mixture, a reduction in the cost of iron can be achieved due to the expected reduction in the specific consumption of coke due to an increase in its post-reaction strength.

Leaching Behavior of the Main Metals from Copper Anode Slime during the Pretreatment Stage of the Kaldor Furnace Smelting Process

The Kaldor furnace smelting process is currently the mainstream process for treating copper anode slime, but the existence of copper, tellurium and other impurities has adverse effects on the recovery of gold and silver during the Kaldor furnace smelting stage. Therefore, it is necessary to pretreat the copper anode slime to remove these impurities before Kaldor furnace reduction smelting. However, the current pretreatment process of copper anode slime generally has the problem of low removal efficiency of copper and tellurium, and little research on the occurrence state of main metals in copper anode slime. Therefore, this study quantitatively determined the phase composition of Cu, Te, Pb, Bi, As, Sb, Se, Ag and Au, and hydrogen peroxide was introduced to enhance the leaching of impurities. The leaching behavior of each metal in copper anode slime was investigated in detail. The results demonstrate that Cu and Te in the copper anode slime mainly exist in the form of CuO and CuSO4 and Te and AuTe2, respectively. More than 99% of the Cu and 97% of the Te were leached out using 250 g/L H2SO4 and 28.8 g/L H2O2 with a leaching pressure of 0.8 MPa at 150 °C for 2 h, while the leaching of Au and Ag was both < 0.03%. The removal of Cu and Te and the enrichment of precious metals were achieved. This study provides a rich theoretical reference for the optimization of the Kaldor furnace process.

Hybrid static-sensory data modeling for prediction tasks in basic oxygen furnace process

In this paper, we propose a novel data-driven prediction system for Multivariate Time Series (MTS) in an industrial context, where classic relational data contain keyinformation in order to properly interpret the MTS. Particularly we focus on the accurate endpoint prediction of temperature and chemical composition at the basic oxygen furnace, which is a step in the steel production pipeline where liquid iron is refined to steel. The precise prediction of temperature is important for proper process control while reaching the target chemical composition is essential for quality control. Our deep learning methodology employs two modules followed by an aggregation block; a Convolutional Neural Network (CNN) handles the MTS, while in parallel, the static data is processed by a Fully Connected Network (FCN). We enhance the CNN performance by adding two Squeeze-and-excitation (SE) blocks, which act like an attention module over the different channels. By taking the MTS data into account we improve the prediction by up to 10% relative over the models which only consider the static data. The hybrid FCN-CNN-SE architecture slightly improves the state-of-the-art MTS approaches by 2%, with less outliers on the prediction of final temperature and phosphorus concentration, while being easier to implement and more scalable to larger datasets and input space than current solutions.

The Shewhart-type RZ control chart for monitoring the ratio of autocorrelated variables

In many industrial manufacturing processes, the quality of products can depend on the relative amount between two quality characteristics X and Y. Often, this calls for the on-line monitoring of the ratio Z = X/Y as a quality characteristic itself by means of a control chart. A large number of control charts monitoring the ratio have been investigated in the literature under the assumption of independent normal observations of the two quality characteristics. In practice, due to the high frequency in sensor data collection, both autocorrelation and cross-correlation between consecutive observations can exist for X and Y and should be modelled to protect against the false alarm rate inflation when implementing a control chart for monitoring the ratio Z = X/Y. In this paper, we tackle this problem by investigating the performance of the Phase II Shewhart-type RZ control chart monitoring the ratio of two normal variables whose relationship is captured by a bivariate time series autoregressive model VAR(1), which can also account for the cross-correlation between the two quality characteristics. With the numerical study, we discuss how the design and the statistical performance of the Shewhart-type RZ control chart change with the VAR(1) model's parameters. We also provide an example to illustrate the use of the Shewhart-type RZ control chart with bivariate time series of observations in a furnace process.

Simulation of the Effects of Hydrogen Injection on the Main Indicators of Blast Furnace and Carbon Dioxide Emission Reduction by Zero-Dimensional Model

The steel industry accounts for about 7% of the total human carbon dioxide emissions, and faces enormous pressure to reduce emissions, especially the blast furnace (BF) process with high carbon dioxide emissions. BF hydrogen injection is one of the important technical measures for emission reduction. However, there are still few studies on the limit of hydrogen injection in BF and the influences of hydrogen injection on the changes of main indicators of BF. To evaluate the limits of BF hydrogen injection and these effects, this paper introduces in detail the application of Excel MMULT (MINVERSE (zone 1), (zone 2)) function to build BF mass and heat balance model and application of the Generalized Reduced Gradient (GRG) nonlinear optimizer of Excel Solver Add-In for BF operation optimization. The paper simulates the effects of hydrogen injection on the replacement of BF coke and pulverized coal, the reduction of CO₂ emission, the increase of production, and the changes of raceway adiabatic flame temperature (RAFT) and top gas temperature (TGT), the use of blast temperature (BT), and oxygen enrichment for thermal compensation. The possible limits of hydrogen injection are simulated. The influences on BF direct reduction degree (Dr), CO and H₂ utilization rate (ηCO and ηH₂) are also simulated. Further, the influences of hydrogen preheating injection on coke ratio, CO₂ emission reduction and use of BT, oxygen-enrichment for thermal compensation are simulated. The paper comprehensively and quantitatively evaluated the various aspects of BF hydrogen injection. The simulation results show that when hydrogen is injected at room temperature (298 K), under condition of full coke, 1250°C BT, oxygen enrichment and maintaining 1800°C RAFT, the maximum CO₂ emission reduction of the BF can reach ~30%. If the allowable RAFT is 1900°C, the maximum hydrogen injection volume is about 1/3 less than 1800°C, and the maximum CO₂ emission reduction will be ~20%. 1 kg/thm of hydrogen injected can replace about 2 kg/thm of carbon; this value varies with BT and oxygen enrichment rate, but is not affected by the composition of the mixed ore.

Разработка современного метода расчета тепловой работы камерной нагревательной печи

Experiments are often considered as the way to find rational heating modes at manufacturing enterprises. Unlike experimental methods, modern calculation methods use mathematical modeling of physical and chemical processes in heating furnaces. It allows to reduce the time of the research and it is less resource intensive. A mathematical model is proposed to find rational operation modes of thermal furnaces according to the specified criterion. The model considers the porosity of the heated material and the flow of the furnace atmosphere through it. Finite difference method and the zonal method to calculate complex heat transfer is used for numerical implementation of the model. A mathematical model of the thermal operation of a thermal furnace to heat bulk cages has been developed, considering the filtration of combustion products. The developed mathematical model is designed to find new modes of thermal operation of the heating furnace, which provide a specified quality of the final product with a minimum fuel consumption or maximum furnace productivity according to the given fuel consumption rate.

Study of Mineralogy and Metallurgical Properties of Lump Ores

The ironmaking process in blast furnaces using iron lumps is an energy-efficient and low-carbon initiative that helps lower the cost of the ironmaking process. The physical and chemical properties of raw materials, process mineralogy, and metallurgical properties of three Malaysian iron ore lumps were researched in this paper, with the goal of showing the relationship between metallurgical qualities and mineralogy. The results show that lump ore A has a better decrepitation index, with a DI−6.3mm of only 0.2%; lump ore J has better reducibility and low-temperature reduction disintegration index, with RI and RDI+6.3mm values of 88.39% and 87.55%, respectively; and the three lump ores have excellent softening and melting properties and a high permeability index. The metallurgical properties of lump ore and mineralogical characterizations are generally correlated, the decrepitation performance of lump ore is mostly determined by the presence of goethite, and the original porosity is unrelated. The higher the content, the worse the decrepitation performance; lump ore’s reducibility is mostly determined by the open porosity and the content of newly generating hematite at high temperatures, which has no relationship to its original porosity. The higher the open porosity, the higher the goethite content, the higher the newly generated porous hematite content, and the better the lump ore’s reducibility; the higher the original hematite content in the lump ores, the lower the open porosity at high temperatures and the worse the reduction degradation characteristics.

Effect of hot metal charging on economic and environmental indices of electric arc furnace steelmaking in China

Hot metal (HM) charging electric arc furnaces (EAF) with good production indices and economic benefits have been widely used in China. In this study, the economic and environmental indices of EAF were analyzed based on a mechanistic process model, and the energy consumption, carbon emissions, and production costs with different hot metal ratios (HMRs) were obtained. There are strong correlations between HMR and the energy consumption, carbon emissions, and production costs of EAF. The Pareto optimal solution for the current state is 50% HMR-EAF, which produces carbon emissions of 1402.68 kg-CO2/t and a production cost of 3609.40 CNY/t. Considering the current situation in China, this process is more in line with the need to coordinate the economic and environmental indices of steel plants compared to direct reduced iron (DRI)-EAF, scrap-EAF, and blast furnace-basic oxygen furnace (BF-BOF) processes. Moreover, it has the potential to be more easily realized and promoted.

Numerical Study of Gas Flow and Temperature Patterns in the Blast Furnace Throat

The gas distribution in the shaft is of key importance for the performance of the blast furnace process, since it affects the way in which the burden is heated and reduced, and also the pressure drop in the shaft and cohesive zone. Traditionally, the gas distribution is followed by gas temperature measurements at several points over the radius or diagonal by the above-burden probes, but novel acoustic techniques can estimate the gas temperature over the full cross section of the throat. A fundamental problem is that the gas is redistributed in the upper bed and above it, so the measured profile may no longer reflect the conditions in the shaft. This paper studies the gas redistribution by a CFD model of the throat region, neglecting heat transfer to the bed and walls. It is demonstrated that strong redistribution and downward-swirling flows may occur, which affect the measurements. The arising conditions under different assumptions of the bed state are studied, and the dynamics of the changes are also briefly analyzed. A comparison of the findings of the computational model with acoustic measurements of the gas temperatures in the throat of two industrial blast furnaces reveals a good resemblance. The results of the study sheds light on the complex flow conditions of the gas in the blast furnace throat region and can be used to interpret information from measuring devices and to study how their positioning affects the measurements.

Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation

Blast furnace gas (BFG) is an important energy-carrying byproduct of the iron and steel industry. High-accuracy prediction of BFG generation is the basis of the dynamic balance of gas supply–demand and energy scheduling. However, due to instrument faults, measurements of BFG are discontinuous or inaccurate, making it difficult to accurately predict future BFG generation by using historical data, which seriously restricts the development of intelligent management and coordination between various gas sources and users. To solve this problem, an attention mechanism-aided data- and knowledge-driven soft sensor is proposed to predict BFG generation. To reduce the complexity of the samples, the proposed method selects key features to simplify the model input by using attention mechanism. Genetic algorithm (GA) is used to optimize hyperparameters to improve the stability of the model. In addition, combined with the knowledge of the blast furnace process, the prediction results are reasonably constrained. The results show that the prediction accuracy of the A-DK-GA-XGBoost model is higher than that of the other prediction models, with a mean absolute error of 68.2 m3/min, a symmetric mean absolute percentage error of 0.83%, a root mean square error of 68.71 m3/min, and an R squared of 99.06%. It is proven that the A-DK-GA-XGBoost model has superior performance.

Individual and combined effect of operating and geometric parameters on combustion behaviour of low and high volatile coals

ABSTRACT Pulverized coal injection technology plays a crucial role in controlling the blast furnace process but increasing the combustion efficiency of coal remains a challenge. Towards this, the manuscript attempts to examine the performance of different coals subjected to different operating and geometric parameters through a validated numerical model. Two coals with different volatile matter have been selected for this study while oxygen enrichment, lance position, and particle size distribution have been varied. Among all parameters, particle fineness has proven to be the major contributor towards improving burnout. The underlying mechanism has been explained in terms of oxygen distribution and particle temperature. In addition, the combined effect of all parameters increases the final conversion by 6% and 10% for high and low volatile coals respectively. Ease of volatile release plays an important role in controlling the overall burnout where a higher release rate for high volatile coal somewhat compensates for the impact of the parameters studied.

A Data-Based Compact High-Order Volterra Model for Complex Blast Furnace System

In this article, a data-based compact Volterra model is constructed to carry out the silicon prediction task. The main motivations are to, on the one hand, make better use of the strong memory ability of the Volterra series, which is suitable for reflecting the large inertia of the blast furnace process, on the other hand, overcome the difficulty that high complexity of the original Volterra models may result in serious overfitting. Six kinds of models, including single-input and two-input linear, second-order and third-order compact Volterra models, are successively designed and verified through a real blast furnace case. The reasonable agreement between the predicted values and the observed values indicates the compact Volterra model, especially the single-input third-order Volterra model, are powerful and competitive for describing the complex blast furnace system. The experimental results can serve as a guide for the blast furnace operators to judge the in-furnace thermal state change in time and further provide an indication on how to control the blast furnace in advance.

Mathematical Modeling of the Ejected Droplet Size Distribution in the Vicinity of a Gas–Liquid Impingement Zone

The controlled splashing of metal droplets plays a decisive role in the kinetics of the basic oxygen furnace (BOF) process. In this work, a mathematical model was developed for predicting the size distribution of spherical droplets ejected at an impingement zone. Harmonic oscillators are used to describe the ejection sites, and the upper limit for the droplet population is calculated through a force balance. The model was validated against literature data from high-temperature crucible experiments involving different supply pressures and lance heights as well as both single-hole and multihole lances. The predicted size distribution of the metal droplets was found to be in good agreement with the droplet size distributions measured from outside the crucible. The model was also applied for predicting the size distribution parameters of the Rosin–Rammler–Sperling (RRS) size distribution function. The model developed is computationally light and is suitable to be used as a part of offline and online simulation tools for the BOF process.