Industrial Blast Furnace Research Articles

A Predictive Database Model for Process Analysis in A Large Industrial Blast Furnace

A Predictive Database Model for Process Analysis in A Large Industrial Blast Furnace

Particle size-dependent properties of a char produced using a moving-bed pyrolyzer for fueling pulverized coal injection and sintering operations

Pyrolytic chars are a potential high-quality fuel for use in iron ore sintering and pulverized coal injection (PCI) operations of blast furnace. In this study, industrial char produced from a moving-bed pyrolyzer was screened into four particle size ranges and compared with coke used in an industrial sintering process and a PCI coal used in an industrial blast furnace, and their basic compositions, micropore structures, functional group distributions, carbonaceous structures, reactivities, and gas release behaviors during combustion were analyzed. Furthermore, the mechanism underlying the particle size-dependent properties of the char was analyzed by comparing it with a char prepared under laboratory-simulation conditions. Results demonstrate that char particles larger and smaller than 3 mm (the turning point) have different structures and properties. More specifically, particles larger than 3 mm have stable properties, better developed pore structures, carbonaceous structures that are more ordered, superior reactivities, and lower releases of SO2 and NOx. Therefore, they represent a desirable option for use in fueling sintering and PCI operations. Char particles smaller than 3 mm were further investigated by screening them into particle size ranges, and results showed that they had significantly varied compositions and structures, which further confirmed their unsuitability for use in the two operations. Finally, the particle-size-dependent properties of the char can be explained by the differing residence times, the radial variations in pyrolysis degrees, and the mechanical (or thermal) stresses experienced by the different raw coal particles within the pyrolyzer.

Advances in thermal level measurement techniques using mathematical models, statistical models and decision support systems in blast furnace

The estimation of thermal level in blast furnace is of utmost importance, because the processes occurring inside the blast furnace are complex in nature and any drift in thermal level could lead to abnormal furnace state. The present review is made to understand the methods for estimating thermal level in blast furnace, and the drift in estimation of the thermal level. The thermal level estimation is divided into 3 categories, viz. mathematical models, statistical models and decision support systems. The mathematical models are based on the first principle of thermodynamics and give an estimate of the thermal level in blast furnace. On the other hand, the statistical models are mainly the data-based approach that uses the historical data to predict the instability in blast furnace. Lastly, the decision support systems are the prescriptive models that give the recommendations for making the necessary corrections in the process parameters to avoid occurrence of abnormality in blast furnace. Further, the drifts in estimation of thermal level by these techniques are identified and recommendations are made to improve the accuracy of thermal level estimation. The recommendations to control thermal level in blast furnace are provided which when applied in the industrial blast furnace, can avoid the occurrence of catastrophic condition.

A review on liquid level measurement techniques using mathematical models and field sensors in blast furnace

Stable blast furnace operation requires proper drainage of liquid from the hearth. The estimation of drainage rate and hearth liquid level are of utmost importance for understanding the underlying hearth phenomena. The present review gives an insight of the need for measurement of the hearth liquid level and further throws light on various methods of estimation of hearth liquid level in-depth, which is primarily divided into 2 categories namely; model-based estimation and sensor-based estimation. Although the model-based estimation and sensor-based estimation have their own advantages and disadvantages, an integrated system comprising of both methods could potentially facilitate operators to reveal the state of hearth, which is unless not available with the implementation of a single method of estimation. Furthermore, the challenges in the estimation and the prospects for determining the reliable hearth liquid level measurement in blast furnace are discussed. The article is presented to give prospect of encountering the drift occurring in estimation of liquid level, which leads to inaccurate prediction and simulation with the industrial blast furnace. Lastly, the article gives recommendation for improving the liquid level estimation methods in blast furnace.

Ensemble pattern trees for predicting hot metal temperature in blast furnace

In steel industry, it is crucial to predict hot metal temperature (HMT), which is strongly related to the product quality and the thermal state, to keep high productivity of the blast furnace. The present work proposes a novel ensemble pattern trees model to predict HMT. Ensemble pattern trees is a robust nonlinear modeling method, which aggregates a set of pattern trees models into a single predictive model via the bagging technique. Ensemble pattern trees overcomes the drawback of single pattern trees which may not be robust enough against the random variations such as process perturbations and noises in the blast furnace. In addition, a novel variable importance measure derived from the ensemble pattern trees is proposed to understand which process variables affect the final hot metal quality. The proposed method was validated through an industrial blast furnace ironmaking process, and the results have demonstrated its superiority to several conventional methods.

Numerical investigation of the effects of size segregation on pulverized coal combustion in a blast furnace

Pulverized coal combustion in a blast furnace raceway can play a critical role in blast furnace iron-making operations. This research study integrated the particle combustion and the raceway formation processes using a refurbished coupling method based on computational fluid dynamics (CFD). The CFD model was validated, and there was good agreement between the simulation results and the two sets of experimental data. The combustion behaviors of pulverized coal particles in an industrial blast furnace were also simulated over a wide size range. The simulation results showed that size segregation occurred along the vertical direction in the raceway. Along the coal plume, the effectiveness of the size segregation process can be significantly reduced by the turbulence generated owing to the existence of the lance, the expansion of the gas jet near the tuyere tip, and the gas recirculation at the front end of the raceway. The burnout rate of particles smaller than 60 μm was also shown to be sensitive to the degree of weakening of the size segregation. The particle distribution in the coke bed indicated that the group of particles with diameters equal to 52.5 μm was associated with the largest proportion of unburnt char. This research extended the applicability of the existing numerical methods and provided a better understanding of the pulverized coal injection (PCI) process from the viewpoint of size segregation, which is beneficial to the further optimization of PCI usage

Influence of the properties of blast furnace slag on cast iron heating at pulverized coal injection

In the course of the studies at two industrial blast furnaces, we established a change in the characteristics of slag mode and cast iron heating, resulting from the transition to the pulverized coal fuel injection with a decrease in basicity of CaO/SiO 2 slag from 1.16 to 1.10 and from 1.20 to 1.12 units. It was shown that depending on a particular influence of alumina and magnesia in the slag composition, as well as on their ratio, a decrease in slag basicity can be accompanied by both an increase and a decrease in viscosity. During the research, the dependence between slag viscosity at the temperature of 1,400 °C and chemical heating of cast iron heating at the outlet was established. According to obtained data, an increase in slag viscosity by 0.1 Pа∙s was accompanied by an increase in silica concentration in cast iron by 0.08–0.1 %. The relation of physical heating of metal with slag basicity and stoichiometry was established. It was shown that a decrease in basicity of (CaO+MgO)/SiO 2 by 0.1 units led to a decrease in cast iron temperature by 30 °C. Such a substantial effect of basicity of metal heating indicates the need to take this influence into account not only to control the blast furnace process, but also to state the thermal balance of steel smelting. It was shown that in the studied range of slag compositions, the influence of magnesia on heating of cast iron is limited. A slight influence of MgO on physical heating of metal was pronounced at a magnitude of the ratio Al 2 O 3 /MgO that is close to unity. Results of present research could be used to improve control over blast furnace smelting in terms of taking into consideration slag influence on heating of cast iron.

Changes in Microstructure and Chemical Composition of Deadman Coke of a 2800 m<sup>3</sup> Industrial Blast Furnace

Changes in Microstructure and Chemical Composition of Deadman Coke of a 2800 m<sup>3</sup> Industrial Blast Furnace

Viscosity of Industrial Blast Furnace Slag in Indian Scenario

Twenty-five numbers of blast furnace slag samples are collected from different operational Indian blast furnaces. The viscosities of these slag samples are estimated using various models developed by several researchers for the purpose. Subsequently, viscosities of some selected slag samples are measured using a high temperature viscometer to choose the best possible model that can be used in the present investigation. On the basis of the results, a comparison is made between the various models bringing about the shortcomings of these mathematical models. It is observed that the estimated viscosities by Iida model are best fitted with the viscosities of slags measured by high temperature viscometer and hence, the viscosity data obtained by Iida model are analyzed at length to ascertain the compositional dependence of slag viscosity. It is observed that the industrial blast furnace slag viscosity is greatly influenced by its CaO/SiO2 ratio, MgO and Al2O3 contents.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. This indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

What a Blast!: A Massive MIMO Radar System for Monitoring the Surface in Steel Industry Blast Furnaces

The main goal of surfacegauging radar applications is to obtain a complete image of a surface area inside a closed and possibly tightly sealed container filled with materials having sufficient reflectivity for highfrequency electromagnetic waves. Radar systems are applicable where standard acoustical or optical measurement techniques are strongly impaired due to harsh environmental conditions-such as extreme dust, heat, occasional heavy humidity, and aggressive chemicals-that occur, for example, inside a fully operative blast furnace.

Just-in-Time Correntropy Soft Sensor with Noisy Data for Industrial Silicon Content Prediction

Development of accurate data-driven quality prediction models for industrial blast furnaces encounters several challenges mainly because the collected data are nonlinear, non-Gaussian, and uneven distributed. A just-in-time correntropy-based local soft sensing approach is presented to predict the silicon content in this work. Without cumbersome efforts for outlier detection, a correntropy support vector regression (CSVR) modeling framework is proposed to deal with the soft sensor development and outlier detection simultaneously. Moreover, with a continuous updating database and a clustering strategy, a just-in-time CSVR (JCSVR) method is developed. Consequently, more accurate prediction and efficient implementations of JCSVR can be achieved. Better prediction performance of JCSVR is validated on the online silicon content prediction, compared with traditional soft sensors.

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

ABSTRACTA new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.

Features of performance of concrete and reinforced concrete structures of industrial facilities operated under impact of increased and high temperatures

Currently, the Normative base of Ukraine has moved to European standards. The process of harmonization with the Eurocodes was completed, new national normative documents of DBN and DSTU were developed and put into effect. At the same time it was turned out that the big section “Concrete and reinforced concrete structures intended for work in conditions of increased and high temperatures” is absent in the Eurocodes. By order of the Ministry of Regional Development, Construction and Housing and Communal Services of Ukraine, the development of a normative act is provided for the replacement of SNiP 2.03.04-84 with DBN with a change in status. Heat-resistant concretes in the elements of thermal aggregates are used in the iron and steel industry (blast furnaces, air heaters, coke batteries, etc.), non-ferrous metallurgy (graphite furnaces, fluidized bed furnaces, etc.), oil refining and petrochemical industry (tubular furnaces, vertical sectional furnaces, etc.), in the industry of building materials, and in various sectors of industry. These norms and rules apply to the design of concrete and reinforced concrete structures designed to operate under conditions of systematic exposure to increased (50 to 200 °C inclusive) and high (above 200 °C) process temperatures.

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

Identification of switching linear systems using self-organizing models with application to silicon prediction in hot metal

An approach based on self-organizing models is presented for identification of piecewise linear switching systems, whose dynamics switch between a number of modes. The proposed method is based on a formulation of the identification problem as a generalized plane-clustering problem, which is solved using self-organizing maps. The method does not assume that the system modes depend on the state, but the mode switches may occur in an arbitrary and unknown manner. The procedure does not require knowledge of the number of modes or system orders, and it can be used for both on-line and off-line identification. Numerical examples illustrate that the procedure identifies switching systems correctly. The identification method is also applied to data from an industrial blast furnace for modeling and prediction of the silicon content of the hot metal. A switched linear model is demonstrated to capture different dynamics of the process and an analysis of the results reveals how mode switching models gradual and rapid changes in the output. The resulting models are finally shown to provide insight into factors that govern the silicon content in the blast furnace in different states.

Double locally weighted principal component regression for soft sensor with sample selection under supervised latent structure

Just-in-time learning is an important framework for adaptive soft sensor modeling. However, the prediction accuracy strongly depends on the appropriate selection of relevant samples. Most traditional sample selection methods only consider the input information of samples, without any reference to the output information. In this paper, a new sample selection method in the supervised latent structure is proposed, in which the latent variables are highly related to the output. After that, to enhance the performance of the JITL local model, both sample importance and variable importance are taken into consideration for feature extraction in principal component regression (PCR), which is termed as double locally weighted principal component regression (D-LWPCR) in this paper. The effectiveness of the D-LWPCR model with sample selection in supervised latent structure is validated through a numerical example and an industrial blast furnace ironmaking process.

Enhanced just-in-time modelling for online quality prediction in BF ironmaking

Various data driven soft sensor models have been established for online prediction of the silicon content in blast furnace ironmaking processes. However, two main disadvantages still remain in these empirical models. First, most of traditional outlier detection methods for preprocessing the data samples assume that they (approximately) follow a Gaussian distribution and thus may be invalid for some situations. To address this problem, a support vector clustering (SVC) based efficient outlier detection method is proposed whereby the process nonlinearity and non-Gaussianity can be better handled. Second, only using a single global model is insufficient to capture all the process characteristics, especially for those complicated regions. In this paper, a reliable just-in-time modelling method is proposed. The SVC outlier detection is integrated into the just-in-time-based local modelling method to enhance the reliability of quality prediction. A healthier relevant data set is constructed to build a more reliable local prediction model. Moreover, the historical data set is updated repetitively in a reasonable way. The superiority of the proposed method is demonstrated and compared with other soft sensors in terms of online prediction of the silicon content in an industrial blast furnace in China.

Investigation of Freeze-Linings in a Nonferrous Industrial Slag

Slag freeze-lining reactor wall protection is a widely used technology in high temperature reaction systems. An air-cooled probe technique was used to investigate the formation of the freeze-linings in an industrial blast furnace slag. The compositions of the phases and the microstructures within the deposits have been characterized. It has been demonstrated that an industrial air-cooled probe can be used to take bath samples from actual smelter operations. In addition, a laboratory-scale experiment was undertaken to investigate the formation, stability, and bath/deposit interface temperature at steady-state conditions. Importantly, the current study has shown that stable steady-state freeze-linings can be obtained in metallurgical reactors operating below the slag liquidus temperature. In spite of the fact that solids are present in the bulk slag, the deposit thickness remains unaltered due to the dynamic conditions present at the deposit/bath interface. The results are consistent with findings obtained on a number of other different slag systems and the proposed dynamic mechanism of deposit stabilization. The findings demonstrate the basis for, and potential benefits that may follow from, operating the high temperature reactors at temperatures below the liquidus temperature, i.e., with solids present, without a catastrophic build-up of solids. This change in design concept could result in significant decreases in operating temperature, energy, and operating cost savings.

CFD modeling of multiphase reacting flow in blast furnace shaft with layered burden

The ironmaking blast furnace is a counter-current chemical reactor which includes the ascending gas flow and the counter-current descending porous bed (burden). A Computational Fluid Dynamics (CFD) model has been developed to simulate the multiphase reacting flow in blast furnace shaft. The gas flow dynamics, burden movement, chemical reactions, heat and mass transfer between the gas phase and burden phase are included in the CFD model. The blast furnace burden consists of alternative layers of iron ore and coke. A novel methodology is proposed to efficiently model the effects of alternative burden layer structure on gas flow, heat transfer, mass transfer and chemical reactions. Different reactions and heat transfer characteristics are applied for difference types of layer. In addition, the layered CFD model accurately predicts the Cohesive Zone (CZ) shape where the melting of solid burden taking place. The shape and location of the CZ are determined by an iterative method based on the ore temperature distribution. The theoretical formation and the methodology of the CFD model are presented and the model is applied to simulate industry blast furnaces. The proposed method can be applied to investigate the blast furnace shaft process and other moving bed system with periodic burden structure configuration.