Blast Furnace Ironmaking Process Research Articles

Abnormality Detection of Blast Furnace Ironmaking Process Based on an Improved Diffusion Convolutional Gated Recurrent Unit Network

Abnormality Detection of Blast Furnace Ironmaking Process Based on an Improved Diffusion Convolutional Gated Recurrent Unit Network

A Digital Twin Model of Three-Dimensional Shading for Simulation of the Ironmaking Process

Advanced manufacturing is a new trend for sustainable industrial development, and digital twin is a new technology that has attracted attention. Blast furnace smelting is an effective method in the manufacturing of iron and steel. Comprehensive and dependable surveillance of the blast furnace smelting process is essential for ensuring the smooth operation and improving of iron and steel output quality. The current technology makes it difficult to monitor the entire process of blast furnace ironmaking. Based on Unity 3D, this study presents a digital-twin virtual reality simulation system of blast furnace ironmaking. First, shading modeling creates a three-dimensional dynamic geometric model in different ironmaking system scenarios. Then, we script the animation and call particle system according to the motion mode of distinct geometric objects to give the dynamic effect of geometric objects. Shaders are the focus of the design and contributions. In addition, shader optimization technology can reduce hardware resource consumption and increase system fluency. Vertex shaders are used for all types of coordinate space transformation and vertex output; fragment shaders are used for texture sampling, light model calculation, normal calculation, noise superposition, and color output. The shader rendering technique allows for more realistic lighting effects. The presented dynamic digital twin system implements more realistic lighting analyzed in the ironmaking process. Virtual interaction logic’s design and deployment process is based on HTC VIVE hardware and VRTK toolkit. In the actual simulation process, the typical animation frame rate is stable at about 75 FPS (frames per second). The simulation system runs smoothly and a cutting-edge and state-of-the-art method for observing the blast furnace ironmaking process is suggested.

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.

Multi-Gate Mixture-of-Experts Stacked Autoencoders for Quality Prediction in Blast Furnace Ironmaking.

The blast furnace is an energy-intensive and extremely complex reactor in the ironmaking process. To reduce energy consumption, improve product quality, and ensure the stability of blast furnace operation, it is very important to predict the quality indicators of molten iron accurately and in real time. However, most of the existing product quality prediction models, such as the stacked autoencoder (SAE) model, use a single-channel stack structure. For such models, when the working conditions of the blast furnace ironmaking process change, a large prediction error will occur. To solve this issue, this paper develops a novel deep learning model, called the multi-gate mixture-of-experts stacked autoencoder (MMoE-SAE), for predicting the quality variable in the blast furnace ironmaking processes. The proposed MMoE-SAE model is constructed based on a multi-gate hybrid expert structure, in which a series of SAE networks are selected as experts. The MMoE-SAE model inherits the advantages of MMoE and SAE, which can not only extract the deep features of the data but also have better adaptability to the changes of working conditions in the blast furnace ironmaking process. To verify the effectiveness and practicability of the proposed MMoE-SAE model, it was applied to predict the silicon content of molten iron in the blast furnace ironmaking process. The experimental results demonstrate that the proposed MMoE-SAE model outperforms other prediction models in prediction accuracy.

A new monitoring method for the blocking time of the taphole of blast furnace using molten iron flow images

When to block the taphole is a concern in blast furnace ironmaking process. Currently, the blocking time of the taphole mainly depends on expertise, causing poor reliability of the blocking operation. Therefore, this paper proposes a new method to intelligently determine the blocking time using molten iron flow images. Firstly, a monitoring system is designed to obtain molten iron flow images at the taphole. Secondly, the blocking time monitoring problem is transformed into a classification problem of molten iron image to obtain tapping stages, and a novel image classification module, which further promotes the network’s representational ability by merging three feature extraction mechanisms in parallel, is proposed to accurately classify the images and obtain the tapping stages. Finally, a categorical correction model and a blocking time calculation model are constructed to obtain an accurate blocking time. Experimental results show that the proposed method can accurately monitor the taphole’s blocking time.

Production of pre-reduced sinter based on sensible heat updraft of sinter

Utilization of pre-reduction burden in the blast furnace ironmaking process is an important measure for realizing low-carbon operation. In this paper, a new process for the production of pre-reduced sinter based on sensible heat updraft of sinter was put forward. The effects of sinter temperature and reducing gas composition in the pre-reduction cooling section on metallization rate and sieve size distribution of pre-reduction sinter were studied. The results showed that the metallization rate and proportion of 10–12.5 mm particle size of pre-reduction sinter increase with the increase of sinter temperature. When the sintering temperature was 950 °C and the reducing gas was the mixture of H 2 and CO, the metallization rate of the pre-reduced sinter was up to 35.5%. When the sintering temperature was lower than 750 °C, the carbon deposition reaction between H 2 and CO reduces the proportion of 10–12.5 mm particle size of the pre-reduced sinter significantly. The metallurgical properties of pre-reduced sinter with different roasting systems under hydrogen-rich smelting in blast furnace were studied. The results showed that compared to the traditional sinter, the reduction degradation index, reducibility index, and softening-melting properties of pre-reduced sinter were improved, and the permeability and maximum differential pressure of the stock column were reduced, which was beneficial to the improvement of blast furnace production efficiency. Based on the comprehensive effects of sinter temperature and reducing gas composition on the metallization rate, sieve size distribution, and metallurgical properties of pre-reduced sinter, the quality of the pre-reduced sinter was better when the sintering temperature was higher than 750 °C and the reducing gas was a mixture of H 2 and CO. In addition, the new process could organically combine sinter sensible heat utilization, flue gas circulation, and denitrification to form a complete technological process, which had a good development prospect. • Production of pre-reduced sinter based on sensible heat of sinter was proposed. • Metallurgical properties of pre-reduction sinter were systematically studied. • The process can organically combine with flue gas circulation and denitrification. • Process route of production of pre-reduced sinter was schematically mapped.

Multi time scale inception-time network for soft sensor of blast furnace ironmaking process

Time series(TS) forecasting has been widely applied in many fields and industrial soft sensor is one of them. Most time series modeling methods require that all inputs are sampled at equal intervals. However, in industry, the process variables are often sampled on different time scales with varying intervals. To address this problem, in this paper, we designed a framework using deep learning with time representation techniques to model the long temporal industrial data with multiple sampling frequency. Data are aggregated into different time scale by time representation and the network extracts the information on both temporal and spatial dimensions simultaneously using the bottleneck layer and one-dimensional filter. The proposed model has a significant improvement compared with other methods and has been deployed in the factory and updated every month.

Anomaly detection of the blast furnace smelting process using an improved multivariate statistical process control model

Anomaly detection and early warning of the blast furnace ironmaking process is an important research direction of blast furnace production. In this paper, through the analysis and optimization of the multivariate statistical process control (MSPC) model, MSPC based on TOPSIS and the grey (GT-MSPC) model is established based on the problems of missing alarms, false alarms and untimely prediction in the abnormal detection process of the traditional blast furnace ironmaking production process. First, a composite volatility (CV) that consists of the squared prediction error and T2 is computed to form the MSPC model. The parameters of different training set sizes and different alarm conditions are optimized by using the technique for order preference by similarity to an ideal solution model to optimize the effect of anomaly detection and fault diagnosis. Second, according to the results of the contribution plot, the fault diagnosis of abnormal occurrence is carried out, the main influencing factors of abnormal occurrence are analysed, and the monitoring is strengthened. Finally, the grey model is used to predict CV to realize anomaly early warning and form the GT-MSPC model. By collecting the field data of blast furnace production and performing empirical analysis, the results show that the GT-MSPC model has higher anomaly detectability. The detection rate is 50 % higher than that of the manual observation method, and the GT-MSPC model alarms 16.4861 min earlier than manual detection method. Fault diagnosis can accurately locate the main influencing factors of blast furnace anomalies, such as the furnace body static pressure and differential pressure. In summary, the GT-MSPC model realizes the accurate identification and prediction of anomalies and greatly reduces the occurrence of missing and false alarms and untimely prediction in the blast furnace system, thus reducing the risk of blast furnaces. It can be used to assist field engineers in dealing with abnormal faults and improve the product quality and production safety of blast furnace ironmaking.

A novel knowledge enhanced graph neural networks for fault diagnosis with application to blast furnace process safety

With the advent of industry 4.0, many traditional industries are moving toward automation, intelligence, and large-scale. The continuous expansion of production scale also means that the structure of industrial processes and the interactions between subsystems are becoming increas- ingly complex, which also introduced potential safety risks into the actual production. Due to the complexity and danger of the production process, the process safety of the modern blast furnace (BF) ironmaking process is a prominent problem. In this paper, a novel fault detection and di- agnosis (FDD) framework is proposed, which can detect the abnormality and infer the root cause with no need of building an accurate mechanism model as the traditional methods. After a fault is detected, to better learn fault propagation mechanisms of the BF ironmaking process, a process knowledge model was proposed and integrated with the data-driven approaches for fault diagnosis. Then, to discover the causalities matrix of the faulty data generating procedure, a novel root cause analysis method based on Graph Neural Networks (GNN) was developed. Moreover, to accurately describe the causal interactions of faulty variables and solve the problem of the redundant edges in the causal discovery, the process knowledge constraint item was added to the GNN model to guarantee the discovered causal graph matches with the practical domain knowledge to strengthen its application in the practical industry. The experimental results on real BF data set and a bench- mark data set (The RT 580 data set) demonstrate that our algorithm not only obtains a significant improvement over other methods but also has a favorable application in the industrial process.

The strength prediction model of iron ore sinter based on an artificial neural network

ABSTRACT The iron ore sinter is still the main raw material for the blast furnace ironmaking process, its properties, such as strength and reducibility, are of vital importance to the productivity and the smooth operation of the blast furnace. In the present study, one model based on an artificial neural network (ANN) was established to predict the sinter strength. The ANN model was trained with the sample set, which was generated from the credible data from the published papers. The comparison between the direct prediction model and the indirect prediction model with the amount of liquid phase and spinal phase calculated with thermodynamic theory as the middle layer was carried out. The results show that the indirect ANN model gave much higher accurate prediction results than that of the direct one without the middle layer.The parametric study with the validated model shows that the sinter strength increased first with increasing the SiO2 to 5.4% and then decreased with further increasing the SiO2.

Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling.

The blast furnace ironmaking process is the core of steel manufacturing, and the optimization of this process can bring enormous economic and environmental benefits. However, previous data-driven optimization methods neglect the uncontrollability of part of the variables in the predictive modeling process, which brings great uncertainty to the optimization results and adversely affects the optimization effect. To address this problem, a consistency optimization framework based on controllability assurance soft sensor modeling is proposed. The method achieves the information extraction of uncontrollable variables in a process-supervised way, and improves the posterior distribution prediction accuracy. The method also proposes an integrated self-encoder regression module, which uses the regression to guide the encoding, realize the construction of latent features, and further improve the prediction accuracy of the model. Integrating the prediction module and the multi-objective gray wolf optimizer, the proposed model achieves the optimization of the blast furnace ironmaking process with only controllable variables as prediction model inputs while being capable of giving uncertainty estimates of the solutions. Empirical data validated the optimization model and demonstrated the effectiveness of the proposed algorithm.

Intelligent optimization system of burden structure in sintering and blast furnace ironmaking process based on improved genetic algorithm

ABSTRACT Under the premise of ensuring the smooth production of the blast furnace, the quality and output of molten iron, minimizing the cost of hot metal and reducing the impact of harmful elements are the most difficult problems faced by iron and steel enterprises at present. The intelligent optimization system of burden structure in the whole process of sintering and blast furnace ironmaking is developed based on the principle of blast furnace ironmaking and material balance, improved genetic algorithm and penalty function. It includes four parts: raw fuel database, intelligent sintering optimization, blast furnace burden structure intelligent optimization and company cost accounting. The industrial application shows that the system can not only guarantee the output and quality of molten iron, but also effectively reduce the cost of molten iron in the process of optimizing the sintering and blast furnace production of the steel company.

Blast furnace ironmaking process with super high TiO2 in the slag: Density and surface tension of the slag

Blast furnace ironmaking process with super high TiO2 in the slag: Density and surface tension of the slag

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the current research works employ linear correlation coefficient methods to select input features in modeling, which may not fully take the nonlinear and coupling relationships between features into account. Therefore, this article considers the input feature selection issue of silicon content prediction model from a new perspective and proposes a multiobjective evolutionary nonlinear ensemble learning model with evolutionary feature selection mechanism (MOENE-EFS), in which extreme learning machine is adopted as the base learner. MOENE-EFS takes the input feature scheme of each base learner as well as their network structure and parameters as decision variables and proposes a modified nondominated sorting differential evolution algorithm to optimize two conflicting objectives, i.e., accuracy and diversity of base learners, simultaneously. Through the optimization, a set of Pareto optimal base learners with high accuracy and strong diversity can be obtained. Moreover, different from the linear ensemble methods commonly used in classical evolutionary ensemble learning, this article proposes a nonlinear ensemble method to combine the obtained base learners based on differential evolution. Experimental results indicate that the two proposed strategies, i.e., evolutionary feature selection and nonlinear ensemble, are very effective in improving the accuracy and stability of the prediction model. MOENE-EFS also outperforms the other prediction models in both benchmark data and practical industrial data. Furthermore, analysis on the input features of all Pareto optimal base learners shows that the evolutionary feature selection is capable of selecting essential features and is consistent with human experience, which indicates it is a promising method to deal with the input feature selection issue in silicon content prediction.

Numerical modeling and analysis of hydrogen blast furnace ironmaking process

Hydrogen use in the blast furnace (BF) ironmaking process promises to mitigate the carbon footprint substantially. This paper presents a numerical study on the effects of hydrogen enrichment on the inner states and overall performance of a 380-m3 industrial BF. This is done using a recently developed three-dimensional process BF model. The model is validated under different conditions. A case of 40-degree pure hydrogen being injected into the hearth tuyeres is considered. The hydrogen content in the bosh gas varies from 3% to 49.5%, while the hot metal temperature, bosh gas volume, and flame temperature are fixed. The numerical results reveal that with increasing hydrogen enrichment, the coke rate decreases initially to a minimum, then increases; however, the productivity increases and subsequently slows down. Therefore, an optimum hydrogen enrichment is identified according to the minimum coke rate. The flow and thermochemical behaviors are analyzed in detail. It shows that as the hydrogen enrichment intensifies, the cohesive zone has a narrower horizontal width and a shorter total length, contributing to a reduced tuyere pressure. The relative importance of chemical reactions at different hydrogen enrichments is also analyzed. It shows that the intensified hydrogen enrichment enhances the H2 indirect reduction and impedes the carbon solution reaction. However, it also decreases the CO indirect reduction rate that is more significant than the H2 indirect reduction rate under the conditions considered. In addition, hydrogen enrichment requires extra coke combustion to compensate for the sensible heat of the reduced hot blast rate. Overall, hydrogen enrichment improves the energy efficiency of BF.

Deep weighted joint distribution adaption network for fault diagnosis of blast furnace ironmaking process

The blast furnace (BF) ironmaking process has the following characteristics: (1) few fault data (2) large fluctuation in data distribution. For robust fault diagnosis, some transfer learning-based methods are proposed. They mostly aim at aligning only the marginal and conditional distribution discrepancy of datasets. However, in actual application, ignoring the prior distribution will lead to degraded domain adaptation performance. Hence, we propose a new fault diagnosis method called deep weighted joint distribution adaption network (DWJDAN) to align the probability distributions of the dataset more comprehensively. The DWJDAN consists of three modules: condition recognition, class prior distribution adaptation, and joint distribution adaptation. The condition recognition is constructed by a convolutional network (CNN) to extract features and generate health labels of BF. The class prior distribution adaptation generates class-specific auxiliary weights to exploit the prior probability on the source and target domains. The joint distribution adaption facilitates the CNN to learn domain-invariant features by minimizing maximum mean discrepancy (MMD) with weighted data. Experiments using actual industrial BF data show that the proposed method can obtain good results in diagnosing the abnormal conditions of the BF ironmaking process.

Abnormality Monitoring in the Blast Furnace Ironmaking Process Based on Stacked Dynamic Target-Driven Denoising Autoencoders

Accurate monitoring of abnormalities is of great significance to the stable operation of the blast furnace ironmaking process. This article proposes a data-driven model to accurately monitor the abnormal conditions of blast furnaces. Generally, data-driven models primarily rely on feature extraction from high-dimensional raw data. Recently, deep learning networks have been developed and considered a promising technology in extracting high-level abstract features. However, most of these networks cannot capture deep target-related features for abnormality monitoring. Thus, this article proposes a novel stacked dynamic target-driven denoising autoencoder for layer-by-layer hierarchical feature representation, and the dynamic relationship between samples and targets is described by dynamic factors. Then, we design a corresponding target-driven reconstruction loss function to pretrain the deep network successively. Experimental results in an ironmaking plant demonstrate the effectiveness and feasibility of the proposed method.

Abnormality monitoring and causality analysis based on KF-PDC and IACE in blast furnace ironmaking process

ABSTRACT Blast furnace (BF) ironmaking is a highly complicated process with multi-variable nonlinear coupling and multi-mode characteristics. In this article, a developed kernel function partial derivative contribution (KF-PDC) is proposed for abnormality location, which makes up for the deficiency of linear multivariate statistical process monitoring (MSPM) and de-redundancies the variable candidate set of causality analysis. Then, to eliminate the influence of multi-mode, an online interval adaptive causation entropy (IACE) is established to analyse the cause–effect relationships of candidate abnormal variables, which contributes to distinguishing the direct and indirect causality, and the Haar wavelet based on the sliding window (HWSW) is constructed for the segmentation of different modes online. Finally, a case study using actual industrial BF ironmaking data illustrates that the monitoring method can better capture the abnormal furnace conditions and effectively obtain the root cause and propagation path.

Research on modified blast furnace dust in demulsification: The synergistic effect of ferric oxide, hydrophobic carbon, and polysilicate

ABSTRACT Current treatment processes for the cold rolling emulsion wastewater were in the dilemma that the cost was so high while the efficiency was not satisfied in China. In this work, a novel material with high-performance demulsification was obtained and used to treat the emulsion wastewater efficiently by modifying blast furnace dust (BFD), a steel industry waste. Firstly, the BFD was characterized by various analytical techniques and the results suggested that hydrophobic functional groups, positively charged iron oxide, and polysilicon in the BFD were contributed to removing stable oil droplets. Therefore, the BFD modification was conducted accordingly by optimizing the proportion and reaction conditions of these three components. The study demonstrated that the removal rate of oil and CODcr reached 75.21% and 81.23% under the conditions of the carbon type of GWF-HAC-R90, the carbon content of 14.86%, and the n(Fe)/n(Si) of 1.55, respectively. Based on this, the effects of pH, demulsifier dosage, and average agitation rate were investigated. The emulsion components before and after demulsification were analyzed by GC-MS, and the demulsification mechanism was expounded combined with kinetics. Results showed that the Fe2O3 with positive charge played a dominant role in emulsions with mainly anionic surfactants, while hydrophobic carbon structures and polysilicate acted as the auxiliaries. Besides, comprehensive analysis and characterization results suggested that the demulsification effect was a combination of the synergic processes: 1) electrostatic interaction developed by the anionic surfactant oil droplets and the positively charged the BFD particles; 2) hydrophobic association among the oil droplet with nonionic surfactant and amphiphilic carbon-iron complexes; 3) adsorption bridging between the surfactant oil droplets and polysilicate. The results of comparative tests in treating the actual cold-rolling emulsion wastewater showed the MBFD could bring about significant technical, economic benefits and achieve utilization of metallurgical solid wastes. Implications: Blast furnace dust (BFD) is an industrial solid waste obtained by dry de-dusting from blast furnace gas during the blast furnace ironmaking process. The main components of BFD are iron oxide and carbon, and also contain small amounts of different recoverable non-ferrous metals zinc, secret, indium and lead, which have recovery value. In this study, we enhanced the three parts of BFD and used to demulsification. In this study, the BFD was modified in three aspects (hydrophobic functional groups, positively charged iron oxide, and polysilicon) to achieve the secondary utilization of waste in emulsion wastewater treatment. And investigated the effect of reaction conditions on the demulsification effect with modified blast furnace dust (MBFD). Furthermore, the GC-MS analyzes combined with kinetics interpret the demulsification mechanism with the MBFD as a secondary resource. Finally, the comparative tests in treating the actual cold-rolling emulsion wastewater showed the MBFD could bring about significant technical and economic benefits and achieve utilization of metallurgical solid wastes.

Fault Diagnosis of Blast Furnace Iron-Making Process With a Novel Deep Stationary Kernel Learning Support Vector Machine Approach

In the blast furnace iron-making process (BFIP), there still has been a significant push to maintain a stable process and ensure maximum efficiency. Although some control systems can compensate for multiple types of disturbances, some significant process faults always require precise human intervention to avoid safety hazards. Therefore, it is crucial to develop an efficient and stable diagnosis system to identify these faults quickly. This paper focuses on a novel approach called deep stationary kernel learning support vector machine (DSKL-SVM) for BFIP fault diagnosis. To eliminate the impact of nonstationary property on modeling, analytic stationary subspace analysis (ASSA) is adopted to estimate consistent underlying features. Then, design a multi-layer stacked deep kernel network to explore deep nonlinear information further. A DSKL-SVM classifier and the corresponding two-tier model optimization algorithm are constructed to isolate different data types. At last, a series of case studies based on actual BFIP present the effectiveness of DSKL-SVM. The results demonstrate that the proposed method has an outstanding effect on fault diagnosis, and it is verified that the performances of stationary projection, deep stationary nonlinear construction and online computation times are superior to other methods.