Silicon Content In Hot Metal Research Articles

Prediction Model for Silicon Content of Hot Metal Based on PSO-TCN

Prediction Model for Silicon Content of Hot Metal Based on PSO-TCN

Deep learning for robust forecasting of hot metal silicon content in a blast furnace

AbstractThe hot metal silicon content is a key indicator of the thermal state in the blast furnace and it needs to be kept within a pre-defined range in order to ensure efficient operations. Effective monitoring of silicon content is challenging due to the harsh environment in the furnace and irregularly sampled measurements. Data-driven approaches have been proposed in the literature to predict silicon content using process data and overcome the sparsity of silicon content measurements. However, these approaches rely on the selection of hand-crafted features and ad hoc interpolation methods to deal with irregular sampling of the process variables, adding complexity to model training and optimisation, and requiring significant effort when tuning the model over time to keep it to the required level of accuracy. This paper proposes an improved framework for the prediction of silicon content using a novel deep learning approach based on Phased LSTM. The model has been trained using 3 years of data and validated over a 1-year period using a robust walk-forward validation method, therefore providing confidence in the model performance over time. The Phased LSTM model outperforms competing approaches due to its in-built ability to learn from event-based sequences and scalability for real-world deployments. This is the first time that Phased LSTM has been applied to real-world datasets and results suggest that the ability to learn from event-based data can be beneficial for the process industry where event-driven signals from multiple sensors are common.

Prediction of Silicon Content of Hot Metal in Blast Furnace Based on Optuna-GBDT

Prediction of Silicon Content of Hot Metal in Blast Furnace Based on Optuna-GBDT

Improved operation of a large-scale blast furnace using a hybrid dynamic model based optimizing control scheme

In the steel industry, the blast furnace (BF) is a core piece of equipment along the route from iron ore to steel, with the largest energy consumption and CO2 emissions. The stable, efficient, and economically viable thermal control of blast furnaces is still a challenging task, and fully automatic solutions are not applied industrially. The process exhibits multi-phase and multi-scale physio-chemical phenomena, in the presence of fast and very slow dynamics with latency periods of 6-8 h. Direct online measurements of key inner variables are missing, and unknown disturbances are strongly influencing the process; in particular, the quality of solid raw materials that are fed at the top of the furnace. Partial automation is the state-of-the-art strategy in the operation and control of industrial blast furnaces, and the experience and the dedication of the operators have a direct impact on the stability and efficiency of the operation. Model-based optimizing control schemes, which can be deployed either in closed-loop or as a guidance system to achieve a smooth, efficient, and reliable operation of blast furnaces, are promising. In this work, we propose a hybrid dynamic model based optimizing control scheme for the stable, energetically efficient, and economically optimal thermal control of blast furnaces. The underlying optimization problem is formulated as a combined tracking and performance optimizing control problem, where the goal is the tight control of the hot metal silicon content ([Si]) and the slag basicity (SB), while simultaneously maximizing the CO-efficiency of the furnace. These two variables are key product quality indices, and [Si] is an indicator of the internal thermal state of the BF process. Simulation results using real operational data of a large-scale industrial blast furnace are presented to demonstrate the potential of the approach for an improved operation of blast furnaces.

Prediction of Silicon Content in the Hot Metal of a Blast Furnace Based on FPA-BP Model

In the process of blast furnace smelting, the stability of the hearth thermal state is essential. According to the analysis of silicon content in hot metal and its change trend, the operation status of the blast furnace can be judged to ensure the stable and smooth operation of the blast furnace. Based on the error back-propagation neural network (BP), the flower pollination algorithm (FPA) is used to optimize the weight and threshold of the BP neural network, and the prediction model of silicon content is established. At the same time, the principal component analysis method is used to reduce the dimension of the input sequence to obtain relevant indicators. The relevant indicators are used as the input, and silicon content in the hot metal is used as the output, which is substituted into the model for training and utilizes the trained model to predict. The results show that the hit rate of the prediction model is 16% higher than the non-optimized BP prediction model. At the same time, the evaluation indicators and operation speed of the model are improved compared with the BP prediction model, which can be more accurately applied to predict the silicon content of the hot metal.

Collaborative Extraction of Intervariable Coupling Relationships and Dynamics for Prediction of Silicon Content in Blast Furnaces

Soft sensor of silicon content in hot metal is challenging due to the nonlinearity, dynamics, and non-Euclidean coupling relationship between process variables. The inter-variable coupling relationship widely exists in the ironmaking process. Nevertheless, they are generally overlooked in modeling. To solve this issue, this paper proposes a novel temporal graph convolutional network with supervised graphs (TGCN-S). Different from traditional soft sensor methods, TGCN-S explicitly models the inherent non-Euclidean and irregular coupling relationships in the ironmaking process. First, a graph structure learning module is designed, which can adaptively learn potential inter-variable relationships from data. This module is embedded in TGCN-S to learn the supervised graph structures in an end-to-end manner. Second, a novel methodological framework is proposed based on the supervised graph structures, which can collaboratively extract the inter-variable coupling relationships and intra-variable dynamics. In this structure, information between process variables is selectively aggregated while taking into account the dynamics within variables. Finally, experiments based on the real ironmaking process demonstrate the effectiveness of the proposed method.

Retracted: Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN.

[This retracts the article DOI: 10.1155/2021/1767308.].

A Context-Aware Enhanced GRU Network With Feature-Temporal Attention for Prediction of Silicon Content in Hot Metal

Blast furnace ironmaking is one of the most complicated industrial processes. As an essential reference index for blast furnace operation, the prediction of silicon content in hot metal is important. Most previous works focus on the dynamics and nonlinearity of the process without comprehensively considering the correlation between the process variables and the silicon content. Besides, the timing mismatch of input and output variables caused by the inertia of the process still cannot be handled effectively. To solve these problems, in this article, the proposed model puts extra attention on input variables to strengthen the information of key variables, and introduces causal convolution-based self-attention to incorporate local context into attention mechanism in the temporal dimension, which realizes local awareness enhancement and variables soft alignment. With a series of theoretical and practical verification, the hybrid model shows significant improvement at hit rate and mean-square error.

Modeling of artificial neural networks for silicon prediction in the cast iron production process

The main way to produce cast iron is in the blast furnace. In the production of hot metal, the control of silicon is important. Alumina and silica react chemically with limestone and dolomite to form blast furnace slag. In this work, 12 artificial neural networks (ANNs) were modeled with different numbers of neurons in each hidden layer. The number of neurons varied between 10 and 200 neurons. ANNs were used to predict the silicon content of hot metal produced. The ANN with 30 neurons showed the best performance. In the test phase, the mathematical correlation was 97.5% and the mean square error (MSE) was 0.0006, and in the cross-validation phase, the mathematical correlation was 95.5% while the MSE was 0.00035.

Predictive modeling of the hot metal silicon content in blast furnace based on ensemble method

The silicon content of the hot metal is not only an important indicator of the quality of the hot metal and blast furnace (BF) operation but also reflects the level of energy utilization and the thermal state within a BF. It is important to develop an accurate prediction model for hot metal silicon content. In present study, two models for predicting hot metal silicon content are developed based on two ensemble learning methods, random forest regression (RFR) and extreme gradient boosting (XGBoost). First, Box plot was used to visualize the collected data and determine extreme outliers in the raw data. Extreme outliers are replaced with null value, and all null value are filled by linear interpolation. Secondly, feature selection is performed using recursive feature elimination. Cross-validation is performed to optimize machine learning hyperparameters and having a robust accuracy measure. Based on this, two hot metal silicon content prediction models are developed. Finally, the prediction results of the two models are compared and evaluated. The results show that both ensemble learning models show good prediction performance in predicting hot metal silicon content, but the prediction performance of the RFR model is better than that of the XGBoost model and reaching 98.77%.

Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

The main production route for cast iron and steel is through the blast furnace. The silicon content in cast iron is an important indicator of the thermal condition of a blast furnace. High silicon contents indicate an increase in the furnace's thermal input and, in some cases, may indicate an excess of coke in the reactor. As coke costs predominate in the production of cast iron, tighter control of the silicon content therefore has economic advantages. The main objective of this article was to design an artificial neural network to predict the silicon content in hot metal, varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, 100, 125 , 150, 170 and 200 neurons. In general, all neural networks showed excellent results, with the network with 30 neurons showing the best results among the 12 modeled networks. The validation of the models was confirmed using the Mean Square Error (MSE) and Pearson's correlation coefficient. The cross-validation technique was used to re-evaluate the performance of neural networks. In short, neural networks can be used in practical operations due to the excellent correlations between the real values ​​and those calculated by the neural network.

Model Predictive Control of Molten Iron and Slag Quality Indices in a Large-scale Ironmaking Blast Furnace using a Hybrid Dynamic Model*

The stable operation and the optimal thermal control of industrial blast furnaces are challenging due to the complexity of the multi-phase and multi-scale physical and chemical phenomena, the presence of fast and extremely slow dynamics with latency periods of more than 8 hours, the absence of direct measurements of key inner variables, and the occurrence of a wide range of unknown disturbances. Industrial blast furnaces are still operated in a semi-automated manner and the quality of the control depends on the skills and dedication of the operators. Model-based control schemes, operated either in closed-loop or as advisory systems, are an obvious option to achieve a smooth and energetically efficient operation of blast furnaces. This work proposes a hybrid dynamic model-based optimizing control scheme for achieving the desired operational objectives by tightly controlling the hot metal silicon content ([Si]) and the slag basicity (SB) at their desired set-points. These two variables are key product quality indices and indicators of the internal thermal status of the blast furnace process. Within the proposed framework, the optimizer regulates the fast dynamics of the blast furnace to counteract the unmeasured disturbances that are caused by the variations in the solid feed, subject to operational constraints. Simulation results on a large-scale industrial blast furnace demonstrate the potential of the proposed approach for an improved operation of the blast furnace process.

Temporal Hypergraph Attention Network for Silicon Content Prediction in Blast Furnace

Online dynamic prediction of the hot metal silicon content in the blast furnace ironmaking process is crucial for stabilizing the furnace condition and improving the molten iron quality. However, due to the complex nonlinear correlations and time-varying time lags between silicon content and process variables, silicon content prediction is a challenging task. To tackle the problem, we propose a novel silicon content prediction method, called temporal hypergraph attention network (T-HyperGAT), which is combined the hypergraph attention network (HyperGAT) and the gated recurrent unit (GRU) network. Specifically, the Hyper-GAT is used to capture the high-order correlations of input features and perform equal-dimensional feature transformation to maintain the temporality of input features, and the GRU network is used to capture the time-series characteristics of transformed input features to overcome the time-varying time lags of process variables. Then the T-HyperGAT model can capture high-order correlations and time-series characteristics from complex industrial data. The effectiveness of the proposed T-HyperGAT method is verified by actual blast furnace ironmaking process data from a blast furnace in China.

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

The blast furnace is the principal method of producing cast iron. In the production of cast iron, the control of silicon is vital because this impurity is harmful to almost all steels. Artificial neural networks with Bayesian regularization are more robust than traditional back-propagation networks and can reduce or eliminate the need for tedious cross-validation. Bayesian regularization is a mathematical process that converts a nonlinear regression into a "well-posed" statistical problem in the manner of ridge regression. The main objective of this work was to develop an artificial neural network to predict silicon content in hot metal by varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, and 100 neurons. The results show that all neural networks converged and presented reliable results, neural networks with 20, 25, and 30 neurons showed the best overall results. However, In short, Bayesian neural networks can be used in practice because the actual values correlate excellently with the values calculated by the neural network.

Influence of process parameters, hot metal silicon content and slag properties on steel dephosphorization

During the process of dephosphorization, the main solid phases formed in slag are dicalcium silicate (2CaO.SiO2), tricalcium silicate (3CaO.SiO2) and tricalcium phosphate (3CaO.P2O5). The last one is the main responsible for phosphorous removal. The present paper aims to study the influence of temperature, chemical composition, slag properties (viscosity, liquid amount, activities of CaO and FeO) and hot metal silicon (Si) content in the efficiency of steel dephosphorization. Different industrial heats with different slags, hot metal and steel compositions, were selected to be analyzed. The mentioned slag properties and phases were determined by computational thermodynamics using the software FactSage and the results of these analyzes was related to the dephosphorization efficiency from the slags respective heats. Based on these simulations, the parameter Dephosphorization Factor (FDeP = aCaO + aFeO + (Nliquid – Nsolid) ∗ KDeP) was used to measure the slags efficiency in hot metal dephosphorization and several analyzes were made by correlation observation. The results show that CaO activity is the most important parameter on the dephosphorization, whereas hot metal with higher Si content presents less efficiency in dephosphorization, due to the higher SiO2 formation, which increase the amount of solid phases, in addition to decrease in basicity and CaO activity. It was found that FDeP presents a high correlation (R2) with the dephosphorization efficiency and can be used to predict which slag will be the most efficient. The temperature, in the analyzed range, does not seem to be a directly relevant factor in the DeP efficiency.

A hybrid dynamic model for the prediction of molten iron and slag quality indices of a large-scale blast furnace

The stable, economically optimal, and environmental-friendly operation of blast furnaces is still a challenge. Blast furnaces consume huge amounts of energy and are among the biggest sources of CO2 in the metal industry. The operation of industrial blast furnaces is challenging because of their sheer size, multi-phase and multi-scale physics and chemistry, slow dynamics with response times of 8 hours and more, and the lack of direct measurements of most of the important inner variables. Model-based schemes are prime candidates for providing the missing information and improving the operation. However, only recently, such schemes have been applied successfully, and there is still a lot of room for improvements. The spatial extension, the lack of precise mechanistic knowledge about the chemical and physical phenomena, and the presence of unmeasured disturbances make the application of first-principle models to process operations extremely challenging. In this work, a hybrid dynamic model is developed for the prediction of the hot metal silicon content and the slag basicity in the blast furnace process. These two variables are the key indicators of the internal process conditions, and the ultimate goal of our work is to control them by a model-based scheme. The core relationships between the process variables are imposed by a first-principles-based steady-state model, and a parallel data-based model represents the process dynamics and compensates for the deficiencies of the mechanistic model. Validation results for real plant measurements of a world-scale blast furnace show that the hybrid model is more accurate than the rigorous model and a stand-alone data-based model in long-term predictions of the dynamic behavior of the process.

Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system

Hot metal silicon content (HMSC) is usually utilized to measure the quality of hot metal and reflect the thermal status of blast furnace (BF) system. However, most state-of-the-arts ignore the time-varying behavior of BF ironmaking process, which are impractical. Accordingly, a novel dual ensemble online sequential extreme learning machine (DE-OS-ELM) is proposed to establish the online estimation model of HMSC, which can update the data-driven model with the latest operation data. Specifically, an online learning method with recursive modification is first proposed based on OS-ELM (referred to as RM-OS-ELM) to address the modeling with uncertainty. To heel, a dynamic forgetting factor is presented for the dynamic tracking capability enhancement and convergence acceleration. Furthermore, a final updating rule for sequential implementation is constructed by combining the output weights of OS-ELM and RM-OS-ELM based on their corresponding contributions on modeling. Considering the modeling accuracy and curve trend consistency, multiobjective parameter optimization model is also implemented to achieve the satisfactory performance. By taking the proposed DE-OS-ELM, the estimation model of HMSC is established using industrial data. Comprehensive experiments demonstrate that DE-OS-ELM-based HMSC estimation model is more feasible and practical.

Silicon content prediction of hot metal in blast furnace based on attention mechanism and CNN-IndRNN model

The stability of blast furnace temperature is an important condition to ensure the efficient production of hot metal. Accurate prediction of silicon content in hot metal is of great significance to the control of blast furnace temperature in iron and steel plants. At present, the accuracy of most silicon prediction models can only be guaranteed when the furnace condition is stable. However, due to many factors affecting the silicon content in hot metal of blast furnace, and there are large noises, large delays and large fluctuations in the data, the previous prediction results are of limited guiding significance to the actual production. In this paper, combined with the actual situation, the convolution neural network is used to extract the furnace condition characteristics, and then combined with the attention mechanism and the IndRNN model to get the prediction results, so that the prediction can better adapt to the fluctuating data set. The experimental results show that the prediction error of this model is lower than that of other models, which provides a new solution for the research of silicon content in hot metal of blast furnace.

Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN.

The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.

Pyrometallurgical processing of high-titaniferous ores.

The future development of Ural mineral and raw materials base of steel industry is considerably stipulated by the development of deposits of titanium-magnetite ores, the reserves of which are accounted for near 77% of iron ores of Urals. It was shown, that the content of titanium dioxide as well as harmful impurities in the titanium-magnetite have the decisive meaning for selection of processing technology of them for extraction out of them vanadium and other useful components. Technological schemes of the titanium-magnetite enrichment and industrial methods of titanium-magnetite concentrates processing considered. Examples of titanium-magnetite processing by coke-BF and coke-less schemes given. The problems of blast furnace melting of titanium-magnetite ores highlighted. Main problems relate to formation of refractory compounds in a form of carbo-nitrides during reduction of titanium and infusible masses in blast furnace hearth. It was shown, that intensification if carbides precipitation is stipulated by increase of intensity of titanium reduction at increased temperatures of a heat products and requires the BF heat to be run at minimal acceptable temperature mode. Technological solutions, necessary to implement in blast furnace for iron ore raw materials with increased content of titanium processing were presented, including increase of basicity of slag from 1.2 to 1.25-1.30, increase of pressure at the blast furnace top from 1.8 to 2.2 atm, decrease of silicon content in hot metal from 0.1 to 0.05%, application of manganese-containing additives. It was noted, that theoretically the blast furnace melting of titanium-magnetite is possible at titanium dioxide content in slag up to 40% when application of the abovementioned technological solutions, silicon content in hot metal to 0.01% and very stable heat conditions of a blast furnace. The actuality of titanium and its pigmental dioxide production increase was noted. Possibilities of development of Medvedevskoje and Kopanskoje deposits of high-titaniferous ores in Chelyabinsk region with extraction not only iron and vanadium but also titanium considered.