Endpoint Carbon Content Research Articles

Soft sensor model for endpoint carbon content and temperature in BOF steelmaking based on just-in-time learning with multilayer structure preservation and sparse information enhancement

Ensuring precise prediction of the endpoint carbon content and temperature is paramount in controlling the endpoint of the basic oxygen furnace (BOF) steelmaking process. However, due to the frequent fluctuations in real-time blowing conditions, the conventional just-in-time learning soft sensor approach encounters challenges in effectively anticipating the blowing endpoint. To address these aforementioned issues, this research introduces a novel approach known as the pattern-oriented multilayer structure preservation and sparse information enhancement model (POMLSP-SIE). This approach involves dynamically generating a dataset with the same distribution based on the characteristics of the query sample pattern. It is combined with a multi-pathway dimension reduction model to preserve multi-view spatial geometry information while reducing data dimension. The low-dimensional embedding serves as dictionaries containing various hierarchical structural characteristics. Significant information within these dictionaries is sparsely represented to amplify its influence during the regression prediction process while diminishing the impact of less relevant information. This refinement aims to rectify the problem of static regression models being weak to adapt to changing working conditions, ultimately enhancing prediction performance. The effectiveness of the proposed method is substantiated through an experimental study utilising real converter steelmaking process data, thereby confirming its practical applicability.

Dynamic Soft Sensor Model for Endpoint Carbon Content and Temperature in BOF Steelmaking Based on Adaptive Feature Matching Variational Autoencoder

The key to endpoint control in basic oxygen furnace (BOF) steelmaking lies in accurately predicting the endpoint carbon content and temperature. However, BOF steelmaking data are complex and change distribution due to variations in raw material batches, process adjustments, and equipment conditions, leading to concept drift and affecting model performance. In order to resolve these problems, this paper proposes a dynamic soft sensor model based on an adaptive feature matching variational autoencoder (VAE-AFM). Firstly, this paper innovatively proposes an adaptive feature matching (AFM) method. This method utilizes the maximum mean discrepancy to calculate the values of the marginal and conditional distributions. Based on the discrepancy between these two values, a dynamic adjustment algorithm is designed to adaptively assign different weights to the two distributions. This approach dynamically and quantitatively evaluates and adjusts the relative importance of different distributions in the domain adaptation process, thereby enhancing the effectiveness of cross-domain data alignment. Secondly, a variational autoencoder (VAE) is employed to process the data, as the VAE model can capture the complex data structures and latent features in the steelmaking process. Finally, the features extracted by the VAE are processed with the adaptive feature matching method, thereby constructing the VAE-AFM dynamic soft sensor model. Experimental studies on actual BOF steelmaking data validate the efficacy of the offered approach, offering a reliable solution to the challenges of high complexity and concept drift in BOF steelmaking data.

Ensemble learning soft sensor method of endpoint carbon content and temperature of BOF based on GCN embedding supervised ensemble clustering

Abstract The endpoint control of Basic Oxygen Furnace (BOF) steelmaking depends on the prediction of the endpoint carbon content and temperature. However, predicting these variables is challenging because of the numerous working conditions in the industrial field and the volatility of the sensor data collected during BOF steelmaking. The accuracy of prediction models in ensemble learning depends significantly on the initial distribution of data. However, the complex nature of BOF steelmaking data makes it challenging to generate diverse subsets, which ultimately affects the accuracy of predictions. This paper presents a new approach called Graph Convolutional Network Node Embedding Supervised Ensemble Clustering (GESupEC) for soft sensor modelling in ensemble learning to tackle these issues. GESupEC utilizes a similarity graph derived from a co-association matrix and employs graph convolutional networks to extract structural information among nodes. By optimising the clustering loss within the network, GESupEC learns compact node representations that are useful for the clustering task. Furthermore, it generates a reconstruction matrix based on the similarity of node embeddings. This matrix helps with the extraction of a suitable subset of data for BOF steelmaking through matrix decomposition. After that, the gradient boosting decision tree regression sub-model is established based on the data subset. An ensemble strategy called Gray Relational Analysis Weighted Average is proposed, which assigns weights based on the grey relation similarity between test samples and different data subsets. This weighted average strategy aims to enhance the accuracy of carbon content and temperature predictions. When tested with actual BOF steelmaking generation process data, the prediction accuracy of carbon content reached 88.6% within the error range of ±0.02%, and the prediction accuracy of temperature reached 92.6% within the error range of ±10 °C.

BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection

BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection

BOF steelmaking endpoint carbon content and temperature soft sensor based on supervised dual-branch DBN

Accurate prediction of endpoint carbon content and temperature is critical in the basic oxygen furnace (BOF) steelmaking process. Although deep learning soft sensor approaches have the capacity to extract abstract features from high-dimensional nonlinear steelmaking data, they confront the challenge of a low correlation between acquired features and labels. This work presents a BOF steelmaking soft sensor model based on supervised dual-branch deep belief network (SD-DBN) to address this issue. The SD-DBN model incorporates label information into the feature extraction process and fuses crucial feature information to complete the feature extraction in order to extract features that are closely connected to the target variables. First, the supervised Restricted Boltzmann Machine (RBM) is improved by using a pruning strategy to extract features that are highly correlated with quality information, and then the autocorrelation key feature extraction module is spliced and fused to form a dual-branch feature extraction module to improve key information extraction. Second, stacking the supervised dual-branch RBM modules to build a deep feature extraction network enhances the deep extraction capabilities of data features. This deep network stacking not only increases the impact of essential target data in hierarchical training, but it also acquires characteristics associated with the target variables.

Soft sensor method for endpoint carbon content and temperature of BOF based on multi-cluster dynamic adaptive selection ensemble learning

Abstract The accurate control of the endpoint in converter steelmaking is of great significance and value for energy saving, emission reduction, and steel quality improvement. The key to endpoint control lies in accurately predicting the carbon content and temperature. Converter steelmaking is a dynamic process with a large fluctuation of samples, and traditional ensemble learning methods ignore the differences among the query samples and use all the sub-models to predict. The different performances of each sub-model lead to the performance degradation of ensemble learning. To address this issue, we propose a soft sensor method based on multi-cluster dynamic adaptive selection (MC-DAS) ensemble learning for converter steelmaking endpoint carbon content and temperature prediction. First, to ensure the diversity of the ensemble learning base model, we propose a clustering algorithm with different data partition characteristics to construct a pool of diverse base models. Second, a model adaptive selection strategy is proposed, which involves constructing diverse similarity regions for individual query samples and assessing the model’s performance in these regions to identify the most suitable model and weight combination for each respective query sample. Compared with the traditional ensemble learning method, the simulation results of actual converter steelmaking process data show that the prediction accuracy of carbon content within ±0.02% error range reaches 92.8%, and temperature within ±10°C error range reaches 91.6%.

Soft sensor method of multimode BOF steelmaking endpoint carbon content and temperature based on vMF-WSAE dynamic deep learning

Abstract The difficulty of endpoint determination in basic oxygen furnace (BOF) steelmaking lies in achieving accurate real-time measurements of carbon content and temperature. For the characteristics of serious nonlinearity between process data, deep learning can perform excellent nonlinear feature representation for complex structural data. However, there is a process drift phenomenon in BOF steelmaking, and the existing deep learning-based soft sensor models cannot adapt to changes in the characteristics of samples, which may lead to their performance degradation. To deal with this problem, considering the characteristics of multimode distribution of process data, an adaptive updating deep learning model based on von-Mises Fisher (vMF) mixture model and weighted stacked autoencoder is proposed. First, the stacked autoencoder (SAE) and vMF mixture model are constructed for complex structural data, which can initially establish nonlinear mapping relationships and division of different distributions. Second, for each query sample, the basic SAE network will perform online adaptive fine-tuning according to its data with the same distribution to achieve dynamic updating. Moreover, each sample is assigned a weight according to its similarity with the query sample. Through the designed weighted loss function, the updated deep network will better match the working conditions of the query sample. Experimental studies with numerical examples and actual BOF steelmaking process data are provided to demonstrate the effectiveness of the proposed method.

Real-Time Prediction Model of Carbon Content in RH Process

In the Ruhrstahl–Heraeus (RH) vacuum degassing process, we propose a real-time prediction model for the carbon content in molten steel, and show that the decarburization endpoint can be accurately determined using this model. Firstly, we applied a novel off-gas analyzer that can measure the carbon oxide concentration produced in the decarburization reaction faster and more accurately. Next, we generate decarburization curves using the off-gas components measured by the new analyzer. The decarburization curve describes the carbon content profile well during operation, and shows good agreement with the actual carbon content. In order to predict the carbon content during operation in real time, we create an artificial neural network (ANN) using the decarburization curves and operation data. By comparing the endpoint carbon content measured at the end of the operation with the predicted values, we confirmed the excellent predictive performance of the ANN model. Finally, we show that it is possible to accurately determine the decarburization endpoint using the prediction model. We expect that the proposed real-time prediction model can increase the productivity of the RH process.

Just-in-time updating soft sensor model of endpoint carbon content and temperature in BOF steelmaking based on deep residual supervised autoencoder

The key of Basic oxygen furnace (BOF) endpoint control is to achieve accurate forecast of the endpoint carbon content and temperature. Due to the highly nonlinear and complex distribution characteristics of process data, the predictions of these two indicators are inaccurate. Although deep learning can extract abstract features of complex data, existing methods are pre-trained in an unsupervised manner. For soft sensor applications, the key is to highlight important information and extract features related to the target variables for prediction. Thus, this paper proposes a feature extraction model based on deep residual supervised autoencoder (DRSupAE). By incorporating important information in the feature of previous hidden layers into deeper feature learning in the form of residual connection, and each residual supervised autoencoder (RSupAE) utilizes the target variables to guide feature extraction. In this way, the stacking of new deep networks not only enhances the impact of important target-related information in layer-wise training, but also captures features related to target variables. Moreover, for the time-varying phenomenon of the BOF steelmaking process, a strategy of just-in-time updating regression network (JITRN) is proposed, which is used to solve the problem that the static regression model cannot adapt to the frequent changes of sample characteristics and causes the prediction performance to decline. An experimental study on BOF steelmaking process data is provided to demonstrate the effectiveness of the proposed method.

Influence Factor Analysis and Prediction Model of End-Point Carbon Content Based on Artificial Neural Network in Electric Arc Furnace Steelmaking Process

In this manuscript, we consider the accuracy of end-point carbon content prediction affected by oxygen injection in multiple stages of electric arc furnace (EAF) melting process. Such a prediction would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. Principal component analysis (PCA) was used to normalize the 13 input variables affecting the endpoint carbon content. log-sigmoid and tan-sigmoid functions were used to verify the same sample, and it was found that the Mean squared error(MSE) of the model under logsig + logsig function was smaller, indicating that the model was more stable. At the same time, different hidden layer nodes were tried, and finally the structure of the model was determined as 13 × 10 × 8 × 1, and the activation function was logsig + logsig. Using historical smelting data to train and test the neural network model, the correlation coefficient (R) of the verified model is 0.7632, the model prediction is in the range of ±0.03%, the hit rate of the model is 64.5%, and the hit rate of the model is 42% in the range of ±0.02%. Combining the verification basis of the model with the metallurgical reaction principle of the EAF steelmaking process, a pretreatment method of phased input of total oxygen is proposed. The oxygen is divided into three stages, which are the oxygen consumption volume of 0–5 min, 5–30 min and more than 30 min and other variables are kept unchanged. The same neural network is used to train and verify the same data. After verification, the R of the oxygen staged model is 0.8274. The model prediction is in the range of ±0.03%, the hit rate of the model is 78.5%, and the hit rate of the model is 58% in the range of ±0.02%. Finally, an on-line carbon content prediction system based on artificial neural network model is developed and applied to actual production. Running results illustrated that the hit rate of end-point carbon content is 96.67%, 93.33% and 86.67%, respectively when the prediction errors are within ±0.05%, ±0.03% and ±0.01%, the improved neural network model can effectively predict the end-point carbon content, which provides a good basis for the carbon content at the end point of EAF steelmaking process.

Thermal Effect and Metallurgical Characteristics of Hydrogen Bottom Blowing in Top–Bottom Combined Blowing Converter

A new technology of hydrogen bottom blowing instead of traditional argon blowing in the current converter steelmaking process is proposed herein, in the aim of overcoming problems such as energy shortages caused by increased scrap charging, the low stirring intensity of bottom blowing, high CO2 emissions, and endpoint carbon content control. The thermal effect and metallurgical characteristics of hydrogen bottom blowing were investigated based on the production data of a steelmaking converter in Pangang Group Xichang Steel & Vanadium Co., Ltd. This study shows that hydrogen bottom blowing at an intensity of 0.1–0.5 m3·min−1·t−1—rather than argon blowing at an intensity of 0.1 m3·min−1·t−1—can increase the smelting temperature by 16–73 K, increase the scrap charging ratio by 0.89–5.19%, and reduce CO2 emissions by 19.79–115.96 kg per ton of steel. Intensive hydrogen blowing could significantly reduce the oxygen content of molten steel in the late stage of steelmaking and be beneficial to controlling oxygen at the endpoint. Hydrogen can also reduce the (FeO) content in slag, and the equilibrium partial pressure ratios of H2O/H2 for the reaction H2 + (FeO) = H2O + Fe in the middle and late periods are 0.41 and 0.11, respectively. Hydrogen can also slightly suppress the decarbonization reaction in the late period of steelmaking, and the equilibrium partial pressure ratio of H2O/H2 for the reaction H2 + (CO) = [C] + H2O in the late period is 9.65 × 10−2, which means that hydrogen is beneficial in preventing the rapid decrease in [C] and, in turn, helps control the endpoint carbon content. By comparing the degree of the reaction (P2O5) + 5H2 = P2(g) + 5H2O and the reaction (P2O5) + 5H2 = 2[P] + 5H2O, it can be seen that intensive bottom-blown hydrogen may have a slight positive effect on slag gasification dephosphorization. The FactSage simulation results further verify the conclusions of the above analysis.

Industrial cyber-physical system driven intelligent prediction model for converter end carbon content in steelmaking plants

Industry 4.0 provides an opportunity for realizing converter intelligent steelmaking. Based on a cyber-physical system (CPS) framework for steelmaking plants, we developed a non-contact intelligent prediction model for determining molten steel carbon content. We achieved this by leveraging big data in-depth learning and optical information from converter mouth flame characteristics to realize accurate control of end-point carbon content in the process of intelligent steelmaking. The spectral information of a converter port flame was gathered using a USB2000+ spectrometer, and its main characteristics were extracted via factor analysis. The sampling frequency of the converter flue gas analysis mass spectrometer was set to obtain the carbon content of continuous molten steel during the late stages of steelmaking consistent with the spectral information collection frequency. A large sample dataset was constructed to continuously predict the carbon content of the molten steel based on the flame spectrum information of the furnace orifice. Furthermore, the parameters of a support vector regression algorithm were optimized, and the quantitative relationships among the sample data were intelligently mined to obtain a dynamic prediction model of the carbon content of 150 furnace samples. To improve the universality of the model, the initial steelmaking conditions were applied as the optimization criteria of the carbon content prediction model generated by matching different furnace samples. A support vector machine algorithm was applied to construct a model optimization classifier to obtain a universal prediction model. The results indicate that in the late stages of smelting, the spectral information of the flame in the furnace exhibited a high correlation with the carbon content of the molten steel. After testing, the prediction accuracy of this model was more than 90%. With the help of big data deep learning and CPS framework, we can build robust and accurate steelmaking intelligent control systems.

Improved multivariable reasoning control method for carbon content control of argon–oxygen refining ferrochrome

In order to control the end-point carbon content in the process of argon–oxygen refining ferrochromium alloys, according to the mechanism model of the oxygen supply rate and carbon content variation, the mechanism model of the argon supply rate and carbon content variation, and the mechanism model of the supply rate of the two gases and molten iron temperature variation, this paper proposes an improved multivariable inference control system and uses expert control to realize the automatic adjustment of the filter time constant in the multivariable reasoning controller. The simulation results show that this method can ensure the stability of the control system without errors and maintain the furnace temperature below the maximum withstanding temperature, further improving the dynamic response of the system and effectively shortening the smelting time of the entire argon–oxygen refining ferrochromium alloy. It is beneficial for improving smelting efficiency and prolonging furnace service.

Continuous Prediction Model of Carbon Content in 120 t Converter Blowing Process

A continuous prediction model of carbon content of 120 t BOF is established in this paper. Based on the three-stage decarburization theory and combined with the production process of 120 t converter, the effects of oxygen lance height and top blowing oxygen flow rate are also considered in the model. The explicit finite difference method is used to realize continuous prediction of carbon content in the converter blowing process. The model parameters such as ultimate carbon content in molten pool are calculated according to the actual data of 120 t BOF, which improves the hit rate of the model. Process verification and end-point verification for the continuous prediction model have been carried out, and the results of process verification indicate that the continuous prediction model established in the paper basically accords with the actual behavior of decarburization. Moreover, the hit ratio of the continuous prediction model reached 85% for the prediction of end-point carbon content within a tolerance of ±0.02%.

Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.

Just-in-time-learning based prediction model of BOF endpoint carbon content and temperature via vMF mixture model and weighted extreme learning machine

Basic oxygen furnace (BOF) steelmaking is a complicated physical chemical process, in which the endpoint carbon content and temperature are two important indicators. In BOF steelmaking, the quality of raw materials varies greatly between different batches, which would lead to the inaccurate predictions for these two indicators. Additionally, there are imbalance problems in production process data of BOF steelmaking. For the time-varying problem, a novel similarity criterion based on von-Mises Fisher mixture model (VMM) is proposed in this paper and applied for sample selection of just-in-time-learning (JITL)-based endpoint carbon content and temperature prediction model. The V-shaped transfer function is utilized to develop weighted extreme learning machine (WELM) as local regression model to address the imbalance problems. The performance of the proposed methods is compared with other methods under JITL framework. The experimental results show that the proposed online model can provide a more accurate prediction.

A review of end-point carbon prediction for BOF steelmaking process

AbstractThe precise prediction of end-point carbon content in liquid steel plays a critical role in increasing productivity as well as energy efficiency that can be achieved in the basic oxygen furnace (BOF) steelmaking process. Due to numerous and diversity of the studies on BOF end-point carbon prediction, it seems necessary to provide a comprehensive literature review on state-of-the-art developments in end-point carbon prediction for BOF steelmaking. This paper presents the characteristics of different end-point carbon prediction models. The end-point carbon prediction for BOF steelmaking has initially relied on the experience and skill of the operators. With the development of information technology and auto-detection methods, BOF end-point carbon prediction mainly has gone through three stages, such as static prediction, dynamic prediction, and intelligent prediction. Future contributions to the development and application of intelligent end-point carbon prediction in BOF steelmaking are still arduous tasks. However, it is envisaged that the intelligent end-point carbon prediction will witness more frequent applications and greatly improve the high-quality, high-efficiency, and stable production for BOF steelmaking in the future.

End-point dynamic control of basic oxygen furnace steelmaking based on improved unconstrained twin support vector regression

In order to improve the end-point hit rate of basic oxygen furnace steelmaking, a novel dynamic control model was proposed based on an improved twin support vector regression algorithm. The controlled objects were the end-point carbon content and temperature. The proposed control model was established by using the low carbon steel samples collected from a steel plant, which consists of two prediction models, a preprocess model, two regulation units, a controller and a basic oxygen furnace. The test results of 100 heats show that the prediction models can achieve a double hit rate of 90% within the error bound of 0.005 wt.% C and 15 °C. The preprocess model was used to predict an initial end-blow oxygen volume. However, the double hit rate of the carbon content and temperature only achieves 65%. Then, the oxygen volume and coolant additions were adjusted by the regulation units to improve the hit rate. Finally, the double hit rate after the regulation is reached up to 90%. The results indicate that the proposed dynamic control model is efficient to guide the real production for low carbon steel, and the modeling method is also suitable for the applications of other steel grades.

A modified mathematical model for end-point carbon prediction of BOF based on off-gas analysis

Several models for end-point carbon prediction of BOF(Basic Oxygen Furnace) based on off-gas analysis were studied in this paper. The advantages and disadvantages of the integral model, the exponential decay model and the cubic fitting model were analyzed respectively. Based on analysis of the characteristics of the decarburization rate curve, a new exponential model was established by the introduction of a correction algorithm. The principle of the proposed model involves applying the decarburization rate curve and the descending gradient of the historical heats to obtain the average decarburization curve and reference decarburization efficiency coefficient using the regression fitting method. According to the deviation between the actual and the predicted decarburization curves, the decarburization efficiency coefficient was corrected to improve the prediction accuracy. Plant trials were carried out in a 210 t converter to compare the performance of the mentioned models. The results showed that the new model exhibited better adaptability and higher accuracy than the other ones. The hit ratio of the new model reached more than 90% for the prediction of end-point carbon content within a tolerance of ±0.02%.

The research on intelligent extraction of furnace mouth flame characteristics based on DNN

Deep neural networks are a focus of artificial intelligence and big data analysis in recent years. The monitor of the converter mouth is essential to the quality of the steel material production while the requirement of the steel material production is increasingly higher in China. The end-point control of converter blowing is the ultimate regulation of the carbon content and temperature. The severity of carbon-oxygen reaction and the temperature of molten steel can be reflected by the converter mouth flame. Operators judge the end of the steel by watching the converter mouth flame, the converter mouth spark and the time of oxygen supply. So, it is very important to offer a quantitative analysis to converter mouth flame characteristics. We quote the deep neural network into the intelligent extraction of the flame characteristics of the furnace mouth and construct a flame color recognition algorithm based on the deepness letter neural network. This paper belongs to the data science problem in the intelligent research of steel production. By observing the converter flame during the steel flame changes, this paper records the data of light intensity and end-point carbon content of each steel making furnace. When this paper then uses the temperature of flame emission spectrum to deduce and the absorption of the molten steel to judge the contents of the carbon during the converter steel blew process, it is more feasible and accurate than watching by operators. At the same time, by using deep learning algorithm, this paper makes the control process get automatic learning ability and achieve intelligent production so that we can provide a basis for solving the problem of predicting the end-point carbon content in molten steel during the blowing process.