Burn-through Point Research Articles

Prediction model of burn-through point with data correction based on feature matching of cross-section frame at discharge end

Accurately predicting the burn-through point (BTP) is crucial for achieving stable control of the sintering process. However, accurately measuring the raw BTP is difficult due to the harsh production environment and poor thermocouple measurement accuracy of the temperature of exhaust gas in bellows. This paper proposes a prediction model of the BTP with data correction based on the feature matching of cross-section frame at discharge end. Firstly, a feature extraction method of cross-section frames at discharge end is designed. Next, the cross-section frame at discharge end features matching method is used to correct the raw BTP, and this method corrects anomalous data resulting from sensor failures. Finally, the temporal convolutional neural network and gated recurrent unit are used to predict the corrected BTP. The prediction model considers the cross-section frame feature at discharge end and state parameters as inputs, and it can achieve accurate prediction of the corrected BTP. A series of comparative experiments are conducted to verify the feasibility and effectiveness of the proposed model. At the same time, this paper also designs industrial implementation plan,and use actual operation data to verify the feasibility of the designed industrial implementation plan.

An adaptive control system based on spatial–temporal graph convolutional and disentangled baseline-volatility prediction of bellows temperature for iron ore sintering process

The temperature within the sintering furnace is a decisive factor influencing the quality of the sintered ore in the iron ore sintering process. In practical operations, the temperature at the bellows directly linked to the bed layer indirectly signifies the internal furnace temperature. Maintaining a stable temperature at the bellows, particularly at the burn-through point, is vital for minimizing gas emissions, improving carbon efficiency, and enhancing the quality of the sintered ore. This paper proposes an intelligent temperature control system based on Spatial–Temporal Graph Convolutional and Disentangled Baseline-Volatility (STGCDBV) prediction. The STGCDBV network comprises three parallel modules: Adaptive Graph Convolution Network (AGCN), Baseline and Volatility Disentangler (BVD), and a residual link, along with a Temporal–Nodal Encoder–Decoder (TNED) module. The AGCN constructs a graph reflecting the characteristics of bellows temperature, effectively merging static spatial data with dynamic thermal information. The BVD module captures the nonlinear trend data inherent in the sintering process. In contrast, the TNED synergizes the insights from the parallel modules using cross encoding and decoding functionalities. For controlling the sintering gas flow rate, a Model Reference Adaptive Control (MRAC) system is implemented, which utilizes a control scheme founded on a temperature reference model and iterative parameter adjustments. Extensive experiments using actual time-series data from a steel plant have been conducted. Moreover, comparisons between the performance of pre- and post-control interventions demonstrate that the STGCDBV-MRAC system can stabilize temperature fluctuations and exhibit exemplary control proficiency.

Soft-Sensing of Burn-Through Point Based on Weighted Kernel Just-in-Time Learning and Fuzzy Broad-Learning System in Sintering Process

Soft-Sensing of Burn-Through Point Based on Weighted Kernel Just-in-Time Learning and Fuzzy Broad-Learning System in Sintering Process

A 3-D Convolution-Based Burn-Through Point Multistep Prediction Model for Sintering Process

Predicting burn-through point (BTP) in advance is a quite critical task for the sintering process. However, sintering is a complex physicochemical reaction process, and the strong spatial-temporal correlations of data make the multi-step prediction task very challenging. The previous BTP multi-step prediction model only extracts spatial features in the high-level layers, leaving the spatial features in the low-level layers not learned. Specifically, the previous model only considers the relationships between the process variables and BTP, ignoring the spatial coupling relations among process variables. Further, the existing loss function is mainly based on Euclidean distance, which cannot learn dynamic information of multi-step prediction sequence. To tackle these problems, in this study, we propose a 3D convolution-based BTP multi-step prediction model (CBMP) to simultaneously capture spatial-temporal features. First, the 3D convolution is employed to capture the spatial-temporal features from low-level to high-level layers at the same time. Secondly, a spatial-temporal recalibration block is proposed to further refine the extracted features to increase the contributions of informative features and suppress the less useful ones. Finally, we design a time-aware multi-step prediction loss function to dynamically weigh the similarity between the actual sequence and the predicted sequence. The experimental results on two real-world BTP datasets demonstrate the effectiveness and feasibility of the proposed model on the BTP multi-step prediction task.

BTPNet: A Probabilistic Spatial-Temporal Aware Network for Burn-Through Point Multistep Prediction in Sintering Process.

Burn-through point (BTP) is a very key factor in maintaining the normal operation of the sintering process, which guarantees the yield and quality of sinter ore. Due to the characteristics of time-varying and multivariable coupling in the actual sintering process, it is difficult for traditional soft-sensor models to extract spatial-temporal features and reduce multistep prediction error accumulation. To address these issues, in this study, we propose a probabilistic spatial-temporal aware network, called BTPNet, which is used to extract spatial-temporal feature for accurate BTP multistep prediction. The BTPNet model consists of two parts: an encoder network and a decoder network. In the encoder network, the multichannel temporal convolutional network (MTCN) is employed to extract the temporal features. Meanwhile, we also propose a novel architectural unit called variables interaction-aware module (VIAM) to extract the spatial features. In the decoder network, to reduce the accumulated errors of the last step prediction, a probabilistic estimation (PE) method is proposed to improve the performance of multistep prediction. Finally, the experimental results on a real sintering process demonstrate the proposed BTPNet model outperforms state-of-the-art multistep prediction models.

EnvFormer: A Decomposition-Based Transformer for Multistep Burn-Through Point Prediction in the Sintering Process

EnvFormer: A Decomposition-Based Transformer for Multistep Burn-Through Point Prediction in the Sintering Process

DSTED: A Denoising Spatial–Temporal Encoder–Decoder Framework for Multistep Prediction of Burn-Through Point in Sintering Process

Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a direct influence on the yield, quality, and energy consumption of the ironmaking process. Since iron ore sintering is a very complex industrial process with strong nonlinearity, multivariable coupling, random noises, and time variation, traditional soft-sensor models are hard to learn the knowledge of the sintering process. In this article, a multistep prediction model, called denoising spatial–temporal encoder–decoder, is developed to predict BTP in advance. First, the mechanism analysis is carried out to determine the relevant-BTP variables, and the BTP prediction is defined as a sequence-to-sequence modeling problem. Second, motivated by the random noises of industrial data, a denoising gated recurrent unit (DGRU) is designed to alleviate the impact of noise by adding a denoising gate into the GRU. In this case, the encoder with DGRU can better extract the latent variables of original sequence data. Then, spatial–temporal attention is embedded into the decoder to simultaneously capture the time-wise and variable-wise correlations between the latent variables and the target variable BTP. Finally, the experimental results on the real-world dataset of a sintering process demonstrated that the integrated multistep prediction model is effective and feasible.

Operating Performance Improvement Based on Prediction and Grade Assessment for Sintering Process.

Sintering is the preproduction process of ironmaking, whose products are the basis of ironmaking. How to improve the operating performance of the iron ore sintering process has always been a problem that operators are committed to solve. An operating performance improvement method based on prediction and grade assessment is presented in this article. First, considering the data distribution characteristics of the process, a performance index prediction model based on the Gaussian process regression is built, in which the mutual information analysis method is used to select the inputs of the performance index prediction model. Then, the operating performance grade is assessed by a threshold division method. Next, the operating performance grade guides the control of the burn-through point to improve the operating performance. Finally, experimental verification is performed based on the actual running data. The results show that the proposed method has high prediction accuracy, and it is also significant in improving the operating performance. Therefore, this approach provides an effective solution to predict and improve operating performance.

Prediction model of burn-through point with fuzzy time series for iron ore sintering process

Burn-through point (BTP) is an essential parameter in the iron ore sintering process. Operators usually judge whether the current production is stable by monitoring the BTP. It comes with significant application prospects to predict the BTP accurately. A prediction model of the BTP with fuzzy time series is designed in this paper. First, the fuzzy time series prediction method with the Fuzzy C-Means clustering is presented as the core modeling method. A prediction model of the response is constructed to obtain a timely response to the current BTP. The prediction model of the difference is established to estimate the present unmeasurable disturbance on the BTP. Then, a hybrid prediction model is built, which realizes the composition of these two models by an adjustment factor. Finally, a series of experiments is carried out using the raw time series data from an iron and steel plant. The experimental result shows that the designed model has better prediction performance for the BTP than existing models, which is an advantage resulting from the hybrid structure and the fuzzy time series prediction model with the Fuzzy C-Means clustering. This prediction model of the BTP implies the foundation for the stable control of the iron ore sintering process.

Operating Mode Recognition Based on Fluctuation Interval Prediction for Iron Ore Sintering Process

The operating mode is an essential factor affecting product quality and yield of the sinter ore, which inspires the realization of operating mode recognition. Taking burn-through point (BTP) as the decision parameter of operating mode, an operating mode recognition method based on the fluctuation interval prediction is presented. First, combining the principal component analysis and the fuzzy information granulation method, a fluctuation interval prediction model of the BTP is established through utilizing the Elman neural network. Then, the operating mode classification rules are built according to the data distribution of the BTP in the fluctuation interval. Finally, experiments are executed with the data collected from a factory. The results indicate that it can effectively predict the fluctuation interval of the BTP, and then successfully recognize the operating mode. In this article, the proposed method provides a valid reference to control the stable operation of the iron ore sintering process.

Application of grey system theory to phosphorite sinter process: From modeling to control

AbstractThe sintering process of phosphorite ore occurs with a large amount of return caused by untimely process control. The control task of phosphorite ore sintering is to regulate the parameters of the process to obtain a high quality sinter. The parameter clearly responsible for sinter quality is the temperature in the wind box. Therefore, in order to solve the control task, it is necessary to predict the highest temperature of the charge (also known as the burn through point (BTP)). In this paper, the theory of grey systems is used as a predictive model, which makes it possible to obtain an adequate model that uses a small number of initial samples of real temperature data. Based on the grey model GMC(1,n) a new optimal model is presented, which is constructed by using optimization algorithm. Optimal model predicts the BTP, and to establish an optimal regulation, a control synthesis is carried out through an optimization of the prediction according to the “particle swarm” algorithm.

A Fuzzy Control Strategy of Burn-Through Point Based on the Feature Extraction of Time-Series Trend for Iron Ore Sintering Process

Sinter ore is the main raw material for ironmaking, and burn-through point (BTP) is one of the significant factors to measure the stability of the sintering process. In this article, through the feature extraction of time-series trend, a fuzzy control strategy is presented for the BTP. First, the Hurst exponent of the time series for the BTP is calculated by resorting to the rescaled range analysis method, by which the trend feature is analyzed. Then, by using the Mann–Kendall test, both global and local trend feature variable of the time series for the BTP are extracted and regarded as the inputs of the fuzzy controller. Next, a fuzzy controller for the BTP is designed to produce the control quantity of the strand velocity. Finally, based on a semiphysical simulation system and the raw data collected from an iron and steel plant, an experiment is carried out to demonstrate the effectiveness of the proposed control strategy.

Intelligent Integrated Control for Burn-Through Point to Carbon Efficiency Optimization in Iron Ore Sintering Process

The iron ore sintering process is an important step in preparing raw material for ironmaking. How to reduce carbon consumption while ensuring the stable running of the sintering process is an urgent problem to be solved. In this brief, an intelligent integrated control strategy for the burn-through point (BTP) to carbon efficiency optimization in the sintering process is presented. The comprehensive coke ratio (CCR) is employed as a measure of carbon efficiency, and the BTP is a measure of the stability of the sintering process. First, a short time scale model is established to predict the CCR, and the carbon efficiency is optimized by using the particle swarm optimization algorithm. This yields an optimal carbon efficiency and one control quantity of strand velocity. Another control quantity of strand velocity is obtained by a BTP expert-fuzzy controller. Both control quantities are integrated by a well-designed intelligent integrated controller, so that the optimal strand velocity, as the final control input, is determined. An experiment is carried out to verify the effectiveness of the proposed strategy. The experimental results show that the proposed strategy improves the carbon efficiency while ensuring the stable running of the sintering process, which has a good application prospect in the industrial site.

A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters.

As energy efficiency becomes increasingly important to the steel industry, the iron ore sintering process is attracting more attention since it consumes the second large amount of energy in the iron and steel making processes. The present work aims to propose a prediction model for the iron ore sintering characters. A hybrid ensemble model combined the extreme learning machine (ELM) with an improved AdaBoost.RT algorithm is developed for regression problem. First, the factors that affect solid fuel consumption, gas fuel consumption, burn-through point (BTP), and tumbler index (TI) are ranked according to the attributes weightiness sequence by applying the RReliefF method. Second, the ELM network is selected as an ensemble predictor due to its fast learning speed and good generalization performance. Third, an improved AdaBoost.RT is established to overcome the limitation of conventional AdaBoost.RT by dynamically self-adjusting the threshold value. Then, an ensemble ELM is employed by using the improved AdaBoost.RT for better precision than individual predictor. Finally, this hybrid ensemble model is applied to predict the iron ore sintering characters by production data from No. 4 sintering machine in Baosteel. The results obtained show that the proposed model is effective and feasible for the practical sintering process. In addition, through analyzing the first superior factors, the energy efficiency and sinter quality could be obviously improved.

A dynamic subspace model for predicting burn-through point in iron sintering process

This paper presents a dynamic modeling method for predicting the exhaust-gas temperature (EGT) of the burn-through point (BTP) in an iron sintering process. First, a subspace modeling method is used to build a steady-state subspace model (SSSM) for the EGT at a steady state. Then, a dynamic subspace model (DSM) that is driven by the errors of the SSSMs is developed to improve the accuracy of the EGT prediction in a continuous process. Finally, a grid search dynamic subspace model (GSDSM) is established to find the best parameters for each SSSM in the DSM. Verification results show that the GSDSM yields a predicted EGT with a high precision, which can be implemented in a predicting controller an actual sintering process.

T–S Fuzzy Logic Based Modeling and Robust Control for Burning-Through Point in Sintering Process

Burning-through point (BTP) control is the key to guranteeing the sintering process and sinter quality. Due to an opening combustion and multiple input conditions, the combustion process of the sintering process shows a typical characteristic of time-varying operation conditions. This paper analyzes the influence factors resulting in time-varying operation conditions and then constructs an improved Takagi–Sugeno (T–S) fuzzy model of BTP based on the linear parameter varying (LPV-TS-BTP model), in which the BTP temperature and the central bellows temperature are taken as time-varying parameters. The model explains how the different working conditions change the systems dynamics and describes this time-varying nonlinear model in the form of a linear combination model. Then, a fuzzy robust control method based on the LPV-TS-BTP model is designed by using the parallel distributed compensation principle to realize the BTP control of complex process, in which the controller parameters are achieved by using the linear matrix inequality (LMI) method. The simulations about modeling and control, which are carried out on a semiphysical simulation platform for the sintering process, verify that the model has a high prediction accuracy, and the fuzzy robust control makes BTP stable around a given bellows.

Intelligent Coordinating Control Between Burn-Through Point and Mixture Bunker Level in an Iron Ore Sintering Process

Sintering is a process that involves complex physical and chemical reactions. An intelligent coordinating control strategy is proposed for the strong coupling between the burn-through point (BTP) and the mixture bunker level (MBL). First, an intelligent integrated controller is established for the BTP by fusing the neural network, expert rules, and fuzzy logic. Moreover, an expert controller is designed for the MBL based on expert rules using the analysis of the main factors that affect the MBL. Furthermore, by employing the soft switching control algorithm, an intelligent coordinating controller for the BTP and the MBL is designed. The optimal operation parameters are obtained from the algorithm, which realize the multi-objective control of the sintering process. Finally, a simulation and an experiment of the intelligent coordinating control between the BTP and the MBL are carried out, where the models of the BTP and the MBL are the Takagi-Sugeno (T-S) fuzzy model and the linear model, respectively. And the results show that the proposed approach is feasible and effective.

Modeling NOx emission of coke combustion in iron ore sintering process and its experimental validation

NOx emission of coke combustion in iron ore sintering was modeled by overall reaction rate equations of NOx formation and reduction. Incorporating into a previous sintering heat treatment model, overall NOx emission can be predicted and the simulated results were well agreed with four sinter pot tests under varying conditions which are similar to actual production. In sintering, NOx emission is significantly related to fuel combustion. Due to heat input by ignition and smaller airflow in the initial stage of sintering, the predicted NOx emission has a higher value of about 350ppm first then it decreases a little and keeps at a relatively constant level of about 300ppm until the burn-through point, and decreases rapidly as a result of the accomplishment of coke combustion. Simulation results indicate that fuel NOx is the main NOx emission in sintering while thermal NOx is rarely produced since the bed temperature is much lower than 1800K. The generated NOx could be reduced not only on the surface and in the pores of coke but also by CO around coke particles, about 50% and 10% of the generated NOx could be reduced by char and CO, respectively. Increasing coke rate and decreasing coke size promote NOx generation by accelerating the coke combustion. The reduction extent by char is greatly influenced by contact between NOx and char while the reduction extent by CO is mainly determined by the combustion atmosphere.

Subspace Modeling Method for Burn-Through Point

In the study, for the iron sintering process which is strongly time-delayed and confounding, a data-driven modeling method called subspace modeling method is proposed to predict the burn-through point (BTP) in this paper. First, by analyzing the mechanism of the sintering process and processing the industrial data, the relationship between the exhaust gas temperature and bellows is confirmed. Then, based on the position of the BTP, a subspace modeling method is used to establish a temperature identification model. This model outputs, the temperature of the BTP and its inputs include the exhaust gas temperature of the front three bellows and the operating parameters. Finally, we compare the obtained result with those of other algorithms. The simulation experiment shows the effectiveness and veracity of the subspace model for BTP, which will provide a precise model for controller design.

Monitoring Sintering Burn-Through Point Using Infrared Thermography

Sintering is a complex industrial process that applies heat to fine particles of iron ore and other materials to produce sinter, a solidified porous material used in blast furnaces. The sintering process needs to be carefully adjusted, so that the combustion zone reaches the bottom of the material just before the discharge end. This is known as the burn-through point. Many different parameters need to be finely tuned, including the speed and the quantities of the materials mixed. However, in order to achieve good results, sintering control requires precise feedback to adjust these parameters. This work presents a sensor to monitor the sintering burn-through point based on infrared thermography. The proposed procedure is based on the acquisition of infrared images at the end of the sintering process. At this position, infrared images contain the cross-section temperatures of the mixture. The objective of this work is to process this information to extract relevant features about the sintering process. The proposed procedure is based on four steps: key frame detection, region of interest detection, segmentation and feature extraction. The results indicate that the proposed procedure is very robust and reliable, providing features that can be used effectively to control the sintering process.