Breakout Prediction Research Articles

Investigation on Mold Breakout Prediction Methods Based on Image Recognition via Convolutional Neural Network

Investigation on Mold Breakout Prediction Methods Based on Image Recognition via Convolutional Neural Network

Research on LFFA-BP neural network model in breakout prediction

Research on LFFA-BP neural network model in breakout prediction

Breakout Prediction Based on Twin Support Vector Machine of Improved Whale Optimization Algorithm

Breakout Prediction Based on Twin Support Vector Machine of Improved Whale Optimization Algorithm

An Intelligent Logic-Based Mold Breakout Prediction System Algorithm for the Continuous Casting Process of Steel: A Novel Study.

Mold breakout is one of the significant problems in a continuous casting machine (caster). It represents one of the key areas within the steel production facilities of a steel plant. A breakout event on a caster will always cause safety hazards, high repair costs, loss of production, and shutdown of the caster for a short while. In this paper, a logic-judgment-based mold breakout prediction system has been developed for a continuous casting machine. This system developed new algorithms to detect the different sticker behaviors. With more algorithms running, each algorithm is more specialized in the other behaviors of stickers. This new logic-based breakout prediction system (BOPS) not only detects sticker breakouts but also detects breakouts that takes place due to variations in casting speed, mold level fluctuation, and taper/mold problems. This system also finds the exact location of the breakout in the mold and reduces the number of false alarms. The task of the system is to recognize a sticker and prevent a breakout. Moreover, the breakout prediction system uses an online thermal map of the mold for process visualization and assisting breakout prediction. This is done by alerting the operating staff or automatically reducing the cast speed according to the location of alarmed thermocouples, the type of steel, the tundish temperature, and the size of the cold slab width. By applying the proposed model in an actual steel plant, field application results show that it could timely detect all 13 breakouts with a detection ratio of 100%, and the frequency of false alarms was less than 0.056% times/heat. It has the additional advantage of not needing a lot of learning data, as most neural networks do. Thus, this new logical BOPS system should not only detect the sticker breakouts but also detect breakouts taking place due to variations in casting speed and mold level fluctuation.

Application of GA-ACO Algorithm in Thin Slab Continuous Casting Breakout Prediction

Application of GA-ACO Algorithm in Thin Slab Continuous Casting Breakout Prediction

Recognition of sticker breakout in mould thermograph based on template matching

ABSTRACT Breakout is a serious accident during continuous casting. Sticker is the main type of breakout accidents. The propagation behaviour of sticker breakout in mould is the key to recognizing and preventing breakout accident. A visual monitoring system of breakout has been developed to capture sticker characteristics, which usually consist of hot and cold regions. According to the propagation behaviour of sticker breakouts, four templates were selected with a Euclidean distance algorithm, such as typical, long-span, left one-sided and right one-sided templates. Then, the template matching algorithm was applied to identify the true and false sticker breakouts. The results show that 120 cases of true and false stickers can be distinguished. The recognition rate of true sticker is 100%. There is no false sticker to be determined as a true sticker. This method is not only helpful to capture the propagation behaviour characteristics of sticker breakouts in the mould thermograph but also improves the accuracy of breakout prediction.

Integrated Model of ACWGAN-GP and Computer Vision for Breakout Prediction in Continuous Casting

Integrated Model of ACWGAN-GP and Computer Vision for Breakout Prediction in Continuous Casting

Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms.

Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.

Intelligent breakout prediction method based on support vector machine

Breakout is a serious accident during continuous casting. In order to reduce breakout accident, a breakout prediction method was proposed based on support vector machine model. The typical characteristics of continuous casting breakout were reconstructed by thermocouple temperature signals. The temperature data of three adjacent thermocouples in the first row were added and then connected with the temperature data of the middle second row, which was used to capture the typical spatial characteristics of breakout. The 80% of true and false breakout samples were used to train the support vector machine model and 20% samples were used to test the model. The results show that this method can predict all breakouts, which has a true-sticker alarm rate of 100%. There is one false alarm and the accuracy rate is 95.8%.

Modeling of breakout prediction approach integrating feature dimension reduction with K-means clustering for slab continuous casting

Breakout is a catastrophic accident in continuous casting. The existing breakout prediction methods based on logical judgment and neural networks need to constantly adjust the prediction parameters or prepare high-quality samples as the input, resulting in poor robustness and unstable precision stability. Therefore, it is particularly important to develop a breakout prediction method that not only can predict breakout accurately but also avoid human intervention significantly. This work proposes a novel approach for breakout prediction combining K-means clustering and feature dimension reduction. The method uses feature dimension reduction to obtain the typical feature vector (TFV) that can characterize the original temperature change trend, and then a K-means clustering model is established to realize online detection of breakout prediction. The results show that the model has a 100% alarm rate for the true breakout, and meanwhile, reduces the number of false alarms from 555 to 217 compared with the on-line breakout prediction system (BPS). The proposed method does not need to adjust the prediction parameters frequently or prepare the input samples carefully, which not only avoids the human intervention but also meets the requirements of online monitoring for the practicality and applicability of the breakout prediction method.

Predicting mobile application breakout using sentiment analysis of Facebook posts

Publishing mobile applications on the official stores is becoming a big business. Many developers are charmed by the billion-dollar success of breakout applications. Thus, in order to ensure success, mobile applications need to sustain top ranking. Previous work on the predictability of mobile applications success aimed to extract from app stores relevant features that influence high rating. In this article, we propose an automated approach to exploit data available on Facebook platform that predicts mobile applications breakout. We collect data from Facebook graph API, then determine sentiment polarity of user comments. We design statistical features to score users sentiment for each post. Then, we compose posts scores with Facebook statistical measures to form a mobile applications breakout dataset. Finally, we use machine learning techniques to build our breakout prediction model. We evaluate our approach with 199 mobile applications and obtain a prediction accuracy of 83.78%. We find that Likes count on a Facebook page is decisive for climbing mobile applications ranking. However, a high rate of negative opinions declines application ranking and deprives mobile application of achieving a breakout. Based on these findings, we provide evidence that user interactions on social networks can influence the success of mobile applications.

Development of prediction method for mold sticking breakout based on density-based spatial clustering of applications with noise and dynamic time warping

As the core component of continuous casting machines, complex behaviors of fluid flow, heat transfer, mass transfer, and solidification occurring inside the mold are the key factors affecting the slabs quality. Breakout is one of the most catastrophic accidents in continuous casting process, which brings severe impacts on personal security, smooth producing, slab quality, and caster equipment. In particular, with the development of the high-speed casting technology, quality defects and sticking breakouts caused by high-load emerge frequently and missing or false alarms for online prediction of breakout occasionally occur. Thus, accurate identification and prediction for the mold breakout is a top priority for online processing control. Considering the typical temperature characteristics of “time lag” and “space inversion” during a breakout, this paper introduced the concepts of dynamic time warping (DTW) and density-based spatial clustering of applications with noise (DBSCAN) in machine learning. On the basis of collecting and distinguishing the typical change modes of mold temperature, an integrated novel method for predicting breakout was developed. The proposed method applied DTW to measure the similarity of mold thermocouple temperature under different casting speeds, steel grades, and other operating conditions, while DBSCAN was used to cluster and separate the temperature samples between normal casting status and sticking breakout. On the basis of the above mentioned method, the results show that the mold sticking breakout can be effectively detected and predicted. Compared with the traditional method based on logical judgment and artificial neural network, the clustering-based breakout prediction method does not require manual setting of thresholds or parameters. According to the common rule of temperature variation in historical samples of breakout, the typical characteristics of temperature in time and space can be extracted and fused, and the breakout can be accurately distinguished and predicted, which shows good self-adaptability and robustness.

Application of k-means clustering for temperature timing characteristics in breakout prediction during continuous casting

Breakout is the most serious incident in continuous casting. The missing and false alarms will seriously damage the caster and greatly affect the quality of slabs. In view of the defects of existing breakout prediction methods, k-means clustering and dynamic time warping (DTW) are combined to investigate and develop an effectual prediction method. Through extracting the typical temperature timing characteristics from the temporal and spatial perspectives, the method based on k-means clustering and DTW is proposed to distinguish and recognize the breakout. Compared with in-service breakout prediction system (BPS), the prediction results of the proposed method confirm that the number of false alarms can be reduced from 50 to 8 while ensuring a 100% correct alarm rate. The excellent prediction performance demonstrates that the clustering-based breakout prediction method exhibits good application potential, while it offers a novel approach to monitoring abnormalities in the continuous casting process.

Development of Sticking Breakout Prediction Method Based on Hierarchical Clustering of Temperature Timing Characteristics in Continuous Casting

Development of Sticking Breakout Prediction Method Based on Hierarchical Clustering of Temperature Timing Characteristics in Continuous Casting

Integrated Approach to Density-Based Spatial Clustering of Applications with Noise and Dynamic Time Warping for Breakout Prediction in Slab Continuous Casting

Mold breakout is a catastrophic accident that has serious impacts on smooth production, slab quality, and caster equipment. Accurate identification and prediction of an impending breakout are always top priorities in continuous casting operations. In view of crucial common features of mold copper plate temperatures during a breakout, such as time lag and space inversion, the concepts of density-based spatial clustering of applications with noise and dynamic time warping are introduced, and an integrated novel method for breakout prediction is developed. Through extracting and fusing the representative singularity and approximation of temperature variation, the typical temporal and spatial temperature characteristics during breakout can be distinguished and predicted accurately. Compared with traditional methods of logical judgment and artificial neural network, the method based on clustering does not need to modify forecast thresholds or parameters artificially, which overcomes the limitation of model dependence on human beings, and demonstrates excellent adaptability and robustness for online abnormality prevention.

A novel tool for simulating brittle borehole breakouts

Breakouts are important both as a threat to borehole or tunnel stability and as a tool for primary stress state interpretation. This work suggests a semi-analytical method based on conformal mapping for the prediction of breakouts in brittle rock. After introducing the underlying theory, the developed algorithm is outlined. The single components are described and verified. Finally, the applicability and efficiency of the resulting tools is assessed. The aim is to provide a simple, fast and easy to use tool for the assessment of borehole and tunnel stability and for the estimation of the in situ stress in brittle rock.

3D poro-elasto-plastic modeling of breakouts in deviated wells

Borehole breakouts are stress-induced enlargements of the wellbore cross-section. Predicting wellbore breakouts is important for the success of any drilling operation; however, accurate prediction of breakouts in deviated wells can be a technically challenging task. Misalignment between wellbore trajectory and field principal stress orientation along with the strong coupling between fluid flow, stresses and plasticity are some of the factors that makes breakouts in deviates well more challenging. This paper discusses a 3D poro-elasto-plastic numerical modeling approach for breakouts in deviated wells. Dynamic mudcake buildup on the wellbore wall is also incorporated into the numerical model, enabling quantitative analysis of the mudcake effect on the borehole breakout. The influences of well trajectory, fluid seepage between wellbore and formation pores, plastic strain softening of the rock, and dynamic mudcake buildup on the breakout are highlighted and analyzed using numerical examples. The results indicate that the breakout shape of deviated wells is largely dependent on well inclination while its volume can be reduced by mitigating fluid seepage through mudcake buildup. The proposed finite-element modeling approach in this study confirms the need to consider the coupling among these factors when predicting wellbore breakouts in complex geological and drilling conditions.

Wellbore breakouts: Mohr-Coulomb plastic rock deformation, fluid seepage, and time-dependent mudcake buildup

Borehole breakout is a time-dependent failure process which includes breakout initiation, propagation, and stabilization. Plastic rock deformation, fluid seepage, and time-dependent mudcake buildup on the wellbore wall affect the near-wellbore stress state and, therefore, the breakout behavior. In this paper, a hydro-mechanical model was developed for breakout prediction taking into account these factors. Filtration tests were conducted to obtain time-dependent permeability and thickness of mudcake, and the experimental testing results were incorporated to embody the dynamic mudcake buildup process. A sensitivity analysis was performed to investigate the effects of horizontal stress anisotropy, drilling mud pressure, and time-dependent fluid flow and mudcake buildup on breakouts in a vertical borehole. The simulation results show increased possibility of breakouts with larger horizontal stress anisotropy. Additionally, fluid seepage between the wellbore and the surrounding formation makes the breakout a time-dependent process. For low mud pressure, the initial breakout shape immediately after drilling is very similar to the final breakout shape after reaching steady state seepage. However, for high mud pressure, the wellbore may experience significant breakout propagation after drilling, owing to considerable fluid seepage associated with the larger differential pressure between the wellbore and the formation. Time-dependent mudcake buildup on the wellbore wall can effectively reduce the likelihood of borehole breakout by acting as a low-permeability barrier that mitigates fluid seepage across the wellbore wall and reduces changes in formation pore pressure. Disregarding the mudcake or considering a perfectly impermeable mudcake can lead to overestimating or underestimating the risk of borehole breakout, respectively. The proposed model provides a useful approach to understand and assess borehole breakout for drilling design.

Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules

Given advantages of artificial intelligent technology, the genetic algorithm (GA) and back propagation (BP) neural network is used to construct time series model for recognizing temperature change waveform of single thermocouple in mold breakout process. Based on breakout mechanism, the logic rules are used to construct spatial model of multi-thermocouples for identifying two-dimensional (2D) propagation behavior of the sticker. Time series model based on GA-BP neural network and spatial model based on logic rules form a new breakout prediction method. And the simulation and field test of this method are carried out for validating its performance. Simulation results of time series model show that the GA-BP neural network has better recognition precision than BP neural network for sticking temperature pattern of single thermocouple. And simulation results of spatial model show that it can predict all stickers accurately and timely, with no missed alarm and false alarm. Furthermore, field test results show that this breakout prediction method has detection ratio of 100% and a lower false alarm frequency (0.1365% times/heat), which is better than actual breakout prediction system used in continuous casting production. So the combined method of GA-BP neural network and logic rules is feasible and effective in breakout prediction and can be used in more intelligently industrial process.

Computer vision detection of mold breakout in slab continuous casting using an optimized neural network

A visual breakout prediction method for mold monitoring is proposed based on temperatures measured by thermocouples in the mold combined with the use of computer vision technology. This mold temperature rate thermography allows the characteristics of abnormal temperature regions to be captured and extracted, including the rate of temperature change with time, the geometry, and propagation velocity. On the basis of these characteristics, a back-propagation (BP) neural network model is constructed to detect mold breakout. The weight and threshold values of the model are optimized using the Levenberg–Marquardt (LM) algorithm and a genetic algorithm (GA) through an iterative process of sample training and testing. The results show that the GA–LM–BP neural network model is better than both the traditional BP and the LM–BP models. This breakout prediction model has a higher accuracy rate (83.3 %) and a lower false-alarm rate (0.05 %). The GA–LM–BP model has also been compared with an actual BOPS used in continuous casting production. Meanwhile, it provides a way of detecting abnormalities visually for continuous casting process. The results of this work also provide a positive example of the application of intelligent monitoring and visualization methods to continuous casting.