Mold Level Research Articles

Exploration of the causes of abnormal mold level fluctuation in thin slab continuous casting mold

Abnormal fluctuations in the mold can easily lead to slag entrainment during the thin slab continuous casting process. It is difficult to guarantee the uniform growth of the shell solidification of the slab. The quality of the slab is seriously affected. In this study, the data on thin slab mold level fluctuation were collected from a complete casting sequence of low-carbon steel in a Chinese plant. The discrete wavelet transform (DWT) method is used to decompose the mold level fluctuation and different process parameters. The frequency composition of abnormal fluctuations was clarified, and the causes of the abnormal fluctuations were revealed. The results show that the frequency ranges of abnormal fluctuations are concentrated in 0.3125-0.625 Hz and 0.625-1.25 Hz, and the low set mold level is where the maximum amplitude occurs. Within the time-frequency range, changes in parameters such as electromagnetic braking, mold oscillation, casting speed, stopper rod, and tundish weight cannot be completely matched to abnormal fluctuations collected by the eddy current sensor (ECS). It was found that the abnormal fluctuations in the ECS were that its equipment was disturbed through the analysis of the fluctuations collected by the radioactive source detection (RSD). The degree of disturbance is related to the height of the set mold level. The maximum fluctuation difference exceeded 6 mm between different collection methods when the set mold level was low. In this plant, the position of the ECS is to avoid the area of violent fluctuations of the surface molten steel and be moved appropriately towards the submerged entry nozzle (SEN) to reduce disturbance. When the current ECS position is unchanged, the mold level is set at approximately 305-309 mm, and the casting speed is matched to 4.8 m/min, which minimizes the disturbance with the ECS measurement.

Control of Instantaneous Abnormal Mold Level Fluctuation in Slab Continuous Casting Mold Based on Bidirectional Long Short‐Term Memory Model

The instantaneous abnormal mold level fluctuation (IAMLF) has significant harmful effects on slab quality. This article proposes a prediction and control method for IAMLF. First, the data processed by difference method is used for the IAMLF prediction and stopper‐rod position prediction. Then, the bidirectional long short‐term memory (BI‐LSTM) is introduced to predict the IAMLF; the corresponding stopper‐rod position is predicted according to the mold level prediction result. BI‐LSTM can predict the IAMLF with the mean absolute error of 1.52 mm and the false alarm rate of 1.8%, and also performs well in predicting the stopper‐rod position with a mean absolute error of only 1.92 mm. Furthermore, the two prediction models are combined to form a closed loop, where the mold level fluctuation is predicted according to the industrial data processed by data difference method, and the stopper‐rod position is adjusted in advance to eliminate IAMLF. Finally, the fuzzy proportional‐integral‐derivative (PID) controller is used to control mold level based on the mold level and stopper‐rod position prediction results. The prediction accuracy of IAMLF reaches 98.4%, and the present proposed fuzzy PID controller can effectively prevent the occurrence of IAMLF with a success rate of 95.6%.

Characterizing and Controlling Abnormal Periodic Mold Level Fluctuations in a Commercial Slab Continuous Caster Using Big Data

Characterizing and Controlling Abnormal Periodic Mold Level Fluctuations in a Commercial Slab Continuous Caster Using Big Data

Characteristic analysis of mold level fluctuation during continuous casting of Ti-bearing IF steel

Mold level fluctuation has always been one of the key factors in slab quality control. Abnormal fluctuations can easily cause slag entrainment and form surface defects of hot-rolled and cold-rolled steel sheets, thus reducing the product qualification rate. In this study, mold level fluctuation data was collected during Ti-bearing Interstitial Free (IF) steel in a stable state for a complete casting sequence in a certain factory. The mold level fluctuation is analyzed using the discrete wavelet transform (DWT) method, and its characteristic values are classified based on frequency. Combined with the corresponding process parameters, the causes were traced during abnormal fluctuation characteristic values. It was found that the formation of abnormal mold level fluctuation mainly comes from two aspects. On the one hand, there are random fluctuations in the low-frequency band, and on the other, there are clustered fluctuations in the relatively high-frequency band. There is always a rising trend in mold liquid levels when there are random abnormal fluctuations in the low-frequency band. There is a delay of approximately 0.1412 s in the feedback of the stopper rod to the mold level fluctuation. The major causes of the low-frequency random fluctuations include variations in the tundish weight, casting speed, and mold width, and the clogs peeling. The clogging of the side hole at the submerged entry nozzle (SEN) is the primary cause of abnormal clustered fluctuations in the high-frequency band. These are certain relationships with clogging, whether it is high-frequency clustered or low-frequency random abnormal fluctuations. Without a doubt, this is a crucial link that limits the slab quality.

Preparation of Anti Mold Cleaning Agent and its Effect on the Performance of Insulation Materials

A mold resistant cleaning agent with long-term cleaning function was prepared using hydroxyl silicone oil, mold inhibitors, and some additives as the main raw materials. The results show that the anti mold cleaning agent prepared by adding different proportions of anti mold agents not only ensures the cleaning and anti mold effects on the insulation material, but also has no significant impact on its mechanical properties, electrical properties, and surface hydrophobicity. Moreover, when the content of the anti mold cleaning agent is 2% to 3%, its comprehensive mechanical and electrical properties can maintain a good level. Through infrared analysis, it can be seen that during the mixing process of cleaning agent and anti mold agent, due to the consumption of some double bonds, the internal molecular structure of the cleaning agent has been changed, which not only has good cleaning effect but also excellent anti mold effect. When the amount of anti mold agent is 2%, the anti mold level of the insulation material is the best, reaching level 0, and the hydrophobicity can also reach HC1 after cleaning.

Numerical Analysis of the Effect of SEN Port Angle on Mold Level Fluctuation Based on Wavelet Transform

Numerical Analysis of the Effect of SEN Port Angle on Mold Level Fluctuation Based on Wavelet Transform

Optimal Sensor Placement for Mold Level in Continuous Casting

For high product quality in the continuous casting of steel, it is important that the liquid steel level in the mold remains constant and that its surface is flat. A slag layer on top of the liquid steel surface prevents direct optical measurement of the surface. However, knowing the mold level and surface shape is essential for quality monitoring and feedback control. The current paper addresses the optimal placement of mold-level sensors, given that their number is limited. The actual estimation of the surface shape is performed by an observer that receives mold-level measurements as input. The computed optimal sensor positions are validated based on experimental data from a full-scale laboratory water model of the continuous caster.

Self-tuning regulatory controller of cyclical disturbances using data-driven frequency estimator based on fuzzy logic

This paper proposes two learning-type regulatory controllers: adaptive fuzzy iterative learning controller (AF-ILC) and adaptive fuzzy repetitive generalized predictive controller (AFR-GPC). The proposed controllers can reject the cyclical disturbances with small range frequency variations present in many control loops of industrial processes. Moreover, they can estimate the disturbance cycle using fuzzy logic with rules based on the previous values of the integral of the absolute error between the setpoint and output. The estimated period of the disturbance cycle is sent to the regulatory controllers to define the specific control actions required to reduce the oscillations in the process output. The advantage of this technique is that adaptive controllers are automatically adjusted by a frequency estimator, which only depends on process output data. The performance levels of the developed adaptive controllers are compared in the computational simulator of a nonlinear mold level of a continuous casting process employed in the steel industry. This process counteracts the periodic oscillations in the mold level caused by bulging disturbance. The numerical results show that both AF-ILC and AFR-GPC can reduce the oscillations in the output when the disturbance frequency changes by up to 5.2% and 13%, respectively.

Physical Simulation of Mold Level Fluctuation Characteristics

Physical Simulation of Mold Level Fluctuation Characteristics

Influence of mould level instability on the unevenness of solidified shell deformations during continuous casting

ABSTRACT The influence of mould level instability during continuous casting on the unevenness of the initial solidified shell in hypo-peritectic steels has been investigated. Water model experiments and in-plant casting experiments revealed that mould level instability, which increases with increasing casting speed, is related to longitudinal cracking resulting from the unevenness of the solidification shell. A numerical model which considers the effect of mould level instability was developed and used to simulate the formation of the unevenness of the solidification shell and solidified shell deflections. The calculation results show that an increase in the non-uniform infiltration of mould flux increases the unevenness of the solidified shell. On the other hand, the unevenness of the solidified shell decreases due to mild cooling. Overall, the results of the plant tests and calculations show that non-uniform infiltration of mould flux dramatically affects air gap formation and the unevenness of the solidified shell.

Deep Insight into Mold Level Fluctuation During Casting Different Steel Grades

Deep Insight into Mold Level Fluctuation During Casting Different Steel Grades

Casting parameters and propagation behaviour of sticker breakout for the conventional slab

ABSTRACT The casting parameters and behaviour of 61 sticker breakouts were analysed during the continuous casting of the conventional slab. The difference of sticker breakouts between the conventional slabs and wide and heavy slabs was also studied. The results show that the high casting speed or change of casting speed is the main reason. The mould level of the conventional slab is small and not the reason for sticker breakouts, which is different from the causes of wide and heavy slab. In terms of propagation behaviour, there are 14 cases out of 61 sticker breakouts occurred only around one column of thermocouples. The vertical velocity is in the range of 0.3∼2.4 m·min−1, and the horizontal velocity is in the range of 1.0∼9.0 m·min−1. The vertical velocities of the three sticker breakouts are higher than their casting speeds, which is different from the wide and heavy slab.

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.

Examining the reliability of the data analysis system for continuous steel casting

The application of industry 4.0 guidelines is also an integral part of the life of ISD Dunaferr Zrt. Working closely with technology professionals, we have created a data analysis system that processes data from continuous steel casting. The life cycle of slabs can be traced in a complex way from raw steel production to the end of hot rolling. In an industrial environment, the program provides an opportunity to study the effects on slabs (e.g. change in casting speed, change in mould level, change in the position of tundish stopper rod (TSR), etc.). The results provided by the system are suitable for both qualifications made by experts and automatic one during the visual inspection using the web display system. In this paper, we examined the correlation between the slabs burdened with known significant changes in casting parameters in the cast sequence and the slabs with significant changes classified by the data processing system to verify the reliability of the data analysis system. The results confirmed the reliability of the data analysis system and pointed to the greater reliability of the study of cast length-based changes versus time-based changes.

HEPA filtration intervention in classrooms may improve some students’ asthma

Objective The School Inner-City Asthma Intervention Study 2 (SICAS 2) tested interventions to reduce exposures in classrooms of students with asthma. The objective of this post-hoc analysis was limited to evaluating the effect of high-efficiency particulate (HEPA) filtration interventions on mold levels as quantified using the Environmental Relative Moldiness Index (ERMI) and the possible improvement in the students’ asthma, as quantified by spirometry testing. Methods Pre-intervention dust samples were collected at the beginning of the school year from classrooms and corresponding homes of students with asthma (n = 150). Follow-up dust samples were collected in the classrooms at the end of the HEPA or Sham intervention. For each dust sample, ERMI values and the Group 1 and Group 2 mold levels (components of the ERMI metric) were quantified. In addition, each student’s lung function was evaluated by spirometry testing, specifically the percentage predicted forced expiratory volume at 1 sec (FEV1%), before and at the end of the intervention. Results For those students with a higher Group 1 mold level in their pre-intervention classroom than home (n = 94), the FEV1% results for those students was significantly (p < 0.05) inversely correlated with the Group 1 level in their classrooms. After the HEPA intervention, the average Group 1 and ERMI values were significantly lowered, and the average FEV1% test results significantly increased by an average of 4.22% for students in HEPA compared to Sham classrooms. Conclusions HEPA intervention in classrooms reduced Group 1 and ERMI values, which corresponded to improvements in the students’ FEV1% test results.

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.

A Model-Based Predictive Controller of the Level of Steel in the Mold with Disturbances Using a Repetitive Structure

Keeping the level of steel in the mold of the continuous casting process constant is fundamental for the quality of the steel produced and, consequently, its commercial value. It is challenging, considering the several disturbances that cause undesired variations in the mold level. The aim of this paper is to apply a repetitive structure composed of two controllers, a generalized predictive controller (GPC) and a repetitive GPC (R-GPC) with constraints to mitigate the bulging and clogging/unclogging disturbances and the casting speed variation in the mold level of the process. The R-GPC controller has the same characteristics as the GPC, such as performance, robustness to disturbances, and insertion of constraints, and its advantage is the elimination of periodic disturbances. The repetitive structure will be implemented with a robustness filter and tuned by a genetic algorithm (GA). The controller tests are performed by simulations of a nonlinear mathematical model of the mold level, validated using real data from the steel industry. The proposed controller reduces the bulging disturbance amplitude by 98.5% and at 25% of the frequency of reversions in the valve. Consequently, the proposed controller allows an increase in the valve life span, a reduction in maintenance costs, and quality improvement in the steel slab.

Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

Good slow disturbances attenuation in a mold level control with stopper rod is very important for avoiding several product defects and keeping down casting interruptions. The aim of this work is to improve the accuracy of the diagnosis and compensation of an adaptive mold level control method for slow disturbances related to changes of stopper rod. The advantages offered by the architecture, called Adaptive-Network-based Fuzzy Inference System, were used for training a previous model. This allowed learning based on the process data from a steel cast case study, representing all intensity levels of valve erosion and clogging. The developed model has high accuracy in its functional relationship between two compact input variables and the compensation coefficient of the valve gain variations. The future implementation of this proposal will consider a combined training of the model, which would be very convenient for maintaining good accuracy in the Fuzzy Inference System using new data from the process.

Flow Control Based on Feature Extraction in Continuous Casting Process.

The flow structure in the mold of a continuous steel caster has a significant impact on the quality of the final product. Conventional sensors used in industry are limited to measuring single variables such as the mold level. These measurements give very indirect information about the flow structure. For this reason, designing control loops to optimize the flow is a huge challenge. A solution for this is to apply non-invasive sensors such as tomographic sensors that are able to visualize the flow structure in the opaque liquid metal and obtain information about the flow structure in the mold. In this paper, ultrasound Doppler velocimetry (UDV) is used to obtain key features of the flow. The preprocessing of the UDV data and feature extraction techniques are described in detail. The extracted flow features are used as the basis for real time feedback control. The model predictive control (MPC) technique is applied, and the results show that the controller is able to achieve optimum flow structures in the mold. The two main actuators that are used by the controller are the electromagnetic brake and the stopper rod. The experiments included in this study were obtained from a laboratory model of a continuous caster located at the Helmholtz-Zentrum Dresden Rossendorf (HZDR).

Eddy current method for steel billet mould level control

The relevant issue of metal level control in a mould of continuous casting machine (CCM) is improving the measurement accuracy when adding slag powders in a mould. The main disadvantage of the widely used radiometric metal level control devices (MLCDs) is the dependence of the output signal on the amount of slag in a mould. Eddy current (EC) sensor for metal level control is not sensitive to the slag. It detects metal level indirectly by temperature of copper mould wall, which is in contact with liquid metal. As a consequence, eddy current MLCD speed of response is limited to the time constant of thermal transient processes in a mould wall, which is insufficient to modern steel industry. This paper deals with a mould CCM metal level control method by means of the EC sensor based on signal detection from the liquid metal through a copper mould wall. The direct method provides required signal speed of response of eddy current MLCD in the case of steel casting with the slag.