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Overview Mold is the casting billet forming equipment in continuous steel casting, and it is also the core equipment and key technology of continuous casting machine. Its function is to forcibly cool the high-temperature molten steel continuously injected into its inner cavity through the water-cooled copper plate to export its heat and to gradually solidify it into a casting billet with the required cross-sectional shape and billet shell thickness. And the casting billet, which is still in liquid state, is continuously pulled out from the lower nozzle of the mold to create conditions for casting billet to be completely solidified in the subsequent secondary cooling area. The main function of the mould oscillation device is to make the mould perform mechanical movement according to the given characteristics of amplitude, frequency and waveform deflection. The purpose is to facilitate demoulding and prevent the occurrence of sticking and breakout accidents due to the bonding of the casting billet with the copper wall of the mold during the solidification process. MOC-K100 mould oscillation online monitoring system can three-dimensionally detect the positive and negative frequencies and waveform characteristics of the mould oscillation. The online changing data is provided in real time, which provides convenient and effective detection methods for on-site inspection personnel. The meaning of the system 1. When the continuous casting machine is not working properly, the billet quality problems (such as unstable oscillation marks, edge cracks, etc.) occur. According to the monitoring results of the system, we can combine with the actual on- site situation to analyze the cause of the failure, and efficiently evaluate and solve the problem of the oscillation table to improve product quality. 2. During the normal operation of the equipment, with the fatigue and wear of the mechanical structure of the mold, the expected oscillation parameters of the mold and the actual oscillation parameters will have a oscillation difference with time change, resulting in the internal process parameters’ changing of the continuous casting machine. Timely and accurate monitoring of changes in these parameters is helpful to schedule maintenance. 3. Transfer equipment maintenance from regular maintenance to state maintenance, do not repair when there is no problem, and arrange maintenance according to production needs when abnormal signs appear. Prevent over-repair and under-repair. 4. According to the monitoring results of the equipment, the on-site working parameters are adjusted in time to avoid adopting inaccurate or large-error oscillation to work and prevent the "breakout" accident. 5. We can timely find out that the oscillation is not stable or out of control in a small range, so as to avoid the formation of unevenness and unstable oscillation marks on the surface of the slab during the solidification of molten steel into the slab, and improve the surface quality of the product. 6. Adjust the working parameters reasonably according to the monitoring results, avoiding the use of excessive safety factor, making the entire assembly line play a normal working efficiency, increasing the output of billets, and create favorable conditions for increasing the casting speed. The functions of the system (1) Overview of monitoring parameters: Provides an overview of real-time monitoring parameters in the full screen, displaying all real-time data including oscillation parameters and process parameters. (2) Time domain waveform: This function mainly displays the oscillation waveforms of multiple measuring points and one channel standard waveform in real time, and simultaneously displays the three elements of oscillation of each measuring point in real time. (3) Waveform superposition: The difference between this function and the time domain waveform is that the waveforms of multiple measuring points are superimposed on a graph with lines of different colors on the same scale, and the oscillation difference of each channel can be calculated and displayed intuitively. (4) FFT: This functional module completes the calculation and display of the oscillation spectrum in real time. It is used to evaluate the waveform distortion of the entire oscillation table and to determine whether there is a problem with the installation of the oscillation device. (5) Bar graph: The oscillation of each measuring point is indicated by a bar graph of peak value and sensor gap voltage, which is more intuitive. (6) Trend: After starting this module, the operator can search the historical records in the database, analyze the changing trend of each parameter, and use it to analyze the rationality of the steel quality and processing parameter settings for different steels under the oscillation conditions. The parameters of the system 1. Number of measurement channels: According to the mold design, up to 16 sensors can be arranged 2. Sensor: three-dimensional oscillation sensing (X, Y, Z three axes) 3. Sensitivity: 1000mV/g 4. Frequency range: 0.04~10HZ 5. Frequency accuracy: ±0.01 Hz 6. Amplitude accuracy: 0.005mm 7. Transmission mode: wired digital transmission, wireless transmission 8. Host: 3U standard rack, handheld device 9. Sensor power supply: 24V.DC 10. Host power supply: 220V.AC/50Hz Mold oscillation curve The structure of the system The main components of the continuous casting mould online oscillation detection system are: 1. Three-dimensional oscillation measurement sensor 2. Handheld oscillation sensor calibrator 3. Dedicated signal connection cable 4. Preamplifier 5. Operation processing unit 6. Graphic display terminal 7. Industrial Ethernet (TCP/IP) or PROFIBUS-DP communication components 8. Control cabinet

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Overview: The BPS-K600 continuous casting mould breakout prediction system is developed by Wuhan CenterRise M&C Measuring Co., Ltd. It adopts artificial neural network and combines with traditional artificial intelligence and information processing technology to overcome the defects of traditional logic-based prediction models. It has the functions of self-adaptation, self-organization and real-time learning. The breakout prediction system has an optimized industrial network structure. According to the distribution and change rules of the temperature field of the mold, it can track and respond to the breakout process in real time. It can learn and judge by itself, and provide early warning information for on-site operations. It can effectively prevent the occurrence of breakout accidents, greatly reducing the cost and improving the safety factor of continuous casting. The system is suitable for all kinds of continuous casting machines such as square billet, round billet, rectangular billet and slab. Block Diagram of Breakout Prediction System The composition of the system Thermocouple: according to the on-site conditions of the mold, water tank, bolts and mold frame to desgin Front-end acquisition system: Multi-module acquisition chain, industrial Ethernet interface, high-speed embedded processing module Working platform: Real-time display of mold temperature field status, online analysis and prediction of mold breakout Server: Data exchange and storage management, reporting and printing system, with data analysis and self-learning functions The Main technical parameters Data sampling: the sampling frequency of K-type/T-type thermocouple can up to 10HZ Accuracy of temperature measurement: ±0.25℃ Support communication protocol: Industrial Ethernet (TCP/IP)，PROFIBUS-DP，CANopen Executive standard: DE0411 Standard Class III/1EC584/IEC1515 Transmission speed: 100M/bps The composition of the software Online monitoring software for breakout prediction Offline query software for breakout prediction Data recovery and self-learning software packages Data acquisition and monitoring software The software package of mold thermal status display The functions of the system Real-time data acquisition, data analysis and judgment Automatic selection of steel grade’s alarm parameters Real-time dynamic picture monitoring Prediction alarm and information display of breakout signs Automatic deceleration after alarm Historical data storage and historical trend graph display Automatic report generation and printing Alarm prediction and pattern recognition of unknown steel grades by neural network Dynamic real-time temperature profile prediction Mould heat flow field display FIG 1：Typical process of mold breakout prediction The features of the system 1. Reasonable design of temperature measurement point and professional protection front-end acquisition system, and complete installation and calibration tool components. 2. The system has strong adaptability and can meet the transformation of various structural continuous casting machines. 3. High-speed data communication, using industrial network to form an independent local area network for each workstation and server. It can realize high-speed data transmission of alarm files, alarm parameters, and other databases. 4. The combination of artificial neural network and intelligent forecasting model improves the alarm accuracy of breakout forecasting. 5. Historical data storage and historical trend graph display, historical reports, historical alarm legend printing, real-time alarm data printing. 6. Provide a local area network interface, which can realize the storage and forwarding of alarm files, and the input and output of offline alarm data. 7. Remote Web browsing can be achieved according to customer needs. 8. Can be seamlessly connected with MES-C220 mould expert system.

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Overview: In the steelmaking production process, the ladle is the key equipment for holding molten steel, secondary refining, and continuous pouring. In a modern steelmaking plant, refractory materials are required for converters, refining, and ladles. In the steelmaking process, ladle is the main equipment for using and consuming refractory materials. Improving the service life of ladles and reducing refractory consumption is the key technology for steelmaking plants to save costs and reduce energy consumption for green production. In order to ensure the orderly operation of the ladle, save the consumption of refractory materials, prolong the service life of the ladle, and ensure the safe operation of the ladle, Wuhan CenterRise M&C Engineering CO. developed and launched the LDT-N200 integrated management system for the ladle. Introduction: LDT-N200 ladle integrated management system integrates LDT-L200 ladle lining thickness gauge, TBP-S300 ladle, tundish baking temperature and leakage monitoring system, RFD -C100 metallurgical electronic positioning system, and also integrates SQL Server database and NeuralWare (American computer software company) neural network software NeuralWorks to realize intelligent and optimized management of ladles. LDT-L200 ladle lining thickness gauge uses a laser area array scanning sensor to accurately measure the real-time thickness of the ladle lining, calculate the lining loss, and predict the service life of the ladle. TBP-S300 ladle, tundish baking temperature and leakage monitoring system uses infrared thermal imaging camera and embedded armor wire system to comprehensively monitor and prevent ladle leakage. RFD-C100 metallurgical electronic positioning system installs RFID electronic tags on all ladles to realize full-time and full-space ladle positioning and tracking. LDT-S200 ladle optimization control and analysis software, based on SQL Server database, B/S network structure, realizes online management of ladles, and adopts BP neural network system to realize intelligent optimization control of ladles. LDT-N200 ladle integrated management system can realize ladle age management based on ladle lining data and neural network algorithm, safety process control of ladle turnover and use, full-time tracking management of steelmaking production and full-space tracking management of ladle use and provide optimal control and management of steelmaking production and ladle maintenance. Comprehensive ladle measurement and monitoring, ladle positioning and management, and data management and optimization enable the LDT-N200 ladle integrated management system to fully realize ladle entire process management, optimize the service life of ladles, and comprehensively reduce the cost of refractory materials. The features: Using a lining thickness gauge to measure the thickness of the ladle lining Using infrared thermal imaging camera/embedded armor wire to realize ladle leakage detection Using RFID/number plate recognition to realize the location management of ladles Ladle age management system based on overall tracking Overall (full-time full-space) ladle management system based on database Provide overall production scheduling data Using neural network to predict ladle service life based on big data Ladle Optimal Scheduling System based on database and ladle service life Increase the service life of ladle by 12%-20% Reduce the use of refractory materials more than 15% Realize orderly management of ladle baking Realize full space and full time domain control Reduce the costs of ladle management and maintenance Improve the efficiency of ladle turnover operation

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