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Overview LDT-I200 is a new generation of converter lining thickness gauge launched by our company. The system equipment emits a pulsed laser beam to the lining surface of the converter, and receives the returned laser light to measure its flight time. Then the system equipment calculates the distance between the equipment and the target point according to the transmission speed of the laser. During measurement, the user pushes the equipment to a certain position on the platform in front of the converter, and the equipment can start to scan and measure the target area of the converter after the equipment performs rapid self-positioning. The Measuring principle The measurement speed of LDT-I200 can reach up to 2,000,000 points per second, and each scan can obtain distance data of more than 1.5 million points. The actual surface curve of the entire converter lining can be obtained by combining the measurement data of different positions and angles. During the measurement, the laser head of the equipment rotates at a certain step angle to rapidly change the angle of the pulsed laser beam. Within 4 minutes, the three-dimensional surface contour of the converter lining can be formed, and the thickness information of the furnace lining can be obtained through coordinate conversion calculation. The measurement accuracy up to ±2mm. In order to convert the distance from the internal point of the converter to the thickness gauge into the thickness of the furnace lining at each point, we must know the coordinate relationship between the trolley and the converter. We use the second laser head to position the trolley. When the trolley is pushed to the measurement site, the positioning laser head rotates at high speed to scan the three reflective strips opposite the converter. Through triangulation measurement and calculation, we can calculate the relative position of the trolley to the reflective strips. The coordinates of the reflective strip and the converter are known through mapping, so that the coordinates of the trolley relative to the converter can be accurately obtained. The main components 1. Laser measuring head 2. Wireless communication 3. Battery pack 4. Mobile laser thickness gauge host 5. Converter inclination measurement system 6. Cooling circulation system 7. Positioning auxiliary reflector 8. Laser positioning system The technical indexes Laser reproducibility: ±2mm Laser accuracy: ±2mm Local fastest measurement time: 15s Positioning time: 30s Full image measurement time: 4min The highest measurable lining temperature: 1850℃ Measuring distance: 0.5-25m Actual points are measured per frame: 1500000 points Continuous working time after fully charged: ≥3h Eye-safe laser: Class 1 laser product (doesn't hurt the eyes) The parameters of laser measuring head Measurement principle: According to the flight time and speed of the laser to calculate the distance Laser reproducibility: ±2mm Laser accuracy: ±2mm Scan rate: 2000000 points/second Measuring angle: vertical ±40°, horizontal 360° Maximum measurement temperature: 2200℃ Working temperature: -20℃-80℃ Cooling method: Integrated liquid cooling system, IP64 grade protection Laser wavelength: 1550nm (near infrared)

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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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