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Overview In order to ensure the quality of the slab, it is necessary to strictly control the performance and various parameters of the CCM’s guide roll within a certain error range. Due to the continuous casting machine (CCM) is large and in a harsh production environment, it is difficult to measure the performance of the continuous casting machine manually, and many parameters are difficult to measure. Therefore, it is very important to have a set of equipment that can simulate drawing of steel billet to realize the measurement of CCM’s various parameters and performance. The multifunctional strand condition monitor uses various sensors to realize the measurement of CCM’s roll gap value, the arc value of continuous casting section, the rotation quality of the rolls, and the water spray pressure under the condition of simulating drawing of steel billet. Therefore, the CCM’s quality can be controlled in real time, and it is convenient for the maintenance personnel to analyze the CCM’s measurement results. The problem areas of the casting machine equipment can also be found. If these problems doesn’t be found in time, it will cause the bad quality of CCM’s products, the steel breakout in pouring process, surface and internal cracks and centerline deviation. The measurement of the strand condition monitor Roll gap value： By measuring the distance between a series of inner arc guide rolls and outer arc guide rolls on the CCM’s roller bed. It can be used to set the correct roll gap spacing for each pair of guide rolls in the slab caster. Arc condition of guide roller ： Measuring the position between the position of the CCM’s outer arc guide roll and the two adjacent guide rolls and then measuring the deviation compared with the origical position of CCM’s guide roll, we can obtain the arc condition of the outer arc guided roller. Guided roller rotation： It is possible to know whether each guided roller can rotate freely by measuring. Water spray pressure： The operation of the water spray cooling system can be judged by measuring the situation of secondary cooling water spray at different positions of CCM's width direction. Then we can accurately locate whether the water spray at a certain point is normal. The main components of the strand condition monitor 1. The main body of the strand condition monitor used to measure the continuous casting sector 2. The connecting chain between strand condition monitor and dummy bar 3. The storage rack chain of strand condition monitor 4. Battery charger: 24V 5A 5. Calibration equipment 6. Remote control 7. Laptop 8. Printer 9. Data communication cable The technical parameter 1. The measurement range of roll gap: 210mm, 230mm (can be customized according to requirements) 2. The measurement accuracy of roll gap: 0.01mm 3. The arc measurement range: ±80° 4. The arc measurement accuracy: 0.01° 5. The spray measurement range: 0-10 6. The spray measurement accuracy: ±1 7. The battery usage times: 3 times

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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