The steel industry is an important pillar of the national economy, and the quality and production efficiency of hot-rolled strip steel, as a basic material, directly affect the downstream industrial chain. In recent years, as users have increased their requirements for the surface quality and performance of steel plates, traditional laminar cooling technology has gradually exposed problems such as free roll scratches, low precision cooling, and high tension.
This article is based on the “Research and Practice of Laminar Cooling Control Technology” project launched in 2024. It combines advanced technologies such as industrial logic and edge control, and starts from three aspects: process optimization, equipment transformation, and intelligent control. It explores the upgrade path of the laminar cooling system of the steel rolling mill and provides a replicable solution for the industry.
Current status and problem analysis of fluid layer cooling technology
1. Free roller and dead roller problem: The traditional laminar roller adopts drum-shaped gear coupling. Due to poor alignment and insufficient lubrication, free roller and dead roller appear during the start and stop of the roller gap, causing scratches on the plate surface and a decrease in the yield rate of about 5%.
2. Low cooling efficiency: The original side spray system adopts a 400kW high-voltage motor, which is laid high and can only cool on one side. The risk of high-temperature roller deformation is high, which affects the life of the roller.
3. Insufficient temperature control accuracy: In the manual temperature control mode, the plate surface is generally wet, and the temperature fluctuation range reaches ±30℃, resulting in mixed crystals and unstable performance at the edge of the steel plate.
Data verification and demand analysis
In January 2024, the project team collected data from the existing system and found that the temperature difference between the head and tail of the coil reached 40°C, the power consumption of laminar cooling was 280Kwh, and the utilization rate of the automatic control temperature motor was less than 50%.
Through industrial brain modeling and analysis, the optimization direction was determined to be: improving the stability of roller operation, improving cooling synchronization, and realizing intelligent closed-loop control.
Fluid layer cooling control technology solution
Key technology innovation
1. Laminar flow motor transformation
The original 400kW high-voltage motor used in the side spray system was converted into a 110kW low-voltage motor to reduce power consumption.
2. Laminar roller coupling modification
The drum gear coupling was upgraded to a universal shaft structure to reduce the impact of motor shaking on the roller table, the number of free rollers decreased by 70%, and the plate surface scratch rate dropped to below 0.5%.
3. Encrypted header and water curtain system
① Upgrade of bottom spray cooling water: 150 sets of dense cooling pipes are added to cover the roller steps to achieve double-sided uniform cooling, and the roller temperature control accuracy is improved to ±5℃.
② Water curtain system design: upper and lower water curtain headers are installed to spray water through the slits for continuous laminar flow, with a width shrinkage rate close to zero, a 30% increase in cooling efficiency, and a 15% reduction in yarn.
4.Automation control platform
① Industrial Brain AI Model: Build an optimization model based on Alibaba Cloud Industrial Brain, collect temperature, speed, and water volume data in real time, recommend optimal operating parameters, and achieve an 80% utilization rate of automatic temperature control.
② Edge Control Terminal: Deploy APC control algorithm to achieve real-time closed-loop control of laminar cooling, increase the response speed to milliseconds, and reduce the coiling temperature fluctuation range to ±10°C.
System architecture and implementation path
1. Hardware deployment: Install surface detectors, radiation pyrometers and other equipment to build a data acquisition network; upgrade application servers and data servers to support real-time computing needs.
2. Software integration: Develop visual applications and sound and light alarm systems to seamlessly connect with production systems and assist in operation and maintenance management.
3. Phased verification:
Phase I (January-February 2024): Complete demand analysis and architecture design, and conduct a material feeding test to verify feasibility.
Phase II (March-June 2024): Implement roller table transformation and system installation, and optimize control parameters.
Phase III (July-August 2024): Fully invest and train operators, and summarize technical achievements.
Implementation Outcomes and Results
Implementation Outcomes and Results
1. The mixed crystal rate at the edge of the strip is reduced by 90%, and the stability of the finished product performance data is improved to 98%.
2. The temperature difference between the head and tail of the coil is controlled at 15-20℃, reaching the advanced level in the industry.
3. The power consumption of the side spray pump per ton of steel is reduced from 280Kwh to 77Kwh, with significant energy-saving effects.
4. The efficiency of automatic temperature control is increased from 50% to 80%, reducing manual intervention.
Economic and social efficiency
1. Direct benefits: save 570,000 yuan in electricity bills and 100,000 yuan in maintenance costs per year, with a total efficiency increase of more than 670,000 yuan.
2. Improve efficiency: improve product quality stability, reduce customer complaints by 60%, and enhance market competitiveness.
3. Environmental protection contribution: reduce the comprehensive carbon dioxide per ton of steel by 5%, and reduce high-pressure emissions by about 1,200 tons per year, which is in line with the “double carbon” goal.
Through the systematic innovation of laminar cooling control technology, the steel rolling mill has achieved a comprehensive upgrade from equipment transformation to intelligent control. The project not only solves the technical pain points in traditional processes, but also builds an efficient, energy-saving and stable production system with data-driven as the core.
