In the era of digitalization, the steel industry is undergoing a high-quality transformation by integrating advanced technologies (industrial Internet, big data, 5G, AI, smart equipment) with traditional manufacturing. For long product production—characterized by multi-process, multi-line operations—pain points like fragmented data, poor cross-process collaboration, low efficiency, and weak traceability have driven urgent demand for smart manufacturing.
The industry now typically builds integrated smart systems (via control centers, automated upgrades, industrial models, and smart equipment) to synchronize material, energy, and information flows, achieving efficient operations, intelligent processes, and flat management.
I. Core Technologies Reshaping Long Product Rolling
Below is a detailed breakdown of the transformative technologies driving long product rolling, along with their strategic advantages, problem-solving capabilities, and tangible outcomes:
1. Full-Process Material Tracking & Traceability
1.1 Key Technologies
- Smart tracking and identification: Replaces traditional sensor-based methods with AI-powered systems and robotic laser marking. Edge-detection algorithms enable precise “per-piece tracking” of finished sections post-rolling.
- Full-lifecycle traceability system: Establishes a seamless data link across ironmaking, steelmaking, continuous casting, rolling, and finishing processes, assigning a unique “digital ID” to each product.
1.2 Strategic Advantages
- Achieves a 99.9%+ traceability rate, far exceeding the performance of traditional tracking technologies.
- Captures comprehensive data, including raw material composition, real-time process parameters, and quality inspection metrics.
1.3 Problems Solved
- Eliminated the long-standing industry gap of poor traceability for complex long products (e.g., heavy H-beams).
- Resolved tracking errors caused by unstable signals during hot sawing and other high-temperature processes.
1.4 Tangible Outcomes
- Increases yield for heavy H-beams by 3% and reduces scrap rates by over 10%.
- Enables rapid root-cause analysis for quality issues, minimizing customer disputes and enhancing trust.
2. Intelligent Steelmaking Automation
2.1 Key Technologies
- One-click automated systems: Integrates IT and operational technology (OT) to enable automated desulfurization, slagging, steelmaking, tapping, and refining. Autonomously captures material, energy, information, and time flows during production.
- AI-driven slag-steel recognition: Leverages image databases and edge computing to detect iron content in slag; deploys digital twin models for automated, precise slag handling.
2.2 Strategic Advantages
- Minimizes human intervention, reducing reliance on operator experience and eliminating human error.
- Enables precise control of material consumption, particularly optimizing slag iron content to avoid waste.
2.3 Problems Solved
- Reduces high-risk, labor-intensive on-site tasks (e.g., manual temperature sampling in extreme heat).
- Eliminates resource waste stemming from inaccurate manual judgment of slag iron content.
- Stabilizes production rhythms and product quality, which were previously disrupted by inconsistent manual operations.
2.4 Tangible Outcomes
- Lowers iron and steel material consumption by 4.5kg per ton and lime usage to just 27kg per ton (industry-leading efficiency).
- Deploys “robot clusters” (30+ units) to replace high-risk roles, significantly improving labor efficiency and workplace safety.
- Enhances the stability of product composition and mechanical properties.
3. Direct Rolling for Long Products
3.1 Key Technologies
- High-temperature billet control: Optimizes caster layout and casting speed to ensure billet temperature uniformity (≤50℃ temperature difference between head and tail; discharge temperature >1000℃).
- Casting-rolling queue management: Applies queue-theory models to optimize billet transfer, maximizing direct rolling rates.
- Low-energy casting-rolling integration: Utilizes induction heating or soaking (tailored to caster-rolling mill distance) and controlled cooling to stabilize product performance without excessive energy use.
3.2 Strategic Advantages
- Reduces rolling energy consumption by 80% by eliminating or minimizing reheating furnace use.
- Achieves a 99% direct rolling rate, adapting to short, medium, and long caster-rolling mill distances.
- Maintains yield strength variation of ≤10% for products from the same cast.
3.3 Problems Solved
- Balances the conflicting goals of “low energy consumption” and “high quality”—a challenge for traditional direct rolling, which often risked performance instability.
- Resolves poor casting-rolling coordination, such as uneven billet temperatures and disrupted production rhythms.
- Addresses high energy consumption in rolling processes, which account for 10% of total steel industry energy use.
3.4 Tangible Outcomes
- Aligns with global “dual carbon” goals by reducing carbon emissions and energy intensity.
- Increases production capacity from 220,000 tons/year to 345,000 tons/year for optimized lines, lowering unit costs.
- Enables stable production of high-end profiles for specialized sectors (e.g., construction, wind energy).
4. Remote Central Control & Predictive Maintenance
4.1 Key Technologies
- Centralized control center: Integrates real-time production data, equipment status, and quality metrics to enable “one-screen oversight and one-click control” of the entire production chain.
- Automation integration and upgrades: Enhances L1/L2 control systems and 5G networks to enable remote operation of ironmaking, steelmaking, and rolling processes; deploys AI-powered video monitoring for real-time anomaly alerts.
- Predictive maintenance system: Uses sensor data (vibration, temperature, pressure) and machine learning algorithms to forecast equipment failures and enable proactive maintenance.
4.2 Strategic Advantages
- Flattens management structures by breaking down data silos and departmental barriers.
- Improves workplace safety by replacing on-site operations in high-temperature, high-risk environments with remote control.
- Reduces unplanned downtime by shifting from reactive to predictive maintenance.
4.3 Problems Solved
- Eliminates delays in information transmission and inconsistent operations caused by decentralized process control.
- Reduces labor redundancy and inefficiency in legacy production lines reliant on manual experience.
- Cuts maintenance costs and production disruptions caused by frequent equipment breakdowns.
4.4 Tangible Outcomes
- Increases labor efficiency by 30% and production efficiency by 10% (e.g., achieving stable rolling of one billet every two minutes).
- Lowers ton-steel costs by 8% through reduced labor expenses and minimized maintenance downtime.
- Builds an “industrial brain” for smart scheduling, anomaly detection, and data-driven parameter recommendations.
5. Big Data-Driven Rolling Process Optimization
5.1 Key Technologies
- Rolling process knowledge graph: Maps relationships between critical variables (roll deformation, rolling force, billet temperature, defects) to form a dynamic data support system.
- Machine learning and simulation: Uses algorithms for feature extraction and dimensionality reduction to identify quality-driving parameters; deploys discrete element method (DEM) and finite element method (FEM) digital twins to simulate deformation, temperature, and stress distribution—optimizing processes for quality, efficiency, and energy use.
- Real-time defect prediction: Deploys sensors for stress, thickness, temperature, and surface defect detection; combines statistical process control (SPC) and deep learning (DL) models for early anomaly alerts.
5.2 Strategic Advantages
- Delivers high-precision process optimization (e.g., accurate thickness prediction and scratch detection).
- Enables real-time parameter adjustments to adapt to production changes, avoiding batch defects.
- Balances multi-objective optimization (quality, efficiency, energy consumption) for holistic performance.
5.3 Problems Solved
- Simplifies parameter optimization for complex, highly coupled rolling processes previously dependent on manual experience.
- Eliminates batch scrap caused by delayed offline defect detection.
- Mitigates quality issues (e.g., shape defects) resulting from roll deformation and temperature fluctuations.
5.4 Tangible Outcomes
- Reduces product defect rates by 15%+ and improves dimensional accuracy and surface quality.
- Shortens R&D cycles for new products by 20%, accelerating time-to-market for high-value offerings.
- Enables the development of high-end products (e.g., superalloys, high-strength steel) for specialized applications.
II. Future Trends in Long Product Rolling
The global long product rolling sector is poised for transformative growth, with four key trends shaping its evolution:
1. Deep Integration of Digital Twins & Large Language Models (LLMs)
Full-line digital twins will enable end-to-end virtual simulation of production processes. Combined with LLMs, “smart process advisors” will autonomously generate production recipes, troubleshoot in real time, and further reduce reliance on human expertise—driving unprecedented operational agility.
2. Net-Zero Carbon Transition
Direct rolling technologies will integrate with green power (solar, wind) and carbon capture, utilization, and storage (CCUS) systems. The “short-flow + direct rolling” model will become mainstream, pushing ton-steel carbon emissions toward net-zero and aligning with global climate goals.
3. Customized High-End Production
Full-process traceability and data-driven process optimization will enable low-volume, high-variety production of specialized long products. For example, tailored profiles for wind energy, nuclear power, and aerospace applications will be delivered with precise parameter matching and consistent performance—catering to niche market demands.
4. Cross-Industry Collaborative Connectivity
Industrial Internet platforms will link long product manufacturers with downstream customers (e.g., construction, equipment manufacturing). Real-time data sharing will enable demand-driven production, creating a closed-loop smart supply chain that optimizes “demand → production → delivery” workflows and enhances global supply chain resilience.
