Tiangong · AiCS Artificial Intelligence Submerged Arc Furnace System

Tiangong · AiCS collects key variables during furnace operation, including current, voltage, charge layer thickness, electrode position, material temperature, flue gas temperature, charging interval time, etc., to build a multidimensional historical furnace operation database. By integrating deep learning, model ensemble techniques, and feature selection algorithms, the system establishes a "smelting success rate prediction model", an "abnormal trend detection model", and an "optimal charging parameter recommendation model".

Tiangong · AiCS system receives real-time data streams from the furnace DCS control system, weighing system, electrical control system, and batching system. The AI model outputs optimal recommendations for the current operating stage, including charging intervals, charging quantity, electrode movement recommendations, and power control curves. The system supports both "recommendation mode" and "automatic control mode" to interface with the control system, enabling semi-closed-loop or fully closed-loop control.

All control strategies in Tiangong · AiCS are generated through algorithmic calls without displaying specific process logic. The system adopts hierarchical permission management, allowing only authorized personnel to access model structures. Meanwhile, process data is encrypted and export functions are restricted to protect intellectual property rights.

Tiangong · AiCS system continuously collects feedback from each tapping, including composition qualification rate, energy consumption, and production output. It automatically evaluates model prediction deviations and periodically updates model weights and feature sets. The system also supports manual data labeling to assist reinforcement learning and strategy optimization.

The system replaces experience-based manual furnace operation, improving experimental efficiency and repeatability, reducing trial-and-error costs, and accelerating process optimization and iteration.

The system enables optimization of smelting power, increased production capacity, and energy consumption control. By integrating with MES systems, it creates a "closed-loop digital factory" from task assignment to operation execution, while reducing the number of operators and promoting simplified operation and remote management.

Through its black-box operation mode, the system conceals specific process details such as batching formulas, electrode strategies, and temperature control curves. It can be deployed across multiple furnace types, enabling technology output while preventing knowledge leakage.

Headquarters can monitor model performance and quality indicators across different plants without interfering with specific control details, ensuring both process independence and operational security for each plant.

Displays real-time current, voltage, charging frequency, process stage, thermal imaging dynamics, and other operating parameters for each furnace.

Dynamically displays batch formulas, weighing curves, raw material flow direction, charging sequence, and related information.

Shows real-time AI control recommendations and prediction values, model confidence levels, feedback results, and other related data.

Displays trend charts of finished product composition for each tapping, qualification rates, abnormal batch distribution, and related quality indicators.

Includes daily/weekly/monthly production output, energy consumption trends, furnace operation efficiency statistics, and key performance indicator dashboards.

Displays alarm status of each control point, video surveillance interfaces, equipment operating temperature/load conditions, and other safety-related information.

Displays intelligent process animations, achievement showcases, cooperation case studies, visitor guidance interfaces, and other promotional content.