Control of high-power electric arc furnace in metallurgical industry

In response to the control challenges of high-power electric arc furnaces in the metallurgical industry, an intelligent integrated control strategy combining fuzzy neural network control with multi-objective optimization decision-making has been proposed. This approach begins by designing a temperature outer loop controller using a variable structure fuzzy neural network, which generates the current command signal for the three-phase electrode current balance inner loop. Within the inner loop, multiple optimization objectives are integrated into a unified framework, and a multi-objective fuzzy optimization method is applied to achieve a balanced system performance. Research on electric arc furnace control has long focused on managing systems with significant time delays, often through electrode lifting mechanisms to regulate furnace temperature. The goal is to reduce energy consumption and improve power factor. However, due to the complex and dynamic nature of electrode control, the system exhibits strong nonlinearity, time-varying characteristics, and large time delays, making it difficult to establish an accurate mathematical model. Traditional control methods struggle to provide effective solutions under these conditions. Fuzzy control, based on fuzzy logic theory, offers a robust solution by describing system dynamics and performance using linguistic rules. It effectively handles pure time delay and parameter variations, making it suitable for nonlinear and uncertain systems. Meanwhile, neural network-based control provides strong learning capabilities and the ability to process quantitative data. Through offline training and online adaptation, neural networks enable self-adjustment and real-time responsiveness, significantly enhancing control performance. To address these challenges, the author proposes an intelligent integrated control scheme that combines fuzzy neural networks with multi-objective optimization. This system uses a dual-loop structure: a temperature control outer loop and a current balance inner loop, enabling automatic regulation of both temperature and electrode current balance. The temperature outer loop employs a variable structure fuzzy neural network controller, which adjusts the electrode current command signal based on real-time temperature and its trend. The objective is to minimize electrode current fluctuations while considering energy loss. In the inner loop, a multi-objective fuzzy optimization approach is used to balance the three-phase electrode currents, ensuring stable and efficient operation. A feedforward neural network architecture is implemented in the controller, with the variable structure fuzzy neural network enhancing the system's self-learning and adaptive capabilities. During the learning process, the number of hidden layer nodes can be adjusted, improving convergence speed and avoiding local minima. This allows for a gradual refinement of fuzzy rules from coarse to fine, enhancing overall performance. In multi-objective systems, achieving optimal performance for all goals simultaneously is challenging, as one target may conflict with another. The multi-objective fuzzy optimization approach aims to find a satisfactory solution that balances these trade-offs. By fuzzifying individual optimal values and maximizing their intersection, the system identifies a feasible solution that improves multiple objectives without compromising others. The current balance inner loop focuses on fast tracking of the current command and minimizing electrode fluctuations, while also addressing energy efficiency and process requirements. This makes the inner loop a multi-objective information and control system. An optimized objective function is used to determine the best control strategy, with a fuzzy language model helping to form a minimum set pair and construct a fuzzy decision function. Weighting coefficients are applied to transform the multi-objective problem into a single-objective optimization task, leading to an optimal electrode movement control command. This ensures timely and effective implementation of the intelligent control strategy in high-power electric arc furnace systems. In conclusion, whether using a push or pull production system, each approach has its own advantages and disadvantages. A push system can increase output and resource utilization but may lead to excess inventory and process inefficiencies. A pull system reduces inventory and buffer space but may result in lower equipment utilization. Companies should evaluate their specific needs and integrate both approaches to enhance efficiency and reduce costs.

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