Control of high-power electric arc furnace in metallurgical industry

In response to the challenges of controlling high-power electric arc furnaces in the metallurgical industry, an intelligent integrated control strategy combining fuzzy control neural networks with multi-objective optimization decision-making has been proposed. This approach first implements a variable-structure fuzzy neural network for the temperature outer loop, 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 model is used to achieve a balanced system performance. Researchers both domestically and internationally have extensively studied control systems with large time delays, particularly those involving electrode lifting to regulate furnace temperature. The goal is to minimize energy consumption and maximize power factor. However, due to the complex and variable nature of electrode control in electric arc furnaces, there is always a certain level of interdependence among the controlled variables. As a result, it is difficult to establish an accurate mathematical model for the system, which faces nonlinear, time-varying, and strong mismatch characteristics. Conventional control methods struggle to effectively manage such complex systems. Fuzzy control, based on fuzzy logic theory, uses fuzzy language and rules to describe system dynamics and performance indicators. It can effectively handle pure time delays and parameter variations, making it suitable for controlling uncertain and highly nonlinear systems. Neural network-based control, on the other hand, offers strong learning capabilities and the ability to process quantitative data directly. Through offline training and online learning, the controller can self-adjust and adapt, significantly improving real-time control performance. To address these challenges, the author proposes an intelligent integrated control scheme that combines fuzzy control neural networks with multi-objective optimization. This method utilizes a dual-loop structure: a temperature control outer loop and a current balance inner loop, enabling automatic control of both furnace temperature and three-phase electrode current. The temperature control outer loop employs a variable-structure fuzzy neural network controller. Based on real-time temperature and its changing trend, it generates the optimal electrode current command signal for the inner loop. The objective is to ensure fast current tracking while minimizing electrode current fluctuations and considering overall energy loss. Meanwhile, the multi-objective fuzzy optimization decision-making approach helps achieve balanced three-phase electrode current control. The neural network within the controller is implemented using a layered feedforward architecture. To overcome the limitations of traditional fuzzy logic control, where rules may not adapt well to process changes, the variable-structure fuzzy neural network enhances the system’s self-learning and self-adaptive capabilities. During the learning process, the number of hidden layer nodes can be dynamically adjusted, improving convergence speed and avoiding local minima. This allows the fuzzy rules to evolve from coarse to fine, enhancing system adaptability. In multi-objective fuzzy optimization, each target strives for optimality, but trade-offs are often necessary. The system aims to find a feasible solution that improves some goals without worsening others. This is achieved by first determining the optimal values for individual targets, then fuzzifying them, and finally identifying the solution that maximizes the intersection of their membership functions. The inner loop of the high-power electric arc furnace temperature control focuses on fast current tracking and minimal electrode fluctuation, while also considering process requirements and energy efficiency. Therefore, the inner loop is a multi-objective control system. Its optimization goals include balancing three-phase currents, minimizing adjustment time, and reducing energy loss. A fuzzy language model is used to solve the optimized objective function for the three-phase electrode system, forming a fuzzy target minimum set and constructing a fuzzy decision function. A weighting coefficient is applied to transform the multi-objective problem into a single-objective optimization, leading to the best electrode motion control command. This ensures the timely and effective application of the intelligent integrated control system in high-power electric arc furnace operations. In conclusion, push or pull production systems yield different results. A push system may increase work-in-progress inventory and disrupt production flow, but it can boost output rates and equipment utilization. Conversely, a pull system reduces inventory and buffer space but may lower output and resource utilization. In practice, companies should evaluate the pros and cons of both systems and integrate them to improve efficiency and reduce costs.

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