A new power supply strategy for high power rectifying units in electrolytic copper process

다국어 초록 (Multilingual Abstract)

For achieving the minimum energy consumption in electrolytic copper process, this paper proposes a power supply optimization strategy based on the improved BP neural network for high-power electrolytic copper rectifying units to eff ectively improve the utilization rate of electric energy, reduce the production cost, and achieve high effi ciency and energy saving.
Aiming to operation scenarios including normal operation of rectifi ers, fault of random one rectifi er, fault of random two rectifi ers and number change of electrolytic tanks, the output current of each rectifi er, transformer gears and control angle of thyristor are obtained under these four scenarios by the proposed power supply strategy. The simulation results indicate that compared with BP neural network and PSO optimizing BP(PSO-BP)neural network, the prediction error of power supply strategy of GA optimizing BP (GA-BP) neural network is the minimum. Consequently, the optimal control of the output current of each rectifi er is obtained by using GA-BP neural network, and the stabilized current precision of total output current can be kept at 0.003–0.005, which verifi es the eff ectivity and feasibility of the proposed power supply optimization strategy, which provides valuable guidance and reference for the future design of high-power power supply system in electrolytic copper or other electrolytic metals.

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