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

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 effectively improve the utilization rate of electric energy, reduce the production cost, and achieve high efficiency and energy saving. Aiming to operation scenarios including normal operation of rectifiers, fault of random one rectifier, fault of random two rectifiers and number change of electrolytic tanks, the output current of each rectifier, 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 rectifier 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 verifies the effectivity 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.