Research On Power Supply Strategy And Energy Consumption Optimization Of High Power Electrolytic Copper Rectifier Units

In recent years,copper and its alloy materials are widely used in various fields of daily life.With the increase of the number and capacity of high-power electrolytic copper rectifier units,the energy consumption in industrial production increases rapidly.However,the proposed method cannot effectively reduce the power consumption.Therefore,based on the analysis of electrolytic copper production process,power supply of rectifier units and rectifier device,this thesis proposes a power supply strategy and energy consumption optimization scheme of high-power rectifier units to effectively improve the power utilization rate,reduce the production cost and achieve high efficiency and energy saving.This thesis mainly researches on the equipment and production technology of50000 tons of electrolytic copper rectifier in an electrolytic copper enterprise in Shandong Province.The electrolytic copper rectifying equipment is two sets of 12 pulse double star non-homogeneous reverse parallel rectifier units running in parallel.The research contents of this thesis are as follows:Firstly,this thesis introduces the current research stations both at home and abroad,the production process of electrolytic copper,and studies the technical indicators of electrolytic copper.After comparative analysis of the rectifier circuit structure,based on the double anti star rectifier circuit,this thesis designs the main circuit of the rectifier units and gives the system design indicators.Secondly,the existing power supply strategies of rectifier units: expert system,Genetic Algorithm(GA)based on simulated annealing algorithm and hierarchical multi-objective Particle Swarm Optimization(PSO)strategy are analyzed.According to the shortcomings of each algorithm,the power supply optimization strategy of BP neural network,BP neural network based on Particle Swarm Optimization(PSO-BP)and BP neural network based on Genetic Algorithm(GA-BP)are proposed.Through comparative analysis,GA-BP neural network algorithm has the highest prediction accuracy,so this thesis uses GA-BP to realize the optimal power supply strategy.The power supply strategy mainly considers the normal operation of the rectifier units,one rectifier fault,two rectifiers fault and the change of the number of electrolytic tanks.GA-BP neural network is used to predict the rectifier output current,transformer gear and transistor control angle in each case.Thirdly,the prediction accuracy of BP neural network,PSO-BP neural network and GA-BP neural network is compared in the aspect of energy consumption optimization algorithm in electrolysis process,and the prediction error of GA-BP neural network is still the smallest.The main process parameters such as sulfate ion concentration,copper ion concentration and current density were selected as input variables,and the current efficiency,electrolytic tank voltage and DC unit consumption were predicted by GA-BP neural network.Then,the energy consumption of the rectifier unit under four conditions is predicted,and the minimum value of DC unit consumption is quickly found through GA,and the corresponding sulfate ion concentration,copper ion concentration and current density are obtained.Finally,the obtained optimal current density is fed back to the rectifier power supply system,and the optimal current density is the given current.Combined with the electrolytic tank voltage and electrolytic tank temperature as the feedback of the whole units power supply strategy,the simulation verifies that the output optimal current of each rectifier under four conditions of the rectifier units that is the steady current accuracy in each case is stable between 0.001 and 0.002,Therefore,the power supply strategy and energy consumption optimization method proposed in this thesis realize the closed-loop control of optimal power supply and minimum energy consumption in the production process of high-power electrolytic copper rectifier units.