China Nonferrous Metallurgy

The electricity consumption of electrolytic copper foil production process accounts for about 60% of energy consumption for the whole copper foil production, and there is a lot of energy saving space. The energy consumption of copper electrolytic process is directly related to the tank voltage and current efficiency of electrolytic process. However, complex influencing factors and serious coupling of process parameters in copper electrolytic process make it difficult to model energy consumption in copper electrolytic process, and the control of energy consumption in copper electrolytic production process is in a “blind” state, which is difficult to operate in the optimal energy consumption condition. Therefore, a high-precision fitting method based on Random Forest regression model was proposed in the paper to establish an energy consumption model of copper electrolytic process, and a Random Forest regression model was established to characterize the internal relationship between current density, sulfuric acid concentration, copper ion concentration and electrolytic temperature as input variables and electrolytic energy consumption. The problem of energy consumption modeling in copper electrolysis process is solved. According to the established objective function (Random Forest regression model of energy consumption) and the constraint conditions of electrolytic process, the improved competitive group optimization algorithm was used to solve the optimal process parameters of electrolytic process, which reduced the energy consumption of copper foil production from 5400kW·h/t before optimization to 4850kW·h/t. It greatly reduces the production cost of enterprises and effectively improves the production efficiency of enterprises.