Research On Optimization Model Based On Electrolytic Copper

Over the past century since its founding,the Communist Party of China has led all the Chinese people in great socialist construction and reform.China’s economic and social development has made remarkable take-off and achievements,creating a"Chinese miracle"in industrial development.Reducing production costs and realizing high efficiency and energy saving have become the goal of contemporary industrial development.With the continuous development and progress of human society,copper and its alloy materials have become indispensable and important materials in people’s production and life.The rapid growth of China’s demand for copper products has led to the rapid growth of energy consumption of copper smelting in industrial production.As a main link in the copper smelting process,electrolytic process will directly affect the quality,production energy consumption and efficiency of copper products.Copper electrolysis is a very energy consuming process.If the technical management is not in place,it will reduce the current efficiency,increase the power consumption per ton of copper and increase the cost of electrolytic copper.However,at present,there is no very effective solution to reduce power consumption.Firstly,this paper analyzes the electrolysis process in the copper smelting process,explores several main factors affecting the energy consumption in the electrolysis process,and finds that the voltageand current efficiency of the electrolytic cell to evaluate the energy consumptionηAnd DC power consumption W are mainly affected by the concentration of sulfate ion(SO₄^2-),copper ion(Cu²⁺)and current density D_k fluctuation,and then put forward the neural network method in the energy consumption prediction of electrolytic copper process.Then,the BP neural network and beetle antennae search algorithm are improved,and the beetle antennae search algorithm based on quadratic interpolation（QIBAS）is proposed to optimize the initial weight of BP network,make it closer to the extreme point and further speed up the optimization speed.Finally,based on QIBAS-BP neural network,the energy consumption prediction model of electrolytic copper process is established,and the model is trained and tested by using the production data in the actual industry.The intelligent integrated model can realize the efficiency of voltageand current of electrolytic cell with high accuracyηAnd DC power consumption W.the prediction accuracy meets the requirements of industrial production,and the minimum DC power consumption w can be used as the concentration of sulfate ion(SO₄^2-),copper ion(Cu²⁺)and current density D_k parameter optimization operation provides an important reference,so as to reduce industrial production costs,achieve high efficiency and energy saving in production links,and contribute to the construction of beautiful China.