Optimisation Of The Dosing Process In Oxygen-rich Bottom-blow Copper Melting

The oxygen-rich bottom-blowing furnace is an advanced melting furnace with a compact structure,strong adaptability to raw materials,high melting efficiency and in line with current environmental requirements.In practice,the process is influenced by the unstable source of copper ore,raw material inventory,production process,quality indicators and other factors,resulting in a very complex dosing process for the whole oxygen-rich bottom-blowing copper melting process.Therefore,it is important to optimise the dosing process of oxygen-rich bottom-blown copper smelting and to determine a reasonable dosing plan to improve product quality,reduce production costs and stabilise the production process.This paper takes the oxygen-rich bottom-blown copper melting dosing problem of a copper plant as the research background,according to the actual production situation,combined with the copper melting production process to optimize the dosing problem,the main research content of the paper is as follows:(1)Batching is the immediate pre-process of oxygen-rich bottom-blown copper melting.In view of the complexity of the batching process of oxygen-rich bottom-blown copper melting,an in-depth analysis of the batching process was carried out to establish a batching optimisation model with the objective of reducing the cost of batching and improving the quality of copper matte.The results show that the improved NSGA-Ⅱalgorithm can reduce production costs,improve product quality and provide decision makers with multiple dosing solutions,increase the fault tolerance of the dosing process and improve the production efficiency in solving the multi-objective optimization problem of copper melting in oxygen-rich bottom-blowing furnace.(2)Copper matte grade is a key process control parameter in the oxygen-rich bottom-blown copper melting process.In response to the problems of difficulty in realtime detection of copper matte grade,long lag time of detection results and lag time in guiding the optimization of production process parameters,a copper matte grade prediction model based on FA-PSO-RBF neural network was proposed based on indepth mining and processing of production data,and the accuracy of the model was verified through actual production data.The results show that the copper matte grade prediction model proposed in this paper can provide a good prediction effect on the copper matte grade in the actual production process,which provides a method for the optimal control of the parameters of the oxygen-rich bottom-blown copper melting process.(3)In view of the complexity of the oxygen-rich bottom-blown copper melting process,in order to optimise the dosing scheme in a more real-time and accurate manner and to ensure the stable operation of the whole production process,a planning and design scheme for an oxygen-rich bottom-blown copper melting dosing optimisation system is proposed to re-adjust and optimise the dosing process based on various types of data from actual production,so as to further reduce the cost of the enterprise and improve the quality and production efficiency of the product.