有色设备

The bottom-blowing continuous treatment for lead-based solid waste has the characteristics of multivariability, nonlinearity, strong coupling and large lag, which cause difficulties for mechanism based modelling and optimization. To solve these problems, this paper proposes a data-driven raw material blending model for the smelting furnace, which achieves optimized control for key operating parameters. Firstly, based on laboratory and process historical data, the relationship between raw material composition and key process indicators of the smelting furnace is established by applying neural network; on this basis, the Particle Swarm Optimization algorithm is applied to solve the optimal ratio of each component in the raw material from the ideal operating conditions; finally, the ingredient problem is formulated as a multi-objective optimization problem with nonlinear constraints and then solved by SLSQP. Integrating the above modeling and optimization algorithms, a corresponding raw material management system has been developed.