Research on the Prediction and Control Model Optimization of End Carbon Temperature in Converter Steelmaking
In order to improve the production efficiency and economic benefits of converter steelmaking,an improved neural network learning extreme machine prediction model is proposed,and an improved particle swarm optimization algorithm is introduced as the optimization model for endpoint carbon temperature control.After research,it has been shown that there are 89 samples with a converter steelmaking temperature deviation of around 15 ℃,and the hit rate is 62.676％.There are 105 samples with a carbon content deviation w(C)of around 0.015,with a hit rate of 72.112％.It can be seen that the effectiveness of prediction and control models in controlling the endpoint carbon temperature of converter steelmaking and reducing energy consumption are of great significance.
NETL
NSTL
万方数据
