Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression (MLR), support vector regression (SVR), and extreme learning machine (ELM) and the proposed ELM models of online sequential ELM (OS-ELM) and OS-ELM with forgetting mechanism (FOS-ELM) are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process. The ELM model exhibites the best performance compared with the models of MLR and SVR. OS-ELM and FOS-ELM are applied for sequential learning and model updating. The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500, with the smallest population mean absolute relative error (MARE) value of 0.058226 for the population. The variable importance analysis reveals lime weight, initial P content, and hot metal weight as the most important variables for the lime utilization ratio. The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight. A prediction system based on FOS-ELM is applied in actual industrial production for one month. The hit ratios of the predicted lime utilization ratio in the error ranges of +/- 1%, +/- 3%, and +/- 5% are 61.16%, 90.63%, and 94.11%, respectively. The coefficient of determination, MARE, and root mean square error are 0.8670, 0.06823, and 1.4265, respectively. The system exhibits desirable performance for applications in actual industrial production.