Collaborative optimization model of blast furnace raw materials and operating parameters based on intelligent calculation

Abstract

Aiming at the problem of coadjustment of blast furnace raw materials and operation parameters, this paper proposes a cooptimization model of blast furnace batching that integrates Random Forest and NSGA-Ⅲ (Non-dominated Sorting Genetic Algorithm III) algorithm. First, blast furnace field data were collected for a two-year time span, and a predictive model for CO² emissions and blast furnace permeability was constructed using the Random Forest algorithm; taking the goodness of fit (R²), mean square error (MSE) and mean absolute error (MAE) as the evaluation indexes, the R² of the two prediction models obtained reached 0.93 and 0.96 respectively, and the MSE and MAE tended to be close to the zero value. Then, NSGA-Ⅲ was used to establish the blast furnace batching optimization model to optimally solve the batching scheme and the corresponding blast furnace operating parameters by taking the lowest batching cost, the lowest carbon dioxide emission and the maximum blast furnace permeability as the objective function, and the composition requirement of raw materials and the range limitation of operating parameters as the constraints; finally, the model was validated using the actual on-site data, and the application results showed that the output of the model conformed to the Finally, the results show that the model output meets the composition requirements and obtains a lower-cost dosage scheme than the original dosage ratio; moreover, this scheme corresponds to a blast furnace with less carbon dioxide emission, better blast furnace permeability and less slag. Therefore, the model can provide an effective reference for field operators to optimize blast furnace batching and operation.