有色设备

The oxygen injector in the bottom blowing furnace is an immersion oxygen injector, and its gas state of outlet plays a decisive role in the melting process of the bottom blowing furnace, but the outlet information of the oxygen injector cannot be directly detected. In this paper, the numerical simulation research method is used, and the numerical simulation is combined with machine learning, the orthogonal experiment is designed, and the matrix analysis method is used to calculate and predict the outlet information of the oxygen injector. The effects and weights of the gas flow, the angle of the oxygen injector and the liquid level of the bottom blowing furnace on the outlet velocity, outlet pressure and outlet temperature of the oxygen injector were studied. The results show that the gas flow rate has the greatest comprehensive influence on the outlet state of the oxygen injector, while the inclination angle of the oxygen injector has the least effect, and the gas flow state of the outlet of the oxygen injector is the best under the conditions of high flow rate, high liquid level and low inclination angle of the oxygen injector. At the same time, the KNN algorithm is used to establish a fast prediction model, and the results show that the regression coefficient R2 of the indicators of the predicted working conditions and the actual calculated working conditions can reach 0.998, and the prediction accuracy is high, and the prediction model is accurate and reliable.