Abstract:Copper converter blowing is the key process of pyrometallurgical copper smelting. Its end point judgment is closely related to furnace life, copper yield and direct yield. At present, the existing end point judgment methods such as manual experience, instrument measurement and material balance method have some limitations. Theoretically, the end point of copper converter slag blowing period is related to whether Fe content in the slag meets the standard, and the slag samples with different Fe content show different image features. In view of this, based on feature vector extraction principle of graphic recognition, the prediction model of Fe content in copper converter slag during slag blowing period is constructed by using convolution neural network (CNN) algorithm and support vector machine (SVM) algorithm respectively, It lays a digital and analog foundation for application of image recognition technology in judgment of blowing end point of copper converter. The instance analysis of two models shows that the prediction accuracy of training set of convolutional neural network is 98%, and the prediction accuracy of test set is about 50%; the prediction accuracy of training set of support vector machine model is 99%, and the prediction accuracy of test set is 62%.