基于卷积神经网络的烧结成品率预测OA北大核心CSTPCD

Sintering yield is a comprehensive indicator that reflects the production,quality and energy consumption in sintering processes.Addressing the lag in sintering yield detection,the cross-sectional image of the sintering machine tail was used as input and a convolutional neural network(CNN)was employed to fit the relationship between the cross-sectional image and sintering yield,thereby online prediction of sintering yield could be realized.According to the characteristics of infrared images of sintering tail section,a CNN with a DenseNet architecture was utilized for modeling.The network structure was enhanced with multi-scale dense blocks to extract information from images of various sizes within the same layer.By fitting the relationship between high-dimensional image feature information and sintering yield,accurate predictions of the sintering yield were achieved.The model was validated by using historical cross-sectional images and sintering yield data from a major domestic steel production company.The results show that the proposed improved DenseNet network had strong fitting and generalization ability in the sintering yield prediction problem.Defining the absolute error between the predicted and actual yield values in the interval range of±2.8%as the hitting target,the model hitting rate reached 92.66%,and the root mean sguare error was only 1.76%,which could provide a basis for the optimization of the production process parameters.