|
| Research on Prediction Model of Fe Content in Slag during Copper Converter Slag-Making Period Based on Image Recognition |
|
Received:November 14, 2021
Revised:November 18, 2021
Accepted:November 22, 2021
Published Online:March 15, 2022
|
| View Full Text View/Add Comment Download reader |
| DOI：doi：10.3969/j.issn.1007-7545.2022.04.003 |
| KeyWord:copper converter blowing; image recognition; convolutional neural network; support vector machine; end point judgment |
| Author | Institution |
| ZHANG Ran |
江西赣州，江西理工大学材料冶金化学学部 |
| LI Ming-zhou |
江西赣州，江西理工大学，材料冶金化学学部 |
| ZHONG Li-hua |
内蒙古赤峰，赤峰金通铜业有限公司 |
| TONG chang-ren |
江西赣州，江西理工大学材料冶金化学学部 |
| HE Fa-you |
福建龙岩紫金铜业有限公司 |
| HUANG Jin-di |
江西赣州，江西理工大学材料冶金化学学部， |
|
| Hits: 1751 |
| Download times: 1808 |
| 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%. |
| Close |
|
|
|