Metall. Res. Technol.
Volume 118, Number 6, 2021
|Number of page(s)||11|
|Published online||05 October 2021|
A convolutional neural network-based model for predicting lime utilization ratio in the KR desulfurization process
State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai 200444, PR China
2 State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, PR China
* e-mail: email@example.com
Accepted: 1 September 2021
In the presented work, desulfurization process parameters and the lime utilization ratio were correlated by data-driven technique, and a convolutional neural network was applied to predict the lime utilization ratio in the Kambara Reactor (KR) desulfurization process. The results show that compared with the support vector regression model and random forest model, the convolutional neural network model achieves the best performance with correlation coefficient value of 0.9964, mean absolute relative error of 0.01229 and root mean squared error of 0.3392%. The sensitivity analysis was carried out to investigate the influence of process parameters on the lime utilization ratio, which shows that the lime weight and the initial sulfur content have the significant effect on the lime utilization ratio. By analyzing the influence of the lime weight on the lime utilization ratio under the current desulfurization process parameters, it can be concluded that decreasing the lime weight from 3256 kg to 2332 kg can increase the lime utilization ratio by about 5%.
Key words: data conversion / convolutional neural network / lime utilization ratio / Kambara Reactor desulfurization / prediction
© EDP Sciences, 2021
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