Journal
METALLURGICAL RESEARCH & TECHNOLOGY
Volume 118, Issue 6, Pages -Publisher
EDP SCIENCES S A
DOI: 10.1051/metal/2021074
Keywords
data conversion; convolutional neural network; lime utilization ratio; Kambara Reactor desulfurization; prediction
Categories
Funding
- National Natural Science Foundation of China [U1960202]
- China Post-doctoral Science Foundation [2019M651467]
- Natural Science Foundation Joint Fund Project of Liaoning Province [2019-KF-25-06]
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Through data-driven technique and convolutional neural network, the lime utilization ratio in desulfurization process can be accurately predicted. Sensitivity analysis shows that lime weight and initial sulfur content have significant effects on the lime utilization ratio.
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%.
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