| Issue |
E3S Web of Conf.
Volume 406, 2023
2023 9th International Conference on Energy Materials and Environment Engineering (ICEMEE 2023)
|
|
|---|---|---|
| Article Number | 04028 | |
| Number of page(s) | 4 | |
| Section | Geographic Remote Sensing Application and Environmental Modeling | |
| DOI | https://doi.org/10.1051/e3sconf/202340604028 | |
| Published online | 31 July 2023 | |
Soft Sensor Modeling Method for Sulfur Recovery Process Based on Long Short-Term Memory Artificial Neural Network (LSTM)
Qingdao Jeri Automation Co., LTD., Shenzhen Road, Qingdao City, China.
In the process of sulfur recovery, H2S and SO2 concentrations reflect the effectiveness and reliability of the recovery process. However, the concentration of H2S and SO2 in the process of sulfur recovery is difficult to be measured by online analysis instrument, so the soft sensing modeling method is often used to analyze the system. Because SRU system has strong nonlinear characteristics and dynamic process characteristics,traditional soft sensing modeling methods are often limited in use. Long Short-Term Memory (LSTM)Artificial neural networks show strong ability in processing nonlinear data and dynamic data. Therefore, LSTM soft sensing method is used in this paper to systematically analyze the sulfur recovery process.
© The Authors, published by EDP Sciences, 2023
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