Research on semi-supervised soft sensor modeling method for sulfur recovery unit based on ISSA-VMD-ESN

Sulfur recovery unit (SRU) in the refining industry can effectively reduce sulfide emissions and achieve efficient use of resources. Predicting sulfide emissions with soft sensors can provide the basis for process optimization and control, to improve SRU's stability and efficiency. However, due to the dynamicity and nonlinearity of the sulfur recovery process, existing soft sensor models have poor prediction accuracy. Therefore, this paper proposes a semi-supervised soft sensor modeling method based on variational mode decomposition (VMD), echo state network (ESN) and improved sparrow search algorithm (ISSA). Firstly, Reconstructing SRU dataset using semi- supervised fusion method to reduce the impact of dynamicity on prediction results. Secondly, through the ISSA to optimize the VMD parameters and using the optimized VMD to decompose the output sequences into multiple components, to reduce the nonlinearity of sequences. Then, the soft sensor modeling of each component using ESN. Additionally, the ESN parameters are optimized by ISSA to prevent the effect of improper parameter settings on the model's prediction performance. Afterwards, the predicted values of each component are superimposed to obtain the final prediction. Finally, simulation experiments are conducted based on the SRU dataset of an oil refinery to verify the effectiveness and generalization of the proposed method.