Prediction of Inlet NOx Concentration in SCR Denitration System Based on Variational Mode Decomposition and SSA-LSTM
The measurement of NOx concentration at the SCR inlet of coal-fired power units has the characteristics of significant delay,complex influencing factors,and high volatility,often unable to accurately reflect the real-time changes in NOx concentration.A predictive model for SCR inlet NOx concentration based on variational mode decomposition(VMD)and SSA-LSTM is proposed to address the aforementioned issues.Firstly,the variational mode decomposition method is used to decompose the NOx concentration at the SCR inlet,and the mutual information selection algorithm selects auxiliary variables that are strongly correlated with the target variable.Then,the SSA algorithm is used to optimize the LSTM neural network parameters and construct an SSA-LSTM prediction model.Finally,conduct simulation comparative experiments between VMD-SSA-LSTM,LSTM,and VMD-LSTM.The results indicate that the VMD-SSA-LSTM prediction model has higher prediction accuracy,smaller errors,and stronger generalization ability.
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