首页|A Data-driven Model to Reduce Outlet NOx Concentration of SCR System in FCC Unit
A Data-driven Model to Reduce Outlet NOx Concentration of SCR System in FCC Unit
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NETL
NSTL
万方数据
Samples(25500)were collected from a selective catalytic reduction(SCR)denitrification system in a fluid catalytic cracking unit and preprocessed using the quartile method and the K-nearest neighbors interpolation method to remove outliers.Using the Pearson correlation coefficient and LightGBM feature score method,13 key operational variables were identified and used to establish a model to predict outlet nitrogen oxide(NOx)concentration in an SCR system with backpropagation neural network,long short-term memory(LSTM)and LSTM-attention fully connected(FC)model,respectively.The LSTM-attention FC model showed better accuracy and generalization capability compared with other models.Its mean square error,mean absolute error,and coefficient of determination on the training and test datasets were 11.32 and 12.51,3.65%and 3.97%,and 0.96 and 0.94,respectively.Furthermore,a combination of the LSTM-attention FC model with a genetic algorithm used to optimize four feature variables including ammonia pressure compensation,inlet pressure,gas inlet upper temperature,and outlet ammonia concentration.The outlet NOx concentration could be controlled below 80±3 mg/m3,and the ammonia slip concentration could be controlled below 0.1 mg/m3,demonstrating that the optimization model can provide effective guidance for reducing NOx emissions and ammonia slip of SCR systems.
NETL
NSTL
万方数据
