Research on CNN-LSTM coupling model for chemical process early warning
In consideration of the real-time,multi-dimensional,and nonlinear nature of chemical process parameters,as well as the complexity of chemical processes with numerous mutually interfering factors and single warning method,this work proposes an early warning method combining deep learning regression prediction and ADF(Augmented Dickey-Fuller)test.For monitoring and early warning analysis of over-temperature abnormal conditions in condensation reactions,convolutional neural network,and long short-term memory(CNN-LSTM)models are employed in this study to predict crucial process parameters for the next 400 s.Simultaneously,the ADF test is utilized to examine the trend of temperature time series parameters.When the result is an unstable trend and the CNN-LSTM model predicts that the temperature will exceed the alarm threshold at a specific time point,security personnel will be alerted accordingly.The results showed that during the condensation reaction's over-temperature anomalies at feed rates of 700 and 800 kg/h,the CNN-LSTM model's regression forecasting for temperature metrics manifested R2 values of 0.9827 and 0.9882.Correspondingly,the model elicits RMSE(Root Mean Square Error)values of 0.1425 and 0.1453,and MAE(Mean Absolute Error)values of 0.1184 and 0.1234.These indices testify to the model's exceptional fidelity and precision,surpassing the conventional LSTM model's predictive accuracy as reflected in its R2,RMSE,and MAE values.The ADF test results on the temperature time series data corroborate the presence of an unstable trend,aligning with the actual process behavior.By combining both methods,the early warning model is able to detect temperatures exceeding the alarm threshold 18 and 16 s earlier than the simulated alarm point,respectively,and issues a timely alert.The dual application of these methods provides a robust means of monitoring chemical process parameters,enabling the early detection of abnormal conditions in chemical processes and advancing the field of chemical process parameter monitoring.
safety engineeringchemical processesmonitoring and warningdeep learningADF test
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