Dynamic Prediction of NO<italic>_x</italic> Concentration at SCR Inlet for Organic Waste Liquid Incineration Based on MIC‒NNG‒LSTM

ObjectiveThis study proposes a dynamic prediction method based on MIC‒NNG‒LSTM that addresses the typical delay, nonlinearity, and dynamic characteristics present in the incineration and flue gas treatment of high-concentration saline organic waste liquids. The method predicts the NO<italic>_x</italic> concentration at the inlet of the selective catalytic reduction (SCR) denitration tower and solves the problem of the denitration system’s inability to adjust the ammonia injection amount in a timely manner when operating conditions change.MethodsFirstly, given the complex operation of the waste liquids incineration process, which involved strong coupling and high correlation among operating parameters, the prediction method examined in this study was based on the traditional Long Short‒Term Memory (LSTM) neural network as the foundational model. This model captured the temporal dependency in the input data and memorized and utilized information over extended periods, ensuring the dynamic temporal relationship between auxiliary variables and target variables throughout the modeling process. Secondly, variables that affected the NO<italic>_x</italic> concentration at the inlet of the Selective Catalytic Reduction (SCR) system, such as furnace temperature, air supply, and natural gas flow rate, exhibited nonlinearity and time lag. The Maximal Information Coefficient (MIC), being relatively insensitive to time lags, automatically identified the optimal delay time between variables in time series. Accordingly, this study adopted the MIC method to determine the delay time of relevant auxiliary variables, comprehensively capturing the dynamic relationships among variables. The variables affecting the NO<italic>_x</italic> concentration at the SCR inlet belonged to high-dimensional multi-feature variable data that contained a substantial amount of redundant information. This study applied MIC to reflect the importance of each input variable relative to the target variable, improved the Non-negative Garrote (NNG) algorithm capable of shrinking variable coefficients, and designed the MIC‒NNG algorithm to reduce the input node count of the LSTM network, eliminating redundant variables and achieving adaptive selection of auxiliary variables. Finally, the set of auxiliary variables, including delay time, was used as the input variable set for the LSTM network to establish a dynamic prediction model for the NO<italic>_x</italic> concentration at the SCR inlet. Experimental comparisons were conducted with three other methods for predicting NO<italic>_x</italic> concentration at the SCR inlet: LSTM, MIC‒LSTM, and NNG‒LSTM.Results and DiscussionsOnce the experimental results were analyzed and compared, the basic LSTM prediction model, which did not consider the time lag between input variables and the impact of redundant variables, exhibited significant fluctuations in its prediction fitting curve. When the NO<italic>_x</italic> concentration underwent drastic fluctuations, the prediction results displayed lags, indicating lower prediction accuracy. The MIC‒LSTM model, which applied the MIC algorithm to screen out nine relevant variables and estimate their delay times, achieved a better fit to the actual NO<italic>_x</italic> concentration values compared to the LSTM model, with reduced prediction lags. This confirmed that considering the delay time of auxiliary variables captured the fluctuation characteristics of NO<italic>_x</italic> concentration more effectively, and selecting highly correlated auxiliary variables contributed to improving prediction accuracy. The NNG‒LSTM model, which incorporated the NNG algorithm, reduced the number of auxiliary variables participating in the prediction from 19 to 13, though this was still four more variables than those selected by MIC. This indicated that neglecting the importance of auxiliary variables relative to the target variable increased the number of weakly correlated variables included in the prediction. Although the NNG‒LSTM model demonstrated better prediction fitting ability than the MIC‒LSTM model, its failure to consider the time delay of auxiliary variables on SCR inlet NO<italic>_x</italic> concentration resulted in prediction lags, demonstrating that considering the time delay of auxiliary variables affected prediction accuracy. The proposed MIC‒NNG‒LSTM model, which integrated the improved NNG algorithm, reduced the number of auxiliary variables participating in the prediction to 7, with more accurately selected relevant variables compared to the NNG‒LSTM model. This led to a fitting curve that closely matched the actual values, with improved prediction lags. The combination of MIC’s time delay estimation and MIC‒NNG’s variable selection allowed the prediction model to achieve higher accuracy and lower complexity. The prediction error distributions revealed that the LSTM model’s errors were relatively dispersed, while the MIC‒LSTM model’s errors were more concentrated. The NNG‒LSTM model’s error distribution was narrower than those of the LSTM and MIC‒LSTM models, and the MIC‒NNG‒LSTM model’s error distribution was the most concentrated. The standard deviations of the four models were 4.007 0, 2.679 3, 1.826 8, and 0.812 5, respectively, with the MIC‒NNG‒LSTM model showing the smallest standard deviation, further confirming its superior prediction capability. The performance indicators RMSE for the LSTM, MIC‒LSTM, NNG‒LSTM, and MIC‒NNG‒LSTM models were 3.552 2, 2.492 1, 1.875 6, and 1.567 4 mg/Nm³, respectively. The MAPE values were 0.007 9%, 0.004 7%, 0.004 2%, and 0.003 4%, and the A·R2(adjusted R-squared) values were 74%, 86%, 88%, and 93%, respectively. The MIC‒NNG‒LSTM model demonstrated the best performance among the four models in all indicators, confirming that it accounted for the impact of time delay on prediction results and eliminated redundant variables, achieving optimal prediction performance. This further highlighted the accuracy and effectiveness of the MIC‒NNG‒LSTM prediction model.ConclusionsThe results indicate that considering the delay time of input variables ensures the dynamic performance of the LSTM network while accurately expressing the nonlinear relationship between SCR inlet NO<italic>_x</italic> concentration and related auxiliary variables. The MIC‒NNG algorithm more accurately selects input variables than the NNG algorithm, shortening model prediction time and improving both prediction accuracy and generalization ability. The dynamic prediction model based on the MIC‒NNG algorithm and LSTM neural network comprehensively considers the delay characteristics of variables and the dynamic time-series relationships between parameters in the incineration process of organic waste liquid, which provides a new approach for reducing NO<italic>_x</italic> emissions.