Performance and Statistical Assessment of Deep Learning Frameworks for Low-Temperature Selective Catalytic Reduction...

Nitrogen oxides (NO _x ) are among the most harmful emissions produced by internal combustion engines, contributing to air pollution, acid rain, and climate-related impacts. Accurate modeling of the NO _x reduction process is therefore essential for developing efficient emission control technologies and optimizing catalytic performance. This study explores the potential of advanced deep learning architectures to predict NO _x conversion efficiency in an ethanol-assisted selective catalytic reduction (SCR) system. Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and a hybrid TCN-LSTM were designed and optimized using Bayesian optimization to obtain the most effective hyperparameter configuration. Experimental data collected from a two-cylinder diesel engine equipped with Ag-Sn-P-based catalysts were used for model training and validation. Model performance was evaluated through Mean Absolute Error (MAE) and Theil's U₂ indicators, supported by bootstrap confidence interval analysis to ensure statistical robustness. Among the tested models, the TCN achieved the best predictive accuracy, with a mean absolute error of 0.118 and a Theil's U₂ value of 0.051, followed closely by the hybrid TCN-LSTM model with an MAE of 0.132 and U₂ of 0.053. The results confirm that convolutional and hybrid architectures provide reliable and generalizable frameworks for capturing the nonlinear dynamics of catalytic NO _x reduction, offering valuable insights for next-generation emission control strategies.