An Efficient Control Strategy for Denitrification Systems Using a Hybrid Model and CSGA to Enhance Performance

Flue gas denitrification is a critical process in industrial pollution control, essential for achieving environmental sustainability. The central objective of denitrification system control is to precisely regulate the ammonia flow rate while ensuring consistent compliance with nitrogen oxide (NOx) emission standards, thereby achieving a balance between environmental benefits and economic operational efficiency. However, industrial flue gas treatment is characterized by complex operating conditions, strong nonlinearity, and multivariable coupling, which complicates precise and cost-effective control. To address these challenges, this study proposes an optimized control strategy for denitrification systems based on a hybrid modeling approach and an enhanced genetic algorithm. We developed a Multidimensional Spatiotemporal Convolutional Attention Network (MSCA-Net) to tackle the nonlinear, multivariable, and high-dimensional data characteristics of the system, enabling accurate prediction of NOx emissions. Furthermore, we implemented a Constraint-aware Genetic Algorithm with Smoothing (CSGA) for global optimization, integrating an Extreme Gradient Boosting (XGBoost) model to generate initial ammonia flow settings, achieving a coefficient of determination (R2) of 0.951. Following optimization, the NOx compliance rate increased to 94.23%, average ammonia consumption decreased by 33.56 L/h, and the rate of threshold exceedance fell by 8.39%. These results demonstrate that the proposed approach effectively resolves multi-objective optimization challenges, achieving coordinated optimization of NOx emissions and ammonia flow. The method enhances system stability while simultaneously improving environmental and economic performance.