Operational Hybrid Neural Network Model for NO$_{x}$ Forecast and Control in Real-World 2-GW Coal-Fired Power Plant

This study presents the development and implementation of an advanced hybrid neural network (HNN) model for predicting nitrogen oxide (NO
$_{x}$
) emissions and controlling ammonia (NH
$_{3}$
) injection in a 1-GW generator within a 2-GW operational coal-fired power plant. The HNN model, which integrates both endogenous and exogenous input features to effectively analyze complex relationships, shows significant improvement in accuracy with a forecast skill of 22% compared to multiple benchmark models. The real-world application of the HNN-based control strategy resulted in a slight increase in average outlet NO
$_{x}$
concentration but remained well within the regulated limit of 50 ppm, while reducing the standard deviation from 9.7 to 4.9 ppm, indicating a more stable and controlled outlet NO
$_{x}$
concentration. The successful deployment of the HNN model in an operational power plant demonstrates its practical applicability and effectiveness in large-scale industrial settings, ultimately supporting the transition toward a sustainable energy future.