Robust NOx Emission Prediction Framework for Combined Cycle Power Plant via Parallel GATv2-LSTM

The real-time prediction of nitrogen oxide (NOx) emissions is essential in terms of effective denitrification control for combined cycle power plant (CCPP). However, prediction accuracy and temporal transferability are often challenged by various operational conditions of CCPP including dynamic gas flow in large-scale structure and periodic overhauls. In this regard, this paper presents a robust and precise prediction method applicable to the NOx emission in the CCPP. To overcome these challenges, this study develops a graph attention mechanism that utilizes the complete set of operational monitoring signals, guided by an attention-based signal selection process with learnable input importance. Furthermore, a prediction algorithm is formulated by integrating a parallel GATv2-LSTM framework, which effectively captures operational relationships and temporal dynamics across different time steps. Numerical tests utilizing real plant operation data show that the proposed method outperforms recent deep learning models in terms of prediction accuracy. Moreover, it demonstrates robustness in terms of temporal transferability problems as well as noise. This study offers practical insights for optimizing denitrification systems in CCPP.