Reliable AI models can reveal key processes of heat recovery steam generator operation in air pollutant emission

Abstract

Artificial intelligence (AI) algorithms have been applied to air pollution prediction modeling, but assessment of their trustworthiness is lacking. This study aims to examine whether AI-based models can identify key operational processes of a Heat Recovery Steam Generator (HRSG) facility and predict the emission of target air pollutants, including nitrogen oxides (NOx), sulfur oxides (SOx), and total suspended particles (TSP). First, key operational variables are selected based on the feature importance of the Random Forest models (that is, data-driven input selection). Secondly, Bidirectional Long Short-Term Memory-based AutoEncoder (BiLSTM-AE) and Random Forest (RF) models with the data-driven input selection are trained and evaluated using multiple predictive performance metrics. Then, the BiLSTM-AE and RF models are trained and evaluated using the operational variables selected by experts. Results demonstrate that the data-driven and expertise-based methods select five common operational variables that are key factors to predict variations of target output variables. The results from the multi-metric evaluations show a skillful prediction of BiLSTM-AE and RF models for NOx and TSP, but not SOx. For TSP prediction, RF is more sensitive to the input selection method than BiLSTM-AE. This study underscores the dependency of the prediction skill of AI algorithm-based models on the target air pollutant and input selection method, and suggests the trustworthiness of selected key operational variables by RF's feature importance.