Data-driven prediction of pollutants emission from small-scale heating units using temporal deep learning

Artificial intelligence (AI), particularly its subfield of machine learning (ML), has gained increasing attention in the field of environmental modelling and energy systems. These data-driven techniques offer robust tools for handling high-dimensional, nonlinear, and noisy datasets that are common in combustion diagnostics and emission prediction. This study investigates the use of advanced machine learning models for predicting flue gas emissions from residential heating systems under real-world operating conditions. Three types of solid-fuel boilers – automatic pellet, down-draught lignite, and gasification with hard coal – were analyzed using time-series data collected during full combustion cycles. Emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and organic gaseous compounds (OGC) were modelled using two deep learning approaches: a neural network with long short-term memory (NN-LSTM) and a hybrid convolutional LSTM (CNN-LSTM). In addition, Random Forest analysis was applied to identify the most influential operational parameters driving emission formation.

The results show that CO₂ emissions are predicted most reliably, especially in the gasification boiler using NN-LSTM (R² = 0.72). CNN-LSTM outperforms NN-LSTM in predicting CO and OGC in boilers with high variability, such as the down-draught system. However, both models face limitations when modelling NO_x and SO₂, suggesting the need for additional variables or physics-informed modelling. Feature importance analysis confirms oxygen concentration, flue gas temperature, and boiler heat output as key emission predictors.

The findings demonstrate the feasibility of applying AI-based models for real-time emission estimation and optimization of small-scale combustion systems. This study provides a realistic baseline for future integration of predictive emission models with adaptive boiler control systems in residential energy applications.