AI-driven real-time hydrogen forecasting via thermographic imaging: A case study on a downdraft biomass gasifier

Hydrogen is considered a key energy vector in the European transition toward a low-carbon economy due to its high energy density and the absence of CO 2 emissions during its use. It can be produced through a wide variety of thermochemical and electrochemical processes, many of which require accurate monitoring of hydrogen concentration in real time to optimize efficiency and safety. Traditional gas analysis systems, however, are often expensive (typically $30,000–40,000) and invasive, limiting their deployment in decentralized or small-scale applications. This study presents a general-purpose, non-invasive, and cost-effective methodology for hydrogen concentration forecasting based on thermographic imaging and machine learning, with an estimated cost of only $1500–2000. The approach is applicable to any thermal process where reactor surface temperature correlates with internal gas composition. As a case study, the methodology was implemented in a 25 kW t downdraft biomass gasifier operating with olive pomace pellets. Over one year of operation, thermal images and hydrogen concentrations were recorded every 5 s, yielding temperature profiles of 281 points. A total of 25 machine learning models were trained and evaluated. The best-performing model, a random forest regressor with 500 trees and a maximum depth of 10, achieved a mean absolute error (MAE) of 4 . 22 × 1 0 − 3 and an R 2 of 0.9995. Model analysis shows that the combustion zone, the reactor’s middle section, is the most critical for predicting hydrogen concentration. Higher temperatures accelerate water-carbon reactions and H 2 release, making these hot zones the main areas of hydrogen generation, consistent with the thermodynamic behavior of the gasification process. These results validate the proposed method as a viable solution for real-time hydrogen monitoring in various industrial processes. • Real-time prediction of H 2 in a biomass gasifier was achieved using thermal image and AI. • 25 kW t downdraft gasifier was used to collect the whole dataset over one year of operation. • Twenty five models covering LR, SVR, RFR, and ANN were trained and evaluated. • RFR with 500 trees and maximum depth of 10 achieved MAE 4.22 × 10 − 3 and R 2 0.9995. • The model identifies the combustion zone as the most influential region for H 2 prediction.