Explainable AI-based deep learning architectures for hydrogen prediction in biomass syngas and photocatalysis processes: Towards a sustainable and efficient energy economy

Abstract

This study, focusing on sustainable energy production and particularly the hydrogen (H 2 ) economy, presents an advanced artificial intelligence (AI) framework for accurately predicting H 2 production from sucrose photocatalysis catalyzed by LaFeO 3 (GLFO) and the H 2 concentration in syngas derived from biomass conversion. The research was conducted along two main directions: (i) deep learning (DL) architectures comprising nine specially designed recurrent neural network-based models and (ii) six conventional machine learning (ML) algorithms. For both H 2 production systems, the best-performing models were identified and further interpreted using explainable AI (XAI) based on SHapley Additive exPlanations (SHAP). Among all investigated algorithms, a hybrid One-dimensional Convolutional Neural Network–Bidirectional Long Short-Term Memory–Bidirectional Gated Recurrent Unit (1D-CNN–BiLSTM–BiGRU) model demonstrated the highest predictive performance. Using 10-fold cross-validation, the proposed model achieved outstanding accuracy, with an R 2 value of 0.9987 ± 0.0011 for photocatalytic H 2 production prediction and 0.9980 ± 0.0006 for syngas H 2 concentration prediction. These results highlight the potential of the proposed framework as a precise, reliable, and practical methodological approach for modeling and optimizing complex thermochemical energy systems.