Deep learning performance prediction for solar-thermal-driven hydrogen production membrane reactor via bayesian optimized LSTM

Abstract

The random fluctuation of solar energy poses significant challenges for direct solar utilization systems. Solar-driven membrane reactors (SMMR) are efficient devices for energy conversion and pure hydrogen production, featuring thermal inertia and complex physicochemical processes. To realize advance control of SMMR under solar variations to promote their practical application, accurate performance prediction is essential. This study develops a deep learning-assisted performance prediction model for SMMR using Bayesian optimized long short-term memory (BO-LSTM) network and weather classification. Time-series datasets with similar environmental features are generated through SMMR multi-physics modeling and trained by BO-LSTM. The BO-LSTM demonstrates high accuracy in both short-time (1-day) and long-time (40-day) predictions, with a correlation coefficient exceeding 0.997 and a deviation of ±0.0016 in long-term predictions. Weather classification ensures consistent prediction accuracy across different weather. Compared to RF, SVM, BPNN, and CNN models, BO-LSTM provides smoother prediction curves and significantly improves accuracy and stability, reducing RMSE by 44.6% on sunny days and 53.3% on cloudy days in short-time predictions, and by 56.7% and 68.0% in long-time predictions, respectively. The BO-LSTM proposed in this study accurately predicts SMMR performance under various weather, guiding practical applications and offering a reference for prediction and control in solar thermal utilization systems.