1. Deep learning performance prediction for solar-thermal-driven hydrogen production membrane reactor via bayesian optimized LSTM.
- Author
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Tang, Xin-Yuan, Yang, Wei-Wei, Liu, Zhao, Li, Jia-Chen, and Ma, Xu
- Subjects
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MEMBRANE reactors , *SOLAR oscillations , *WEATHER forecasting , *HYDROGEN production , *DEEP learning , *SELF-tuning controllers - 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. [Display omitted] • Self-tuning BO-LSTM is used to predict performances of solar-driven membrane reactor. • Weather classification achieves equally excellent prediction for sunny and cloudy. • Errors of long-term data training is smaller than the short-term and R2 is 0.997. • Mean prediction errors of BO-LSTM is reduced by 45%–75% compared to other models. • Predictive accuracy and stability of BO-LSTM for easy integration in control systems. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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