Bayesian surrogate assisted neural network model to predict the hydrogen storage in 9-ethylcarbazole

Optimization of the reaction conditions for hydrogen storage in 9-ethylcarbazole, an efficient liquid organic hydrogen carrier, is essential for advancing hydrogen energy applications. In this study, a deep neural network (DNN) model was developed to analyze the effects of key parameters such as temperature, initial pressure, catalyst type and dosage, and stirring speed on hydrogen storage capacity. Correlational analysis using Pearson, Spearman, and Kendal correlations identified time (r = 0.83) as the most influential factor impacting the hydrogen storage positively, whereas catalyst dosage (r =- 0.75) exhibited a strong negative correlation. Temperature (r = 0.15) exhibited a weak relationship, suggesting nonlinear dependencies. To enhance the predictive accuracy and develop an optimal DNN model, Bayesian Surrogate Random Forest (BSRF), Bayesian Surrogate Gaussian Process (BSGP), and Bayesian Surrogate Gradient Boost Regression Trees (BSGBRT) were integrated with deep neural networks (DNNs). Among these, the BSGP-DNN model exhibited the highest predictive performance, achieving an R² value of 0.89. The reliability of the model is further supported by a low mean absolute error (0.0101), mean square error (0.0001), and relative deviation (3.2 %). The error density curve centered around zero emphasized the model's accuracy and uniform error distribution.