Abstract

In this study, an explainable and interpretable deep learning (DL) model based on convolutional neural network (CNN) was suggested to accurately estimate H2 solubility in various chemicals under vast ranges of pressure and temperature. The model was implemented using more than 3700 authenticated datapoints. The results revealed that the CNN model achieved excellent predictions and surpassed the well-known machine learning (ML) and prior predictive paradigms. In this context, the CNN demonstrated attractive statistical metrics (RMSE = 0.0049 and R2 = 0.9934). The explainability and interpretability of the suggested DL-based model were testified using the Shapley Additive exPlanations (SHAP) method. Additionally, trend analyses were conducted on the model’s predictions to verify that it accurately reflects H2 solubility trends in various chemicals at different pressure and temperature levels. Lastly, the capability of the introduced DL model greatly improves the simulation of processes involving this crucial parameter in both industrial and academic sectors.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.