Hydrogen Storage on Porous Carbon Adsorbents: Rediscovery by Nature-Derived Algorithms in Random Forest Machine Learning Model

Hung Vo Thanh, Sajad Ebrahimnia Taremsari, Benyamin Ranjbar, Hossein Mashhadimoslem, Ehsan Rahimi, Mohammad Rahimi and Ali Elkamel ()
Additional contact information
Hung Vo Thanh: Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 700000, Vietnam
Sajad Ebrahimnia Taremsari: Department of Mechanical Engineering, Payame Noor University (PNU), Tehran 19395-4697, Iran
Benyamin Ranjbar: Energy Department, Politecnico di Torino, 10129 Torino, Italy
Hossein Mashhadimoslem: Faculty of Chemical Engineering, Iran University of Science & Technology (IUST), Tehran 16846, Iran
Ehsan Rahimi: Department of Materials Science and Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands
Mohammad Rahimi: Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Ali Elkamel: Department of Chemical Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Energies, 2023, vol. 16, issue 5, 1-19

Abstract: Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on the high surface area and microporous features of porous carbon-based adsorbents. The present research proposed to predict H 2 storage using four nature-inspired algorithms applied in the random forest (RF) model. Various carbon-based adsorbents, chemical activating agents, ratios, micro-structural features, and operational parameters as input variables are applied in the ML model to predict H 2 uptake (wt%). Particle swarm and gray wolf optimizations (PSO and GWO) in the RF model display accuracy in the train and test phases, with an R 2 of ~0.98 and 0.91, respectively. Sensitivity analysis demonstrated the ranks for temperature, total pore volume, specific surface area, and micropore volume in first to fourth, with relevancy scores of 1 and 0.48. The feasibility of algorithms in training sizes 80 to 60% evaluated that RMSE and MAE achieved 0.6 to 1, and 0.38 to 0.52. This study contributes to the development of sustainable energy sources by providing a predictive model and insights into the design of porous carbon adsorbents for hydrogen storage. The use of nature-inspired algorithms in the model development process is also a novel approach that could be applied to other areas of materials science and engineering.

Keywords: hydrogen storage; machine learning; random forest; nature-based algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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