@Article{cmes.2023.029438,
AUTHOR = {Raghunathan Krishankumar, Dhruva Sundararajan, K. S. Ravichandran, Edmundas Kazimieras Zavadskas},
TITLE = {An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {138},
YEAR = {2024},
NUMBER = {3},
PAGES = {2845--2872},
URL = {http://www.techscience.com/CMES/v138n3/54922},
ISSN = {1526-1506},
ABSTRACT = {Hydrogen is the new age alternative energy source to combat energy demand and climate change. Storage of hydrogen is vital for a nation’s growth. Works of literature provide different methods for storing the produced hydrogen, and the rational selection of a viable method is crucial for promoting sustainability and green practices. Typically, hydrogen storage is associated with diverse sustainable and circular economy (SCE) criteria. As a result, the authors consider the situation a multi-criteria decision-making (MCDM) problem. Studies infer that previous models for hydrogen storage method (HSM) selection (i) do not consider preferences in the natural language form; (ii) weights of experts are not methodically determined; (iii) hesitation of experts during criteria weight assessment is not effectively explored; and (iv) three-stage solution of a suitable selection of HSM is unexplored. Driven by these gaps, in this paper, authors put forward a new integrated framework, which considers double hierarchy linguistic information for rating, criteria importance through inter-criteria correlation (CRITIC) for expert weight calculation, evidence-based Bayesian method for criteria weight estimation, and combined compromise solution (CoCoSo) for ranking HSMs. The applicability of the developed framework is testified by using a case example of HSM selection in India. Sensitivity and comparative analysis reveal the merits and limitations of the developed framework.},
DOI = {10.32604/cmes.2023.029438}
}