An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods Article Swipe
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· 2023
· Open Access
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· DOI: https://doi.org/10.32604/cmes.2023.029438
· OA: W4389197494
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.
