Open Access

ARTICLE

An Evidence-Based CoCoSo Framework with Double Hierarchy Linguistic Data for Viable Selection of Hydrogen Storage Methods

by Raghunathan Krishankumar¹, Dhruva Sundararajan², K. S. Ravichandran², Edmundas Kazimieras Zavadskas^3,*

1 Information Technology Systems and Analytics Area, Indian Institute of Management Bodh Gaya, Bodh Gaya, Bihar, 824234, India
2 Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India
3 Institute of Sustainable Construction, Vilnius Gediminas Technical University, Vilnius, 10223, Lithuania

* Corresponding Author: Edmundas Kazimieras Zavadskas. Email:

(This article belongs to the Special Issue: Linguistic Approaches for Multiple Criteria Decision Making and Applications)

Computer Modeling in Engineering & Sciences 2024, 138(3), 2845-2872. https://doi.org/10.32604/cmes.2023.029438

Received 18 February 2023; Accepted 17 July 2023; Issue published 15 December 2023

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.

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