4.7 Article

Hydrogen production facility location selection for Black Sea using entropy based TOPSIS under IVPF environment

Journal

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 45, Issue 32, Pages 15855-15868

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2019.12.183

Keywords

Black sea; H2S; Uncertainty; TOPSIS; Interval valued pythagorean fuzzy sets; Facility location

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Parallel with the globally increment in energy demand, governments and responsible authorities' environmental awareness increases and they have been taking several preventions not to harm environment while energy demand is satisfied. For this purpose decision makers turn their faces to satisfy energy demand via renewable energy sources such as wind, wave, solar, hydro, biomass, hydrogen etc. Before this awareness human was satisfying energy demand via nonrenewable energy sources such as natural gas, coal etc. Even though not being one of these energy sources, hydrogen energy is a primary energy carrying form which is known one of the energy carrier [1]. This study aims to select the most appropriate site in the northern of Turkey for establishing a hydrogen energy hydrogen-sulphide (H2S) decomposition plant. Based on the researches, Black Sea is determined as one of the richest water to get H2S from. Since selecting the facility location for hydrogen energy production plant requires strategical decisions, Multi Criteria Decision Making (MCDM), as a powerful and efficient tool, is preferred in this study. Accordingly, Entropy and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies are integrated and applied under Interval Valued Pythagorean Fuzzy (IVPF) environment to deal with uncertain information better. The developed method is shown to be useful and effective in terms of applicability and ease of usage. As a result, the best suitable location is determined and sensitivity analysis is conducted to analyze the dynamics of the developed methodology. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

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