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Green hydrogen for industrial sector decarbonization: Costs and impacts on hydrogen economy in qatar

handle: 10576/38710
Abstract This study focuses on the development of a strategic framework for the design of a hydrogen supply chain network (HSCN) mainly investigating the potential of industrial decarbonization and multi-sectors integration (i.e., transportation, energy, shipping) via green hydrogen economy. The problem was formulated as a mixed integer linear programming (MILP) and solved in GAMS/ IBM ILOG CPLEX 30.3.0 solver. The applicability of the developed model was demonstrated using a base case Eco-industrial city consisting of 10 diverse industrial portfolios targeting decarbonization by 5%. The solution was able to find the optimal HSCN from the probable superstructure along with the optimal sizing of green hydrogen production (453.03 MM kg/y), optimal water sources, optimal sinks and the optimal amount of byproducts generation. Furthermore, the multi-purpose model can accomplish detailed techno-economic-environmental analysis for variable scenarios (e.g., variation in decarbonization target, liquid hydrogen demand, hydrogen production cost, earnings from byproducts) based on net present value.
- Qatar University Qatar
- Qatar University Qatar
- The University of Texas System United States
Green hydrogen, 660, Supply chain optimization, Decarbonization, Techno-economic-environmental analysis
Green hydrogen, 660, Supply chain optimization, Decarbonization, Techno-economic-environmental analysis
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).123 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.1%
