Multimodal Logistics Optimization Powered by AI for Green Hydrogen Export Corridors: An Internet of Energy Perspective on Morocco Europe Trade Routes
Raoua NACEIRI MRABTI, Hind EL HASSANI, Noureddine BOUTAMMACHTE
Abstract. Green hydrogen exports from Morocco to Europe need efficient transport systems. This paper tests an AI based method to optimize routes across road, pipeline, and sea transport. We used machine learning on 2 160 hours of simulated data from three corridors: Nador Algeciras, Tanger Barcelona, and Casablanca Rotterdam. Our routing algorithm achieved 100% accuracy. Cost predictions showed R² = 0.985 with mean absolute error of 0.11 €/tonne. Pipeline transport from Nador to Algeciras costs 22.4 €/tonne, 2.8 times cheaper than maritime shipping to Rotterdam 62.8 €/tonne. Pipeline emissions are also lowest at 0.09 kg 〖CO〗_2e/kg H_2 compared to 0.25 kg for long maritime routes. We conclude that trans-Mediterranean pipelines offer the most cost effective and low emission option for large scale hydrogen trade between Morocco and Europe.
Keywords
Green Hydrogen, AI Optimization, Multimodal Logistics, Morocco Europe Corridor, Internet of Energy
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Raoua NACEIRI MRABTI, Hind EL HASSANI, Noureddine BOUTAMMACHTE, Multimodal Logistics Optimization Powered by AI for Green Hydrogen Export Corridors: An Internet of Energy Perspective on Morocco Europe Trade Routes, Materials Research Proceedings, Vol. 64, pp 754-761, 2026
DOI: https://doi.org/10.21741/9781644904091-94
The article was published as article 94 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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