AI-Driven Waste Management in Innovating Space Exploration

David Bamidele Olawade (), Ojima Zechariah Wada, Tunbosun Theophilus Popoola, Eghosasere Egbon, James O. Ijiwade and B. I. Oladapo
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David Bamidele Olawade: Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London E16 2RD, UK
Ojima Zechariah Wada: Division of Sustainable Development, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Education City, Doha 34110, Qatar
Tunbosun Theophilus Popoola: Department of Chemistry, Faculty of Science, University of Ibadan, Ibadan 200005, Nigeria
Eghosasere Egbon: Department of Tissue Engineering and Regenerative Medicine, Faculty of Life Science Engineering, FH Technikum, 1200 Vienna, Austria
James O. Ijiwade: Department of Chemistry, Faculty of Science, University of Ibadan, Ibadan 200005, Nigeria
B. I. Oladapo: Sustainable Development, De Montfort University, Leicester LE1 9BH, UK

Sustainability, 2025, vol. 17, issue 9, 1-19

Abstract: This research evaluates advanced waste management technologies suitable for long-duration space missions, particularly focusing on artificial intelligence (AI)-driven sorting systems, biotechnological bioreactors, and thermal processing methods, such as plasma gasification. It quantitatively assesses the waste generated per crew member. It analyses energy efficiency, integration capabilities with existing life-support systems, and practical implementation constraints based on experimental ground and ISS data. Challenges are addressed, including energy demands, microbial risks, and integration complexities. The research also discusses methodological approaches, explicitly outlining selection criteria and comparative frameworks used. Key findings indicate that plasma arc technologies significantly reduce waste volume, although high energy consumption remains challenging. Enhanced recycling efficiencies of water and oxygen are also discussed. Future research directions and actionable policy recommendations are outlined to foster sustainable and autonomous waste management solutions for space exploration.

Keywords: space waste management; sustainable technology; artificial intelligence; bioreactors; plasma gasification; resource recovery (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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