An End-to-End Framework for AI Integration in Desalination Systems

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Abstract

In the context of population growth, urbanization, and industrial expansion, water scarcity emerges as a significant concern, with projections indicating that around two billion individuals may face this challenge by 2050. Hence, the increased pressure on existing water resources calls for new water supply solutions in light of the growing demand. Desalination emerges as a promising alternative solution, particularly in regions confronting limited water resources. The sector has experienced remarkable growth, witnessing a 41% capacity increase over the past decade, with projections hinting at a twofold expansion by 2030. Such expansion requires integrating cutting-edge modeling techniques to ensure efficacy and cost-effectiveness. Artificial intelligence (AI) shows potential to revolutionize desalination and water treatment practices, yet its implementation remains limited. Delayed integration is believed to stem from the lack of trust among domain experts, knowledge gaps between water professionals and data scientists, and untapped potential within the field. This paper proposes The Integrated System Perspective for AI-based Desalination (ISP); an End-to-End Framework for AI in desalination. ISP-AID facilitates identifying AI applications across various project stages, from design to maintenance, uncovering opportunities for cost reduction and efficiency improvement. It adopts a structured data science perspective, integrating the Cross-Industry Standard Process for Data Mining (CRISP-DM) to guide AI algorithm selection and deployment. Spanning project cycle, process design, and data science levels, the framework aims to instill trust, foster collaborative problem understanding, and highlight untapped potential. This positions domain experts to actively develop data-driven solutions and enhancing confidence in innovative methodologies. By facilitating collaboration and exploring AI applications, the framework could expedite adopting efficient desalination solutions, thereby addressing global water scarcity challenges.
Original languageEnglish
Title of host publicationIPMU2024 Lisboa - Short Paper Proceedings
EditorsMarie-Jeanne Lesot, Susana Vieira, Marek Reformat, Fernando Batista, João Paulo Carvalho, Bernadette Bouchon-Menier, Ronald R. Yager
PublisherZenodo
Pages39-43
Number of pages5
ISBN (Electronic)978-989-33-7470-2
DOIs
Publication statusPublished - 4 Apr 2025
Event20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU2024 - Lisbon, Portugal
Duration: 22 Jul 202426 Jul 2024
Conference number: 20
https://link.springer.com/conference/ipmu

Conference

Conference20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU2024
Abbreviated titleIPMU2024
Country/TerritoryPortugal
CityLisbon
Period22/07/2426/07/24
Internet address

Keywords

  • Desalination
  • Reverse osmosis
  • Machine learning
  • artificial intelligence (AI)

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