Izvorni znanstveni članak
https://doi.org/10.13044/j.sdewes.d14.0666
Integrating Machine Learning into Desalination Supply Chains: A Pathway to Sustainable Water Management
Mohamad Mohsen
; Eastern Michigan University, Ann Arbor, United States
Baha Mohsen
; Emirates Aviation University, Dubai, United Arab Emirates
Sažetak
Desalination is now being used more frequently to effectively address global water shortages and provide much-needed freshwater to arid regions and communities in need. Although there have been many improvements in technology at desalination plants, all the other stages involved in operating a desalination system are still affected by inefficiency, increased energy consumption, rising costs, and negative environmental impacts. Overcoming these problems requires improvement across the entire supply chain, rather than just at the plant level. This study assesses the effects of Machine Learning on enhancing the efficiency, robustness, and sustainability of desalination supply chains. For demand forecasting, supervised learning is utilised to detect deviations and optimise supply chain frameworks, which incorporate reinforcement learning, actual data, and trial situations. The integrated Machine Learning has reduced downtime by 18%, improved product distribution by 12%, lowered operating expenses by 14.2%, and nearly halved the company’s carbon emissions compared to standard operations. The results confirm that Machine Learning encourages more than minor changes and has a significant impact on the water management process. Using Artificial Intelligence in desalination helps experts and planners meet the issues of increasing water use and sustainability worldwide. It introduces a fresh, multi-technique Machine Learning model that enhances water supply management and provides a pathway toward greener, more robust desalination methods, supporting the goal of sustainable water security.
Ključne riječi
Machine Learning; Desalination; Supply Chain; Water Conservation; Sustainability; Optimization
Hrčak ID:
348132
URI
Datum izdavanja:
23.7.2026.
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