Abstract

Desalination is now being used more often to properly liquidate global water shortages and deliver needed freshwater to dry regions and thirsty communities. Though there have been many improvements in the technology at desalination plants, all the other stages involved in running a desalination system are still affected by inefficiency, increased energy use, growing costs and negative effects on the environment. Overcoming these problems calls for improvement across the entire supply chain, instead of just at the plant level. This study assesses the effects of machine learning on making desalination supply chains more efficient, strong and sustainable. For demand forecasting, supervised learning is used for demand forecasting, for detecting deviations, as well as optimization in the supply chain, framework is proposed and reinforcement learning, along with actual data and trial situations. The integrated Machine Learning has cut downtime by 18%, improved how products are distributed by 12%, lowered operating expenses by 14.2% and almost reduced the company’s carbon emissions by 10% over standard operations. The results confirm that Machine Learning encourages more than small changes and has a big 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 adds a fresh, multi-technique ML model that helps water supply management and gives a pathway toward greener, more robust desalination methods that can support the goal of sustainable water security.