AI-driven machine learning and experimental optimization for sustainable solar distillers using recycled stainless-steel shavings-enhanced PCM and ag-NPs-coated recycled cans

Key Points

• Findings reveal significant improvements in efficiency for solar distillers utilizing enhanced phase change materials.
• Efficiency increased by over 30% when using machine learning techniques to optimize performance parameters.
• Assessment using experimental optimization established a direct link between material enhancement and energy output.
• Highlights the potential for sustainable practices in solar technology through the use of recycled resources.